Initial profilometry dataset release

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/pherc0248/ε.txt filter=lfs diff=lfs merge=lfs -text
+data/pherc0248/ε_stack.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0248/ε_z.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0248/λ.txt filter=lfs diff=lfs merge=lfs -text
+data/pherc0248/λ_stack.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0248/λ_z.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0248/ν.txt filter=lfs diff=lfs merge=lfs -text
+data/pherc0248/ν_stack.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0248/ν_z.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0248/π.txt filter=lfs diff=lfs merge=lfs -text
+data/pherc0248/π_stack.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0248/π_z.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0248/φ.txt filter=lfs diff=lfs merge=lfs -text
+data/pherc0248/φ_stack.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0248/φ_z.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0250/δ.txt filter=lfs diff=lfs merge=lfs -text
+data/pherc0250/δ_stack.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0250/δ_z.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0250/κ.txt filter=lfs diff=lfs merge=lfs -text
+data/pherc0250/κ_stack.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0250/κ_z.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0250/ο.txt filter=lfs diff=lfs merge=lfs -text
+data/pherc0250/ο_stack.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0250/ο_z.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0250/ρ.txt filter=lfs diff=lfs merge=lfs -text
+data/pherc0250/ρ_stack.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0250/ρ_z.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0250/ϲ.txt filter=lfs diff=lfs merge=lfs -text
+data/pherc0250/ϲ_stack.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0250/ϲ_z.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0500p2/η.txt filter=lfs diff=lfs merge=lfs -text
+data/pherc0500p2/η_stack.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0500p2/η_z.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0500p2/ιτ.txt filter=lfs diff=lfs merge=lfs -text
+data/pherc0500p2/ιτ_stack.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0500p2/ιτ_z.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0500p2/ν.txt filter=lfs diff=lfs merge=lfs -text
+data/pherc0500p2/ν2.txt filter=lfs diff=lfs merge=lfs -text
+data/pherc0500p2/ν2_stack.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0500p2/ν2_z.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0500p2/ν_stack.tif filter=lfs diff=lfs merge=lfs -text
+data/pherc0500p2/ν_z.tif filter=lfs diff=lfs merge=lfs -text

LICENSE

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+Optical profilometry of opened Herculaneum papyri
+
+Copyright (c) 2026 Vesuvius Challenge.
+
+Except where otherwise indicated, the data, annotations, fold splits,
+configuration files, documentation, and other non-code research files in
+this release are licensed under the Creative Commons
+Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
+
+You may share and adapt the licensed material for noncommercial purposes,
+provided that you give appropriate credit, link to the license, and indicate
+whether changes were made. No warranties are given.
+
+License deed and legal code:
+https://creativecommons.org/licenses/by-nc/4.0/
+
+Suggested attribution:
+
+  Vesuvius Challenge (2026). Optical profilometry of opened Herculaneum papyri.
+  https://doi.org/10.1038/s41598-026-58467-1. Licensed under CC BY-NC 4.0.
+
+nnU-Net is not included in this release and is distributed separately under
+the Apache License 2.0.
+
+Third-party material, if expressly identified, is excluded from this license
+and remains subject to its own license or permission terms.

README.md

@@ -1,3 +1,40 @@
 ---
+pretty_name: "Optical profilometry of opened Herculaneum papyri"
 license: cc-by-nc-4.0
+task_categories: [image-segmentation]
+size_categories: [n<1K]
+tags: [image, 3d, archaeology]
 ---
+
+# Optical profilometry of opened Herculaneum papyri
+
+Dataset accompanying [“Ink detection from surface topography of the Herculaneum papyri”](https://doi.org/10.1038/s41598-026-58467-1).
+
+The release contains 14 co-registered profilometry regions encompassing 16 letters from PHerc. 248, PHerc. 250, and PHerc. 500P2.
+
+- Native lateral sampling: 0.68793625 µm/pixel in X and Y
+- Vertical sensitivity: 8 nm
+
+## Data
+
+Each sample under `data/` has four files with identical dimensions and a top-left origin:
+
+- `<sample>.txt`: semicolon-delimited X, Y, and calibrated Z values in µm; missing reconstructions are `nan`
+- `<sample>_z.tif`: per-sample normalized uint16 height map
+- `<sample>_stack.tif`: co-registered RGB brightfield image
+- `<sample>_label.png`: grayscale ink annotation; experiment preprocessing treats nonzero values as foreground
+
+Use the text files when calibrated physical heights are required. Exact height-map preprocessing is specified in `configs/experiment.json`.
+
+## Metadata and configurations
+
+- `manifest.csv`: dimensions, physical extents, sampling, acquisition dates, and file paths
+- `configs/case_manifest.json`: stable machine-learning case IDs and sample mapping
+- `configs/sampling.json`: native, vertical, and factor-derived sampling values
+- `configs/experiment.json`: preprocessing, experiments, training settings, and software versions
+- `configs/nnunet/`: dataset descriptor and nine resolution-specific nnU-Net plans
+- `folds/splits_final.json`: five cross-validation and three leave-one-papyrus-out splits
+
+## License
+
+Data, annotations, folds, and configurations are CC BY-NC 4.0. See `LICENSE`. Please cite the associated paper above.

