{
  "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"
  }
}