First commit : scripted train/test split with README

README.md

@@ -1,3 +1,72 @@
 ---
-license: cc-by-4.0
+language:
+- en
+pretty_name: Retinal Vessel Segmentation from Holographic Doppler Imaging
+tags:
+- medical-imaging
+- holography
+- retina
+- vessel-segmentation
+- deep-learning
+- artery-vein-segmentation
+- doppler-imaging
+task_categories:
+- image-segmentation
+license: cc-by-nc-4.0
+size_categories:
+- n<1K
 ---
+
+# Retinal Vessel Segmentation from Holographic Doppler Imaging
+
+This dataset provides paired images and segmentation masks for **retinal vessel segmentation** from **holographic Doppler imaging**.  
+It is designed for research in **computational imaging**, **retinal blood flow analysis**, and **deep learning-based vessel segmentation**.
+
+---
+
+## Dataset Summary
+
+Holographic Doppler imaging enables quantitative blood flow mapping in the retina by analyzing temporal fluctuations in optical interferograms.  
+This dataset includes **power Doppler images (M0)** and their corresponding **artery and vein segmentation masks**, representing the spatial distribution of retinal blood flow observed through laser Doppler holography.
+Temporal informations derived from the arterial signal, namely the correlation map and the diasys image, are also provided. 
+See the article submitted to ISBI2026 (*linked soon*) for more details
+
+---
+
+## Data Description
+
+| Attribute | Description |
+|:-----------|:-------------|
+| **Imaging modality** | Holographic Laser Doppler imaging of the retina |
+| **Input type** | Power Doppler intensity images (M0), Correlation map, Diasys image
+| **Target type** | Artery and Vein masks : binary segmentation masks (vessels vs. background) |
+| **Classes** | 0: Background, 1: Vessel |
+| **File format** | PNG (8-bit grayscale) |
+| **Typical image size** | 1023 × 1023 pixels |
+| **Sampling** | Retinal field of view from high-speed interferometric acquisitions |
+| **Preprocessing** | Temporal SVD filtering, Fresnel reconstruction, power Doppler accumulation |
+
+---
+
+## Dataset Structure
+
+HoloDopplerSegISBI/
+├── dataset.py
+├── README.md
+├── train/
+│   ├── measure_001/
+│   │   ├── M0.png
+│   │   ├── correlation.png
+│   │   ├── diasys.png
+│   │   ├── maskArtery.png
+│   │   └── maskVein.png
+│   └── ...
+└── test/
+    ├── measure_122/
+    └── ...
+
+
+Measures have the following form : *id*\_*eye*\_*\_number*, with:
+- *id* corresponding to a unique patient.
+- *eye* equals to 'R' or 'L', respectively for right eye or left eye
+- *number* indicating which measure of the same eye it is

dataset.py

@@ -0,0 +1,47 @@
+import os
+from datasets import DatasetBuilder, DatasetInfo, SplitGenerator, Split, Features, Value
+
+class DopplerRetina(DatasetBuilder):
+    VERSION = "1.0.0"
+
+    def _info(self):
+        return DatasetInfo(
+            description="Retinal Doppler artery/vein segmentation dataset",
+            features=Features({
+                "measure_id": Value("string"),
+                "m0": Value("string"),
+                "correlation": Value("string"),
+                "diasys": Value("string"),
+                "artery_mask": Value("string"),
+                "vein_mask": Value("string"),
+            }),
+        )
+
+    def _split_generators(self, dl_manager):
+        repo_dir = dl_manager.download_and_extract(self.base_path)
+
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={"path": os.path.join(repo_dir, "train")},
+            ),
+            SplitGenerator(
+                name=Split.TEST,
+                gen_kwargs={"path": os.path.join(repo_dir, "test")},
+            ),
+        ]
+
+    def _generate_examples(self, path):
+        for measure in sorted(os.listdir(path)):
+            mdir = os.path.join(path, measure)
+            if not os.path.isdir(mdir):
+                continue
+
+            yield measure, {
+                "measure_id": measure,
+                "m0": os.path.join(mdir, "M0.png"),
+                "correlation": os.path.join(mdir, "correlation.png"),
+                "diasys": os.path.join(mdir, "diasys.png"),
+                "artery_mask": os.path.join(mdir, "maskArtery.png"),
+                "vein_mask": os.path.join(mdir, "maskVein.png"),
+            }