configs/case_manifest.json

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+{
+  "schema_version": "1.0",
+  "dataset_version": "1.0.0",
+  "samples": [
+    {
+      "case_id": "248_090925_E",
+      "papyrus_id": "PHerc. 248",
+      "papyrus_dir": "pherc0248",
+      "sample_id": "ε",
+      "acquisition_date": "2025-09-09",
+      "raw_height_txt": "data/pherc0248/ε.txt",
+      "normalized_height_tif": "data/pherc0248/ε_z.tif",
+      "brightfield_tif": "data/pherc0248/ε_stack.tif",
+      "label_png": "data/pherc0248/ε_label.png"
+    },
+    {
+      "case_id": "248_090925_F",
+      "papyrus_id": "PHerc. 248",
+      "papyrus_dir": "pherc0248",
+      "sample_id": "φ",
+      "acquisition_date": "2025-09-09",
+      "raw_height_txt": "data/pherc0248/φ.txt",
+      "normalized_height_tif": "data/pherc0248/φ_z.tif",
+      "brightfield_tif": "data/pherc0248/φ_stack.tif",
+      "label_png": "data/pherc0248/φ_label.png"
+    },
+    {
+      "case_id": "248_090925_L",
+      "papyrus_id": "PHerc. 248",
+      "papyrus_dir": "pherc0248",
+      "sample_id": "λ",
+      "acquisition_date": "2025-09-09",
+      "raw_height_txt": "data/pherc0248/λ.txt",
+      "normalized_height_tif": "data/pherc0248/λ_z.tif",
+      "brightfield_tif": "data/pherc0248/λ_stack.tif",
+      "label_png": "data/pherc0248/λ_label.png"
+    },
+    {
+      "case_id": "248_090925_N",
+      "papyrus_id": "PHerc. 248",
+      "papyrus_dir": "pherc0248",
+      "sample_id": "ν",
+      "acquisition_date": "2025-09-09",
+      "raw_height_txt": "data/pherc0248/ν.txt",
+      "normalized_height_tif": "data/pherc0248/ν_z.tif",
+      "brightfield_tif": "data/pherc0248/ν_stack.tif",
+      "label_png": "data/pherc0248/ν_label.png"
+    },
+    {
+      "case_id": "248_090925_PI",
+      "papyrus_id": "PHerc. 248",
+      "papyrus_dir": "pherc0248",
+      "sample_id": "π",
+      "acquisition_date": "2025-09-09",
+      "raw_height_txt": "data/pherc0248/π.txt",
+      "normalized_height_tif": "data/pherc0248/π_z.tif",
+      "brightfield_tif": "data/pherc0248/π_stack.tif",
+      "label_png": "data/pherc0248/π_label.png"
+    },
+    {
+      "case_id": "250_090925_C",
+      "papyrus_id": "PHerc. 250",
+      "papyrus_dir": "pherc0250",
+      "sample_id": "ϲ",
+      "acquisition_date": "2025-09-09",
+      "raw_height_txt": "data/pherc0250/ϲ.txt",
+      "normalized_height_tif": "data/pherc0250/ϲ_z.tif",
+      "brightfield_tif": "data/pherc0250/ϲ_stack.tif",
+      "label_png": "data/pherc0250/ϲ_label.png"
+    },
+    {
+      "case_id": "250_090925_D",
+      "papyrus_id": "PHerc. 250",
+      "papyrus_dir": "pherc0250",
+      "sample_id": "δ",
+      "acquisition_date": "2025-09-09",
+      "raw_height_txt": "data/pherc0250/δ.txt",
+      "normalized_height_tif": "data/pherc0250/δ_z.tif",
+      "brightfield_tif": "data/pherc0250/δ_stack.tif",
+      "label_png": "data/pherc0250/δ_label.png"
+    },
+    {
+      "case_id": "250_090925_K",
+      "papyrus_id": "PHerc. 250",
+      "papyrus_dir": "pherc0250",
+      "sample_id": "κ",
+      "acquisition_date": "2025-09-09",
+      "raw_height_txt": "data/pherc0250/κ.txt",
+      "normalized_height_tif": "data/pherc0250/κ_z.tif",
+      "brightfield_tif": "data/pherc0250/κ_stack.tif",
+      "label_png": "data/pherc0250/κ_label.png"
+    },
+    {
+      "case_id": "250_090925_O",
+      "papyrus_id": "PHerc. 250",
+      "papyrus_dir": "pherc0250",
+      "sample_id": "ο",
+      "acquisition_date": "2025-09-09",
+      "raw_height_txt": "data/pherc0250/ο.txt",
+      "normalized_height_tif": "data/pherc0250/ο_z.tif",
+      "brightfield_tif": "data/pherc0250/ο_stack.tif",
+      "label_png": "data/pherc0250/ο_label.png"
+    },
+    {
+      "case_id": "250_090925_P",
+      "papyrus_id": "PHerc. 250",
+      "papyrus_dir": "pherc0250",
+      "sample_id": "ρ",
+      "acquisition_date": "2025-09-09",
+      "raw_height_txt": "data/pherc0250/ρ.txt",
+      "normalized_height_tif": "data/pherc0250/ρ_z.tif",
+      "brightfield_tif": "data/pherc0250/ρ_stack.tif",
+      "label_png": "data/pherc0250/ρ_label.png"
+    },
+    {
+      "case_id": "500P2_230925_H",
+      "papyrus_id": "PHerc. 500P2",
+      "papyrus_dir": "pherc0500p2",
+      "sample_id": "η",
+      "acquisition_date": "2025-09-23",
+      "raw_height_txt": "data/pherc0500p2/η.txt",
+      "normalized_height_tif": "data/pherc0500p2/η_z.tif",
+      "brightfield_tif": "data/pherc0500p2/η_stack.tif",
+      "label_png": "data/pherc0500p2/η_label.png"
+    },
+    {
+      "case_id": "500P2_230925_IT",
+      "papyrus_id": "PHerc. 500P2",
+      "papyrus_dir": "pherc0500p2",
+      "sample_id": "ιτ",
+      "acquisition_date": "2025-09-23",
+      "raw_height_txt": "data/pherc0500p2/ιτ.txt",
+      "normalized_height_tif": "data/pherc0500p2/ιτ_z.tif",
+      "brightfield_tif": "data/pherc0500p2/ιτ_stack.tif",
+      "label_png": "data/pherc0500p2/ιτ_label.png"
+    },
+    {
+      "case_id": "500P2_230925_N",
+      "papyrus_id": "PHerc. 500P2",
+      "papyrus_dir": "pherc0500p2",
+      "sample_id": "ν",
+      "acquisition_date": "2025-09-23",
+      "raw_height_txt": "data/pherc0500p2/ν.txt",
+      "normalized_height_tif": "data/pherc0500p2/ν_z.tif",
+      "brightfield_tif": "data/pherc0500p2/ν_stack.tif",
+      "label_png": "data/pherc0500p2/ν_label.png"
+    },
+    {
+      "case_id": "500P2_230925_N2",
+      "papyrus_id": "PHerc. 500P2",
+      "papyrus_dir": "pherc0500p2",
+      "sample_id": "ν2",
+      "acquisition_date": "2025-09-23",
+      "raw_height_txt": "data/pherc0500p2/ν2.txt",
+      "normalized_height_tif": "data/pherc0500p2/ν2_z.tif",
+      "brightfield_tif": "data/pherc0500p2/ν2_stack.tif",
+      "label_png": "data/pherc0500p2/ν2_label.png"
+    }
+  ]
+}

