kaist-snubh-barreleye: sync pipeline.yaml, data card and main image with GitHub

kaist-snubh-barreleye/README.md

@@ -1,17 +1,42 @@
-# Breast OpenH-RF
-
-*An in-vivo human breast plane-wave raw-channel ultrasound sub-dataset for the OpenH-RF foundation initiative.*
+---
+name: kaist-snubh-barreleye
+pretty_name: "KAIST–SNUBH In-vivo Breast Plane-Wave RF"
+license: cc-by-4.0
+task_categories:
+  - image-classification
+  - other
+tags:
+  - ultrasound
+  - rf
+  - openh-rf
+  - breast
+  - in-vivo
+  - plane-wave
+  - sound-speed-estimation
+language:
+  - en
+size_categories:
+  - n<1K
+---
+
+# KAIST–SNUBH In-vivo Breast Plane-Wave RF
+
+![DAS B-mode reconstruction of a biopsy-proven invasive ductal carcinoma (S01_D1)](assets/main.png)
+
+*Delay-and-sum reconstruction of a biopsy-proven invasive ductal carcinoma, [`data/S01_D1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/kaist-snubh-barreleye/data/S01_D1.hdf5).*
 
 ## Dataset Description
 
-Breast OpenH-RF contains pre-beamformed RF channel-capture data from in-vivo breast ultrasound exams performed on a clinical, FDA-cleared scanner. Every acquisition is a 9-angle plane-wave compounding sequence with a 192-element linear array, paired with a B-mode reference image and a clinically verified diagnostic label. The intended research contribution is two-fold: (1) provide a clinically-grounded benchmark for **sound-speed and attenuation imaging** (Section 6.3 of the RFP) on real human breast tissue with biopsy-proven outcomes and (2) supply a high-quality plane-wave compounding corpus for **generalized reconstruction** research (Section 6.1: super-resolution, aberration correction, adaptive transmit design). Pathology and BI-RADS labels additionally enable benchmarking of **ultrasound interpretation** (Section 6.5).
+This dataset contains pre-beamformed RF channel-capture data from in-vivo breast ultrasound exams performed on a clinical, FDA-cleared scanner. Every acquisition is a 9-angle plane-wave compounding sequence with a 192-element linear array, paired with a B-mode reference image and a clinically verified diagnostic label. The intended research contribution is two-fold: (1) provide a clinically-grounded benchmark for **sound-speed and attenuation imaging** (Section 6.3 of the RFP) on real human breast tissue with biopsy-proven outcomes and (2) supply a high-quality plane-wave compounding corpus for **generalized reconstruction** research (Section 6.1: super-resolution, aberration correction, adaptive transmit design). Pathology and BI-RADS labels additionally enable benchmarking of **ultrasound interpretation** (Section 6.5).
 
 ## Dataset Contributor(s)
 
-- **Lead PI:** Prof. Hyeon-Min Bae — KAIST, School of Electrical Engineering
-- **Co-investigators (KAIST / Barreleye Inc.):** Seok-Hwan Oh, Myeong-Gee Kim, Young-Min Kim, HyeonJik Lee
-- **Clinical co-investigator:** Hyuk-sool Kwon — Seoul National University Bundang Hospital (SNUBH)
-- **Primary point of contact:** Seok-Hwan Oh (Barreleye Inc., Korea) — shoh@barreleye.co.kr
+- Hyeon-Min Bae (lead PI; KAIST, School of Electrical Engineering)
+- Seok-Hwan Oh <shoh@barreleye.co.kr> (primary point of contact; KAIST / Barreleye Inc.)
+- Myeong-Gee Kim (KAIST / Barreleye Inc.)
+- Young-Min Kim (KAIST / Barreleye Inc.)
+- HyeonJik Lee (KAIST / Barreleye Inc.)
+- Hyuk-sool Kwon (clinical co-investigator; Seoul National University Bundang Hospital, SNUBH)
 
 ## Dataset Creation Date
 
@@ -19,7 +44,7 @@ Breast OpenH-RF contains pre-beamformed RF channel-capture data from in-vivo bre
 
 ## License / Terms of Use
 
-**CC BY 4.0** (see [`LICENCE`](LICENCE)).
+[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). Retain attribution and identify modifications when reusing the data.
 
 ## Intended Usage
 
@@ -37,8 +62,28 @@ Quantitative imaging (sound-speed / attenuation estimation), generalized reconst
   - ADC sampling: **62.5 MHz**, exported as float32.
   - Transmit: 9-angle plane-wave compounding at **[-15, -10, -5, -2.5, 0, +2.5, +5, +10, +15]°**.
 