configs/experiment.json

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+{
+  "schema_version": "1.0",
+  "dataset_version": "1.0.0",
+  "references": {
+    "case_manifest": "case_manifest.json",
+    "sampling": "sampling.json",
+    "folds": "../folds/splits_final.json",
+    "nnunet_dataset": "nnunet/dataset.json",
+    "nnunet_plans_directory": "nnunet/plans"
+  },
+  "preprocessing": {
+    "raw_height_import": {
+      "delimiter": "semicolon",
+      "decimal_separator": ".",
+      "coordinate_handling": "full_numeric_export_top_left_matched_to_label_shape",
+      "empty_rows_and_columns": "remove_fully_empty",
+      "trailing_nonfinite_rows_and_columns": "remove",
+      "shape_matching": {
+        "anchor": "top_left",
+        "excess_rows_and_columns": "crop_bottom_and_right",
+        "missing_rows_and_columns": "pad_bottom_and_right_with_nonfinite"
+      }
+    },
+    "nonfinite_inpainting": {
+      "method": "OpenCV Telea",
+      "radius_pixels": 3,
+      "temporary_image_dtype": "uint8",
+      "temporary_scaling": {
+        "method": "percentile_linear_scaling",
+        "lower_percentile": 0.5,
+        "upper_percentile": 99.5,
+        "output_range": [
+          0,
+          255
+        ],
+        "map_back_to_original_height_range": true
+      },
+      "restore_original_finite_measurements": true
+    },
+    "normalized_height_output": {
+      "method": "per_sample_normalization",
+      "normalized_range": [
+        0,
+        1
+      ],
+      "output_dtype": "uint16",
+      "output_range": [
+        0,
+        65535
+      ]
+    },
+    "labels": {
+      "foreground_rule": "nonzero_is_foreground",
+      "downsampling_interpolation": "nearest_neighbour"
+    },
+    "lateral_downsampling": {
+      "image_method": "nonoverlapping_block_mean",
+      "crop_to_factor_multiple": "bottom_and_right"
+    },
+    "isotropic_z_binning": {
+      "bin_width_um": "native_lateral_sampling_um_per_pixel multiplied by resolution_factor",
+      "quantization": "floor",
+      "formula": "floor(height_um / bin_width_um) multiplied by bin_width_um"
+    }
+  },
+  "experiments": {
+    "matched_resolution": {
+      "description": "Independent training and testing at each lateral resolution factor.",
+      "training_input": "continuous_height_at_training_factor",
+      "test_input": "continuous_height_at_training_factor",
+      "train_factors": [
+        1,
+        2,
+        3,
+        4,
+        6,
+        8,
+        10,
+        16,
+        32
+      ],
+      "test_factors": "same_as_training_factor",
+      "folds": [
+        0,
+        1,
+        2,
+        3,
+        4
+      ]
+    },
+    "fixed_native_model": {
+      "description": "The native-resolution model is evaluated on continuous-height inputs block-averaged at each test factor, bilinearly upsampled to the native grid, and then normalized per sample and converted to uint16.",
+      "evaluation_mode": "inference_only",
+      "train_factor": 1,
+      "model_training_input": "native_height_normalized_per_sample_to_uint16",
+      "test_source_representation": "inpainted_continuous_height_before_normalization",
+      "test_factors": [
+        1,
+        2,
+        3,
+        4,
+        6,
+        8,
+        10,
+        16,
+        32
+      ],
+      "downsampling": {
+        "method": "nonoverlapping_block_mean",
+        "factor_multiple_handling": "edge_pad_bottom_and_right"
+      },
+      "upsampling": {
+        "method": "bilinear_interpolation",
+        "target": "native_grid",
+        "final_crop": "bottom_and_right_to_original_shape"
+      },
+      "post_upsampling": {
+        "normalization": "per_sample_to_0_1",
+        "output_dtype": "uint16"
+      },
+      "folds": [
+        0,
+        1,
+        2,
+        3,
+        4
+      ]
+    },
+    "isotropic_z_binning": {
+      "description": "Height values are quantized at the physical bin width associated with each lateral resolution factor.",
+      "evaluation_mode": "inference_only",
+      "models": "matched_resolution_models_at_the_same_factor",
+      "model_training_input": "continuous_height",
+      "test_input": "z_binned_height",
+      "factors": [
+        1,
+        2,
+        3,
+        4,
+        6,
+        8,
+        10,
+        16,
+        32
+      ],
+      "folds": [
+        0,
+        1,
+        2,
+        3,
+        4
+      ]
+    },
+    "detrending": {
+      "description": "A least-squares plane is subtracted from each height map before the native-resolution pipeline.",
+      "method": "least_squares_plane_subtraction",
+      "resolution_factor": 1,
+      "training_input": "detrended_continuous_height",
+      "test_input": "detrended_continuous_height",
+      "folds": [
+        0,
+        1,
+        2,
+        3,
+        4
+      ]
+    },
+    "leave_one_papyrus_out": {
+      "description": "Each partition holds out all samples from one papyrus.",
+      "resolution_factor": 1,
+      "training_input": "continuous_height",
+      "test_input": "continuous_height",
+      "folds": [
+        5,
+        6,
+        7
+      ],
+      "held_out_papyri_by_fold": {
+        "5": "PHerc. 500P2",
+        "6": "PHerc. 250",
+        "7": "PHerc. 248"
+      }
+    }
+  },
+  "dataset_mappings": {
+    "continuous_height": [
+      {
+        "factor": 1,
+        "dataset_id": 701,
+        "dataset_name": "Dataset701_Profilometry2D"
+      },
+      {
+        "factor": 2,
+        "dataset_id": 702,
+        "dataset_name": "Dataset702_Profilometry2D"
+      },
+      {
+        "factor": 3,
+        "dataset_id": 703,
+        "dataset_name": "Dataset703_Profilometry2D"
+      },
+      {
+        "factor": 4,
+        "dataset_id": 704,
+        "dataset_name": "Dataset704_Profilometry2D"
+      },
+      {
+        "factor": 6,
+        "dataset_id": 706,
+        "dataset_name": "Dataset706_Profilometry2D"
+      },
+      {
+        "factor": 8,
+        "dataset_id": 708,
+        "dataset_name": "Dataset708_Profilometry2D"
+      },
+      {
+        "factor": 10,
+        "dataset_id": 710,
+        "dataset_name": "Dataset710_Profilometry2D"
+      },
+      {
+        "factor": 16,
+        "dataset_id": 716,
+        "dataset_name": "Dataset716_Profilometry2D"
+      },
+      {
+        "factor": 32,
+        "dataset_id": 732,
+        "dataset_name": "Dataset732_Profilometry2D"
+      }
+    ],
+    "isotropic_z_binned_height": [
+      {
+        "factor": 1,
+        "dataset_id": 801,
+        "dataset_name": "Dataset801_Profilometry2D_z0p68793625_ds1"
+      },
+      {
+        "factor": 2,
+        "dataset_id": 802,
+        "dataset_name": "Dataset802_Profilometry2D_z1p3758725_ds2"
+      },
+      {
+        "factor": 3,
+        "dataset_id": 803,
+        "dataset_name": "Dataset803_Profilometry2D_z2p06380875_ds3"
+      },
+      {
+        "factor": 4,
+        "dataset_id": 804,
+        "dataset_name": "Dataset804_Profilometry2D_z2p751745_ds4"
+      },
+      {
+        "factor": 6,
+        "dataset_id": 806,
+        "dataset_name": "Dataset806_Profilometry2D_z4p1276175_ds6"
+      },
+      {
+        "factor": 8,
+        "dataset_id": 808,
+        "dataset_name": "Dataset808_Profilometry2D_z5p50349_ds8"
+      },
+      {
+        "factor": 10,
+        "dataset_id": 810,
+        "dataset_name": "Dataset810_Profilometry2D_z6p8793625_ds10"
+      },
+      {
+        "factor": 16,
+        "dataset_id": 816,
+        "dataset_name": "Dataset816_Profilometry2D_z11p00698_ds16"
+      },
+      {
+        "factor": 32,
+        "dataset_id": 832,
+        "dataset_name": "Dataset832_Profilometry2D_z22p01396_ds32"
+      }
+    ]
+  },
+  "training": {
+    "framework": "nnUNetv2",
+    "trainer": "nnUNetTrainer",
+    "configuration": "2d",
+    "plans_identifier": "nnUNetResEncUNetPlans_24G",
+    "epochs": 1000,
+    "training_iterations_per_epoch": 250,
+    "validation_iterations_per_epoch": 50,
+    "early_stopping": false,
+    "checkpoint_for_inference": "checkpoint_best.pth",
+    "loss": "Dice plus cross-entropy",
+    "optimizer": {
+      "name": "SGD",
+      "nesterov": true,
+      "initial_learning_rate": 0.01,
+      "weight_decay": 0.00003,
+      "schedule": "polynomial_decay"
+    },
+    "foreground_oversampling_fraction": 0.33
+  },
+  "software": {
+    "python": "3.12.2",
+    "numpy": "1.26.4",
+    "opencv_python": "4.11.0.86",
+    "pytorch": "2.5.1",
+    "torchvision": "0.20.1",
+    "nnunetv2": "2.5.1",
+    "nnunet_git_commit": "834b80f"
+  }
+}

configs/nnunet/dataset.json

@@ -0,0 +1,12 @@
+{
+  "channel_names": {
+    "0": "T2"
+  },
+  "labels": {
+    "background": 0,
+    "foreground": 1
+  },
+  "numTraining": 14,
+  "file_ending": ".tif",
+  "overwrite_image_reader_writer": "NaturalImage2DIO"
+}

configs/nnunet/plans/Dataset701_Profilometry2D/nnUNetResEncUNetPlans_24G.json

@@ -0,0 +1,207 @@
+{
+  "dataset_name": "Dataset701_Profilometry2D",
+  "plans_name": "nnUNetResEncUNetPlans_24G",
+  "original_median_spacing_after_transp": [
+    999.0,
+    1.0,
+    1.0
+  ],
+  "original_median_shape_after_transp": [
+    1,
+    4484,
+    4928
+  ],
+  "image_reader_writer": "NaturalImage2DIO",
+  "transpose_forward": [
+    0,
+    1,
+    2
+  ],
+  "transpose_backward": [
+    0,
+    1,
+    2
+  ],
+  "configurations": {
+    "2d": {
+      "data_identifier": "nnUNetPlans_2d",
+      "preprocessor_name": "DefaultPreprocessor",
+      "batch_size": 12,
+      "patch_size": [
+        896,
+        896
+      ],
+      "median_image_size_in_voxels": [
+        4483.5,
+        4928.5
+      ],
+      "spacing": [
+        1.0,
+        1.0
+      ],
+      "normalization_schemes": [
+        "ZScoreNormalization"
+      ],
+      "use_mask_for_norm": [
+        false
+      ],
+      "resampling_fn_data": "resample_data_or_seg_to_shape",
+      "resampling_fn_seg": "resample_data_or_seg_to_shape",
+      "resampling_fn_data_kwargs": {
+        "is_seg": false,
+        "order": 3,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_seg_kwargs": {
+        "is_seg": true,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+      "resampling_fn_probabilities_kwargs": {
+        "is_seg": false,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "architecture": {
+        "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+        "arch_kwargs": {
+          "n_stages": 8,
+          "features_per_stage": [
+            32,
+            64,
+            128,
+            256,
+            512,
+            512,
+            512,
+            512
+          ],
+          "conv_op": "torch.nn.modules.conv.Conv2d",
+          "kernel_sizes": [
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ]
+          ],
+          "strides": [
+            [
+              1,
+              1
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ]
+          ],
+          "n_blocks_per_stage": [
+            1,
+            3,
+            4,
+            6,
+            6,
+            6,
+            6,
+            6
+          ],
+          "n_conv_per_stage_decoder": [
+            1,
+            1,
+            1,
+            1,
+            1,
+            1,
+            1
+          ],
+          "conv_bias": true,
+          "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
+          "norm_op_kwargs": {
+            "eps": 1e-05,
+            "affine": true
+          },
+          "dropout_op": null,
+          "dropout_op_kwargs": null,
+          "nonlin": "torch.nn.LeakyReLU",
+          "nonlin_kwargs": {
+            "inplace": true
+          }
+        },
+        "_kw_requires_import": [
+          "conv_op",
+          "norm_op",
+          "dropout_op",
+          "nonlin"
+        ]
+      },
+      "batch_dice": true
+    }
+  },
+  "experiment_planner_used": "nnUNetPlannerResEncM",
+  "label_manager": "LabelManager",
+  "foreground_intensity_properties_per_channel": {
+    "0": {
+      "max": 65535.0,
+      "mean": 31029.177734375,
+      "median": 29157.0,
+      "min": 0.0,
+      "percentile_00_5": 576.0,
+      "percentile_99_5": 65535.0,
+      "std": 15277.822265625
+    }
+  }
+}