+## Processing the Dataset
+
+The acquisitions can be processed with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea).
+
+`zea` streams the data from the Hugging Face Hub and processes it according to the pipeline. You can try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/kaist-snubh-barreleye/data/S01_D1.hdf5 \
+  --config hf://nvidia/OpenH-RF/kaist-snubh-barreleye/pipeline.yaml \
+  --n-frames 1 \
+  --save-as png
+```
+
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/kaist-snubh-barreleye/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF).
+
+This is a single-frame acquisition, so `zea process` outputs a `.png` rather than a `.gif` — this requires a `zea` build newer than the currently pinned 0.1.6 (single-frame PNG output landed after that release).
+
 ## Dataset Format
 
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
 All data is delivered in the **zea HDF5** format (OpenH-RF spec). One HDF5 file per acquisition; one image track per file.
 
 ```
@@ -52,7 +97,6 @@ hdf5/
 - **Channel reordering** of the scanner's raw export into the OpenH-RF convention `(n_frames=1, n_tx=9, n_ax=Ns, n_el=192, n_ch=1)`.
 - No demodulation, decimation, band-pass filtering, or value clipping — `raw_data` is bit-faithful to the scanner export.
 
-
 ## Dataset Quantification
 
 **Current OpenH-RF release:** 70 HDF5 files; 802.10 MB (802,095,104 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
@@ -91,7 +135,7 @@ Per-acquisition feature table (one row per HDF5):
 
 ## Subject Metadata
 
-Per-file metadata follows the **HIPAA Safe-Harbor** approach: only **de-identified subject ID, sex, anatomy, binary label, BI-RADS, pathology subtype** are stored. Free-text identifiers, exact age, exact lesion size, and exam dates are deliberately **omitted from the HDF5 files**. 
+Per-file metadata follows the **HIPAA Safe-Harbor** approach: only **de-identified subject ID, sex, anatomy, binary label, BI-RADS, pathology subtype** are stored. Free-text identifiers, exact age, exact lesion size, and exam dates are deliberately **omitted from the HDF5 files**.
 
 - **Number of subjects:** 35
 - **Sex distribution:** 100% female (35/35)
@@ -142,20 +186,15 @@ Plane-wave transmit beamforming is used, with all 192 elements activated on each
 
 ## Data Validation
 
-A reference reconstruction is provided in [`reconstruct.py`](reconstruct.py). It defines the DAS pipeline directly in code (via zea ops) and applies, to the raw RF channel data:
+`reconstruct.py` reconstructs a B-mode from `raw_data` using the `zea.Pipeline` defined in [`pipeline.yaml`](pipeline.yaml):
 
 ```
 cast(float32) → band-pass filter (1–12 MHz) → demodulate → DAS beamform → envelope detect → normalize → log compression
 ```
 
-The 1–12 MHz band-pass rejects a persistent sub-MHz band before coherent beamforming, which allows to produce a clean B-mode directly from the raw RF. Run it to reproduce a reference B-mode from any delivered file:
-
-```bash
-python reconstruct.py --input hdf5/original/S01_D1.hdf5     # single file
-python reconstruct.py --compare S01_D1                      # DAS vs. scanner reference B-mode
-```
+The 1–12 MHz band-pass rejects a persistent sub-MHz band before coherent beamforming, which allows it to produce a clean B-mode directly from the raw RF.
 
-`python reconstruct.py --save-yaml` exports the pipeline as `pipeline.yaml` if a shareable recipe is needed (the script itself does not load it).
+Reference output: `main.png` — `data/S01_D1.hdf5` (biopsy-proven invasive ductal carcinoma), shown above.
 
 ## Known Issues
 
@@ -167,5 +206,5 @@ python reconstruct.py --compare S01_D1                      # DAS vs. scanner re
 
 - **Consent status:** All subjects gave informed consent under SNUBH IRB protocol **B-2401-876-301**.
 - **De-identification:** No direct identifiers (name, full exam date, free-text clinical notes) are stored. Age is decade-binned at the dataset level (not stored per file); exact lesion size and exam dates are not stored per file; only the acquisition year (2024) is reported. Subject IDs are coded (`S01`…`S35`).
-- **IRB approval:** SNUBH IRB **B-2401-876-301** 
+- **IRB approval:** SNUBH IRB **B-2401-876-301**
 - **Animal welfare (ARRIVE 2.0):** Not applicable — human-only dataset.

kaist-snubh-barreleye/pipeline.yaml

@@ -1,3 +1,15 @@
+parameters:
+    xlims:
+    - -0.0191
+    - 0.0191
+    zlims:
+    - 0.002
+    - 0.04
+    dynamic_range:
+    - -50
+    - 0
+    apply_lens_correction: false
+    f_number: 1.5
 pipeline:
     operations:
     -   name: keras.ops.cast