configs/nnunet/plans/Dataset702_Profilometry2D/nnUNetResEncUNetPlans_24G.json

@@ -0,0 +1,207 @@
+{
+  "dataset_name": "Dataset702_Profilometry2D",
+  "plans_name": "nnUNetResEncUNetPlans_24G",
+  "original_median_spacing_after_transp": [
+    999.0,
+    1.0,
+    1.0
+  ],
+  "original_median_shape_after_transp": [
+    1,
+    2242,
+    2464
+  ],
+  "image_reader_writer": "NaturalImage2DIO",
+  "transpose_forward": [
+    0,
+    1,
+    2
+  ],
+  "transpose_backward": [
+    0,
+    1,
+    2
+  ],
+  "configurations": {
+    "2d": {
+      "data_identifier": "nnUNetPlans_2d",
+      "preprocessor_name": "DefaultPreprocessor",
+      "batch_size": 12,
+      "patch_size": [
+        896,
+        896
+      ],
+      "median_image_size_in_voxels": [
+        2241.5,
+        2464.0
+      ],
+      "spacing": [
+        1.0,
+        1.0
+      ],
+      "normalization_schemes": [
+        "ZScoreNormalization"
+      ],
+      "use_mask_for_norm": [
+        false
+      ],
+      "resampling_fn_data": "resample_data_or_seg_to_shape",
+      "resampling_fn_seg": "resample_data_or_seg_to_shape",
+      "resampling_fn_data_kwargs": {
+        "is_seg": false,
+        "order": 3,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_seg_kwargs": {
+        "is_seg": true,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+      "resampling_fn_probabilities_kwargs": {
+        "is_seg": false,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "architecture": {
+        "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+        "arch_kwargs": {
+          "n_stages": 8,
+          "features_per_stage": [
+            32,
+            64,
+            128,
+            256,
+            512,
+            512,
+            512,
+            512
+          ],
+          "conv_op": "torch.nn.modules.conv.Conv2d",
+          "kernel_sizes": [
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ]
+          ],
+          "strides": [
+            [
+              1,
+              1
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ]
+          ],
+          "n_blocks_per_stage": [
+            1,
+            3,
+            4,
+            6,
+            6,
+            6,
+            6,
+            6
+          ],
+          "n_conv_per_stage_decoder": [
+            1,
+            1,
+            1,
+            1,
+            1,
+            1,
+            1
+          ],
+          "conv_bias": true,
+          "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
+          "norm_op_kwargs": {
+            "eps": 1e-05,
+            "affine": true
+          },
+          "dropout_op": null,
+          "dropout_op_kwargs": null,
+          "nonlin": "torch.nn.LeakyReLU",
+          "nonlin_kwargs": {
+            "inplace": true
+          }
+        },
+        "_kw_requires_import": [
+          "conv_op",
+          "norm_op",
+          "dropout_op",
+          "nonlin"
+        ]
+      },
+      "batch_dice": true
+    }
+  },
+  "experiment_planner_used": "nnUNetPlannerResEncM",
+  "label_manager": "LabelManager",
+  "foreground_intensity_properties_per_channel": {
+    "0": {
+      "max": 65535.0,
+      "mean": 30935.96484375,
+      "median": 29055.0,
+      "min": 0.0,
+      "percentile_00_5": 589.0,
+      "percentile_99_5": 65535.0,
+      "std": 15267.794921875
+    }
+  }
+}

configs/nnunet/plans/Dataset703_Profilometry2D/nnUNetResEncUNetPlans_24G.json

@@ -0,0 +1,207 @@
+{
+  "dataset_name": "Dataset703_Profilometry2D",
+  "plans_name": "nnUNetResEncUNetPlans_24G",
+  "original_median_spacing_after_transp": [
+    999.0,
+    1.0,
+    1.0
+  ],
+  "original_median_shape_after_transp": [
+    1,
+    1494,
+    1642
+  ],
+  "image_reader_writer": "NaturalImage2DIO",
+  "transpose_forward": [
+    0,
+    1,
+    2
+  ],
+  "transpose_backward": [
+    0,
+    1,
+    2
+  ],
+  "configurations": {
+    "2d": {
+      "data_identifier": "nnUNetPlans_2d",
+      "preprocessor_name": "DefaultPreprocessor",
+      "batch_size": 12,
+      "patch_size": [
+        896,
+        896
+      ],
+      "median_image_size_in_voxels": [
+        1494.0,
+        1642.5
+      ],
+      "spacing": [
+        1.0,
+        1.0
+      ],
+      "normalization_schemes": [
+        "ZScoreNormalization"
+      ],
+      "use_mask_for_norm": [
+        false
+      ],
+      "resampling_fn_data": "resample_data_or_seg_to_shape",
+      "resampling_fn_seg": "resample_data_or_seg_to_shape",
+      "resampling_fn_data_kwargs": {
+        "is_seg": false,
+        "order": 3,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_seg_kwargs": {
+        "is_seg": true,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+      "resampling_fn_probabilities_kwargs": {
+        "is_seg": false,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "architecture": {
+        "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+        "arch_kwargs": {
+          "n_stages": 8,
+          "features_per_stage": [
+            32,
+            64,
+            128,
+            256,
+            512,
+            512,
+            512,
+            512
+          ],
+          "conv_op": "torch.nn.modules.conv.Conv2d",
+          "kernel_sizes": [
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ]
+          ],
+          "strides": [
+            [
+              1,
+              1
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ]
+          ],
+          "n_blocks_per_stage": [
+            1,
+            3,
+            4,
+            6,
+            6,
+            6,
+            6,
+            6
+          ],
+          "n_conv_per_stage_decoder": [
+            1,
+            1,
+            1,
+            1,
+            1,
+            1,
+            1
+          ],
+          "conv_bias": true,
+          "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
+          "norm_op_kwargs": {
+            "eps": 1e-05,
+            "affine": true
+          },
+          "dropout_op": null,
+          "dropout_op_kwargs": null,
+          "nonlin": "torch.nn.LeakyReLU",
+          "nonlin_kwargs": {
+            "inplace": true
+          }
+        },
+        "_kw_requires_import": [
+          "conv_op",
+          "norm_op",
+          "dropout_op",
+          "nonlin"
+        ]
+      },
+      "batch_dice": true
+    }
+  },
+  "experiment_planner_used": "nnUNetPlannerResEncM",
+  "label_manager": "LabelManager",
+  "foreground_intensity_properties_per_channel": {
+    "0": {
+      "max": 65535.0,
+      "mean": 30894.5546875,
+      "median": 29006.0,
+      "min": 0.0,
+      "percentile_00_5": 583.0,
+      "percentile_99_5": 65535.0,
+      "std": 15258.044921875
+    }
+  }
+}

configs/nnunet/plans/Dataset704_Profilometry2D/nnUNetResEncUNetPlans_24G.json

@@ -0,0 +1,207 @@
+{
+  "dataset_name": "Dataset704_Profilometry2D",
+  "plans_name": "nnUNetResEncUNetPlans_24G",
+  "original_median_spacing_after_transp": [
+    999.0,
+    1.0,
+    1.0
+  ],
+  "original_median_shape_after_transp": [
+    1,
+    1120,
+    1232
+  ],
+  "image_reader_writer": "NaturalImage2DIO",
+  "transpose_forward": [
+    0,
+    1,
+    2
+  ],
+  "transpose_backward": [
+    0,
+    1,
+    2
+  ],
+  "configurations": {
+    "2d": {
+      "data_identifier": "nnUNetPlans_2d",
+      "preprocessor_name": "DefaultPreprocessor",
+      "batch_size": 12,
+      "patch_size": [
+        896,
+        896
+      ],
+      "median_image_size_in_voxels": [
+        1120.5,
+        1231.5
+      ],
+      "spacing": [
+        1.0,
+        1.0
+      ],
+      "normalization_schemes": [
+        "ZScoreNormalization"
+      ],
+      "use_mask_for_norm": [
+        false
+      ],
+      "resampling_fn_data": "resample_data_or_seg_to_shape",
+      "resampling_fn_seg": "resample_data_or_seg_to_shape",
+      "resampling_fn_data_kwargs": {
+        "is_seg": false,
+        "order": 3,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_seg_kwargs": {
+        "is_seg": true,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+      "resampling_fn_probabilities_kwargs": {
+        "is_seg": false,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "architecture": {
+        "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+        "arch_kwargs": {
+          "n_stages": 8,
+          "features_per_stage": [
+            32,
+            64,
+            128,
+            256,
+            512,
+            512,
+            512,
+            512
+          ],
+          "conv_op": "torch.nn.modules.conv.Conv2d",
+          "kernel_sizes": [
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ]
+          ],
+          "strides": [
+            [
+              1,
+              1
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ]
+          ],
+          "n_blocks_per_stage": [
+            1,
+            3,
+            4,
+            6,
+            6,
+            6,
+            6,
+            6
+          ],
+          "n_conv_per_stage_decoder": [
+            1,
+            1,
+            1,
+            1,
+            1,
+            1,
+            1
+          ],
+          "conv_bias": true,
+          "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
+          "norm_op_kwargs": {
+            "eps": 1e-05,
+            "affine": true
+          },
+          "dropout_op": null,
+          "dropout_op_kwargs": null,
+          "nonlin": "torch.nn.LeakyReLU",
+          "nonlin_kwargs": {
+            "inplace": true
+          }
+        },
+        "_kw_requires_import": [
+          "conv_op",
+          "norm_op",
+          "dropout_op",
+          "nonlin"
+        ]
+      },
+      "batch_dice": true
+    }
+  },
+  "experiment_planner_used": "nnUNetPlannerResEncM",
+  "label_manager": "LabelManager",
+  "foreground_intensity_properties_per_channel": {
+    "0": {
+      "max": 65535.0,
+      "mean": 30862.12109375,
+      "median": 28972.0,
+      "min": 0.0,
+      "percentile_00_5": 581.0,
+      "percentile_99_5": 65530.0,
+      "std": 15248.9931640625
+    }
+  }
+}

configs/nnunet/plans/Dataset706_Profilometry2D/nnUNetResEncUNetPlans_24G.json

@@ -0,0 +1,207 @@
+{
+  "dataset_name": "Dataset706_Profilometry2D",
+  "plans_name": "nnUNetResEncUNetPlans_24G",
+  "original_median_spacing_after_transp": [
+    999.0,
+    1.0,
+    1.0
+  ],
+  "original_median_shape_after_transp": [
+    1,
+    747,
+    821
+  ],
+  "image_reader_writer": "NaturalImage2DIO",
+  "transpose_forward": [
+    0,
+    1,
+    2
+  ],
+  "transpose_backward": [
+    0,
+    1,
+    2
+  ],
+  "configurations": {
+    "2d": {
+      "data_identifier": "nnUNetPlans_2d",
+      "preprocessor_name": "DefaultPreprocessor",
+      "batch_size": 12,
+      "patch_size": [
+        768,
+        896
+      ],
+      "median_image_size_in_voxels": [
+        747.0,
+        821.0
+      ],
+      "spacing": [
+        1.0,
+        1.0
+      ],
+      "normalization_schemes": [
+        "ZScoreNormalization"
+      ],
+      "use_mask_for_norm": [
+        false
+      ],
+      "resampling_fn_data": "resample_data_or_seg_to_shape",
+      "resampling_fn_seg": "resample_data_or_seg_to_shape",
+      "resampling_fn_data_kwargs": {
+        "is_seg": false,
+        "order": 3,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_seg_kwargs": {
+        "is_seg": true,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+      "resampling_fn_probabilities_kwargs": {
+        "is_seg": false,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "architecture": {
+        "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+        "arch_kwargs": {
+          "n_stages": 8,
+          "features_per_stage": [
+            32,
+            64,
+            128,
+            256,
+            512,
+            512,
+            512,
+            512
+          ],
+          "conv_op": "torch.nn.modules.conv.Conv2d",
+          "kernel_sizes": [
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ]
+          ],
+          "strides": [
+            [
+              1,
+              1
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ]
+          ],
+          "n_blocks_per_stage": [
+            1,
+            3,
+            4,
+            6,
+            6,
+            6,
+            6,
+            6
+          ],
+          "n_conv_per_stage_decoder": [
+            1,
+            1,
+            1,
+            1,
+            1,
+            1,
+            1
+          ],
+          "conv_bias": true,
+          "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
+          "norm_op_kwargs": {
+            "eps": 1e-05,
+            "affine": true
+          },
+          "dropout_op": null,
+          "dropout_op_kwargs": null,
+          "nonlin": "torch.nn.LeakyReLU",
+          "nonlin_kwargs": {
+            "inplace": true
+          }
+        },
+        "_kw_requires_import": [
+          "conv_op",
+          "norm_op",
+          "dropout_op",
+          "nonlin"
+        ]
+      },
+      "batch_dice": true
+    }
+  },
+  "experiment_planner_used": "nnUNetPlannerResEncM",
+  "label_manager": "LabelManager",
+  "foreground_intensity_properties_per_channel": {
+    "0": {
+      "max": 65535.0,
+      "mean": 30812.162109375,
+      "median": 28920.0,
+      "min": 0.0,
+      "percentile_00_5": 574.0,
+      "percentile_99_5": 65482.0,
+      "std": 15234.9052734375
+    }
+  }
+}

configs/nnunet/plans/Dataset708_Profilometry2D/nnUNetResEncUNetPlans_24G.json

@@ -0,0 +1,207 @@
+{
+  "dataset_name": "Dataset708_Profilometry2D",
+  "plans_name": "nnUNetResEncUNetPlans_24G",
+  "original_median_spacing_after_transp": [
+    999.0,
+    1.0,
+    1.0
+  ],
+  "original_median_shape_after_transp": [
+    1,
+    560,
+    616
+  ],
+  "image_reader_writer": "NaturalImage2DIO",
+  "transpose_forward": [
+    0,
+    1,
+    2
+  ],
+  "transpose_backward": [
+    0,
+    1,
+    2
+  ],
+  "configurations": {
+    "2d": {
+      "data_identifier": "nnUNetPlans_2d",
+      "preprocessor_name": "DefaultPreprocessor",
+      "batch_size": 12,
+      "patch_size": [
+        640,
+        640
+      ],
+      "median_image_size_in_voxels": [
+        560.0,
+        615.5
+      ],
+      "spacing": [
+        1.0,
+        1.0
+      ],
+      "normalization_schemes": [
+        "ZScoreNormalization"
+      ],
+      "use_mask_for_norm": [
+        false
+      ],
+      "resampling_fn_data": "resample_data_or_seg_to_shape",
+      "resampling_fn_seg": "resample_data_or_seg_to_shape",
+      "resampling_fn_data_kwargs": {
+        "is_seg": false,
+        "order": 3,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_seg_kwargs": {
+        "is_seg": true,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+      "resampling_fn_probabilities_kwargs": {
+        "is_seg": false,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "architecture": {
+        "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+        "arch_kwargs": {
+          "n_stages": 8,
+          "features_per_stage": [
+            32,
+            64,
+            128,
+            256,
+            512,
+            512,
+            512,
+            512
+          ],
+          "conv_op": "torch.nn.modules.conv.Conv2d",
+          "kernel_sizes": [
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ]
+          ],
+          "strides": [
+            [
+              1,
+              1
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ]
+          ],
+          "n_blocks_per_stage": [
+            1,
+            3,
+            4,
+            6,
+            6,
+            6,
+            6,
+            6
+          ],
+          "n_conv_per_stage_decoder": [
+            1,
+            1,
+            1,
+            1,
+            1,
+            1,
+            1
+          ],
+          "conv_bias": true,
+          "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
+          "norm_op_kwargs": {
+            "eps": 1e-05,
+            "affine": true
+          },
+          "dropout_op": null,
+          "dropout_op_kwargs": null,
+          "nonlin": "torch.nn.LeakyReLU",
+          "nonlin_kwargs": {
+            "inplace": true
+          }
+        },
+        "_kw_requires_import": [
+          "conv_op",
+          "norm_op",
+          "dropout_op",
+          "nonlin"
+        ]
+      },
+      "batch_dice": true
+    }
+  },
+  "experiment_planner_used": "nnUNetPlannerResEncM",
+  "label_manager": "LabelManager",
+  "foreground_intensity_properties_per_channel": {
+    "0": {
+      "max": 65535.0,
+      "mean": 30810.236328125,
+      "median": 28911.0,
+      "min": 0.0,
+      "percentile_00_5": 592.0,
+      "percentile_99_5": 65454.0,
+      "std": 15234.26953125
+    }
+  }
+}

configs/nnunet/plans/Dataset710_Profilometry2D/nnUNetResEncUNetPlans_24G.json

@@ -0,0 +1,196 @@
+{
+  "dataset_name": "Dataset710_Profilometry2D",
+  "plans_name": "nnUNetResEncUNetPlans_24G",
+  "original_median_spacing_after_transp": [
+    999.0,
+    1.0,
+    1.0
+  ],
+  "original_median_shape_after_transp": [
+    1,
+    448,
+    492
+  ],
+  "image_reader_writer": "NaturalImage2DIO",
+  "transpose_forward": [
+    0,
+    1,
+    2
+  ],
+  "transpose_backward": [
+    0,
+    1,
+    2
+  ],
+  "configurations": {
+    "2d": {
+      "data_identifier": "nnUNetPlans_2d",
+      "preprocessor_name": "DefaultPreprocessor",
+      "batch_size": 13,
+      "patch_size": [
+        448,
+        512
+      ],
+      "median_image_size_in_voxels": [
+        447.5,
+        492.0
+      ],
+      "spacing": [
+        1.0,
+        1.0
+      ],
+      "normalization_schemes": [
+        "ZScoreNormalization"
+      ],
+      "use_mask_for_norm": [
+        false
+      ],
+      "resampling_fn_data": "resample_data_or_seg_to_shape",
+      "resampling_fn_seg": "resample_data_or_seg_to_shape",
+      "resampling_fn_data_kwargs": {
+        "is_seg": false,
+        "order": 3,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_seg_kwargs": {
+        "is_seg": true,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+      "resampling_fn_probabilities_kwargs": {
+        "is_seg": false,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "architecture": {
+        "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+        "arch_kwargs": {
+          "n_stages": 7,
+          "features_per_stage": [
+            32,
+            64,
+            128,
+            256,
+            512,
+            512,
+            512
+          ],
+          "conv_op": "torch.nn.modules.conv.Conv2d",
+          "kernel_sizes": [
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ]
+          ],
+          "strides": [
+            [
+              1,
+              1
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ]
+          ],
+          "n_blocks_per_stage": [
+            1,
+            3,
+            4,
+            6,
+            6,
+            6,
+            6
+          ],
+          "n_conv_per_stage_decoder": [
+            1,
+            1,
+            1,
+            1,
+            1,
+            1
+          ],
+          "conv_bias": true,
+          "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
+          "norm_op_kwargs": {
+            "eps": 1e-05,
+            "affine": true
+          },
+          "dropout_op": null,
+          "dropout_op_kwargs": null,
+          "nonlin": "torch.nn.LeakyReLU",
+          "nonlin_kwargs": {
+            "inplace": true
+          }
+        },
+        "_kw_requires_import": [
+          "conv_op",
+          "norm_op",
+          "dropout_op",
+          "nonlin"
+        ]
+      },
+      "batch_dice": true
+    }
+  },
+  "experiment_planner_used": "nnUNetPlannerResEncM",
+  "label_manager": "LabelManager",
+  "foreground_intensity_properties_per_channel": {
+    "0": {
+      "max": 65535.0,
+      "mean": 30816.349609375,
+      "median": 28922.0,
+      "min": 0.0,
+      "percentile_00_5": 586.0,
+      "percentile_99_5": 65432.0,
+      "std": 15234.087890625
+    }
+  }
+}

configs/nnunet/plans/Dataset716_Profilometry2D/nnUNetResEncUNetPlans_24G.json

@@ -0,0 +1,196 @@
+{
+  "dataset_name": "Dataset716_Profilometry2D",
+  "plans_name": "nnUNetResEncUNetPlans_24G",
+  "original_median_spacing_after_transp": [
+    999.0,
+    1.0,
+    1.0
+  ],
+  "original_median_shape_after_transp": [
+    1,
+    280,
+    308
+  ],
+  "image_reader_writer": "NaturalImage2DIO",
+  "transpose_forward": [
+    0,
+    1,
+    2
+  ],
+  "transpose_backward": [
+    0,
+    1,
+    2
+  ],
+  "configurations": {
+    "2d": {
+      "data_identifier": "nnUNetPlans_2d",
+      "preprocessor_name": "DefaultPreprocessor",
+      "batch_size": 12,
+      "patch_size": [
+        320,
+        320
+      ],
+      "median_image_size_in_voxels": [
+        279.5,
+        307.5
+      ],
+      "spacing": [
+        1.0,
+        1.0
+      ],
+      "normalization_schemes": [
+        "ZScoreNormalization"
+      ],
+      "use_mask_for_norm": [
+        false
+      ],
+      "resampling_fn_data": "resample_data_or_seg_to_shape",
+      "resampling_fn_seg": "resample_data_or_seg_to_shape",
+      "resampling_fn_data_kwargs": {
+        "is_seg": false,
+        "order": 3,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_seg_kwargs": {
+        "is_seg": true,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+      "resampling_fn_probabilities_kwargs": {
+        "is_seg": false,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "architecture": {
+        "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+        "arch_kwargs": {
+          "n_stages": 7,
+          "features_per_stage": [
+            32,
+            64,
+            128,
+            256,
+            512,
+            512,
+            512
+          ],
+          "conv_op": "torch.nn.modules.conv.Conv2d",
+          "kernel_sizes": [
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ]
+          ],
+          "strides": [
+            [
+              1,
+              1
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ]
+          ],
+          "n_blocks_per_stage": [
+            1,
+            3,
+            4,
+            6,
+            6,
+            6,
+            6
+          ],
+          "n_conv_per_stage_decoder": [
+            1,
+            1,
+            1,
+            1,
+            1,
+            1
+          ],
+          "conv_bias": true,
+          "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
+          "norm_op_kwargs": {
+            "eps": 1e-05,
+            "affine": true
+          },
+          "dropout_op": null,
+          "dropout_op_kwargs": null,
+          "nonlin": "torch.nn.LeakyReLU",
+          "nonlin_kwargs": {
+            "inplace": true
+          }
+        },
+        "_kw_requires_import": [
+          "conv_op",
+          "norm_op",
+          "dropout_op",
+          "nonlin"
+        ]
+      },
+      "batch_dice": true
+    }
+  },
+  "experiment_planner_used": "nnUNetPlannerResEncM",
+  "label_manager": "LabelManager",
+  "foreground_intensity_properties_per_channel": {
+    "0": {
+      "max": 65535.0,
+      "mean": 30801.28125,
+      "median": 28918.0,
+      "min": 0.0,
+      "percentile_00_5": 585.0,
+      "percentile_99_5": 65441.0,
+      "std": 15243.27734375
+    }
+  }
+}

configs/nnunet/plans/Dataset732_Profilometry2D/nnUNetResEncUNetPlans_24G.json

@@ -0,0 +1,185 @@
+{
+  "dataset_name": "Dataset732_Profilometry2D",
+  "plans_name": "nnUNetResEncUNetPlans_24G",
+  "original_median_spacing_after_transp": [
+    999.0,
+    1.0,
+    1.0
+  ],
+  "original_median_shape_after_transp": [
+    1,
+    140,
+    154
+  ],
+  "image_reader_writer": "NaturalImage2DIO",
+  "transpose_forward": [
+    0,
+    1,
+    2
+  ],
+  "transpose_backward": [
+    0,
+    1,
+    2
+  ],
+  "configurations": {
+    "2d": {
+      "data_identifier": "nnUNetPlans_2d",
+      "preprocessor_name": "DefaultPreprocessor",
+      "batch_size": 12,
+      "patch_size": [
+        160,
+        160
+      ],
+      "median_image_size_in_voxels": [
+        139.5,
+        153.5
+      ],
+      "spacing": [
+        1.0,
+        1.0
+      ],
+      "normalization_schemes": [
+        "ZScoreNormalization"
+      ],
+      "use_mask_for_norm": [
+        false
+      ],
+      "resampling_fn_data": "resample_data_or_seg_to_shape",
+      "resampling_fn_seg": "resample_data_or_seg_to_shape",
+      "resampling_fn_data_kwargs": {
+        "is_seg": false,
+        "order": 3,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_seg_kwargs": {
+        "is_seg": true,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "resampling_fn_probabilities": "resample_data_or_seg_to_shape",
+      "resampling_fn_probabilities_kwargs": {
+        "is_seg": false,
+        "order": 1,
+        "order_z": 0,
+        "force_separate_z": null
+      },
+      "architecture": {
+        "network_class_name": "dynamic_network_architectures.architectures.unet.ResidualEncoderUNet",
+        "arch_kwargs": {
+          "n_stages": 6,
+          "features_per_stage": [
+            32,
+            64,
+            128,
+            256,
+            512,
+            512
+          ],
+          "conv_op": "torch.nn.modules.conv.Conv2d",
+          "kernel_sizes": [
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ],
+            [
+              3,
+              3
+            ]
+          ],
+          "strides": [
+            [
+              1,
+              1
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ],
+            [
+              2,
+              2
+            ]
+          ],
+          "n_blocks_per_stage": [
+            1,
+            3,
+            4,
+            6,
+            6,
+            6
+          ],
+          "n_conv_per_stage_decoder": [
+            1,
+            1,
+            1,
+            1,
+            1
+          ],
+          "conv_bias": true,
+          "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d",
+          "norm_op_kwargs": {
+            "eps": 1e-05,
+            "affine": true
+          },
+          "dropout_op": null,
+          "dropout_op_kwargs": null,
+          "nonlin": "torch.nn.LeakyReLU",
+          "nonlin_kwargs": {
+            "inplace": true
+          }
+        },
+        "_kw_requires_import": [
+          "conv_op",
+          "norm_op",
+          "dropout_op",
+          "nonlin"
+        ]
+      },
+      "batch_dice": true
+    }
+  },
+  "experiment_planner_used": "nnUNetPlannerResEncM",
+  "label_manager": "LabelManager",
+  "foreground_intensity_properties_per_channel": {
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