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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - image-segmentation
+  - image-classification
+tags:
+  - image
+  - medical
+  - medical-imaging
+  - surgery
+  - surgical
+  - surgical-data-science
+  - laparoscopic
+  - semantic-segmentation
+  - organ-segmentation
+pretty_name: Dresden Surgical Anatomy Dataset (DSAD)
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: single_organ
+    data_files:
+      - split: train
+        path: single_organ/train.parquet
+      - split: validation
+        path: single_organ/validation.parquet
+      - split: test
+        path: single_organ/test.parquet
+    default: true
+  - config_name: multi_organ
+    data_files:
+      - split: train
+        path: multi_organ/train.parquet
+      - split: validation
+        path: multi_organ/validation.parquet
+      - split: test
+        path: multi_organ/test.parquet
+  - config_name: single_organ_multi_rater
+    data_files:
+      - split: train
+        path: single_organ_multi_rater/train.parquet
+      - split: validation
+        path: single_organ_multi_rater/validation.parquet
+      - split: test
+        path: single_organ_multi_rater/test.parquet
+  - config_name: multi_organ_multi_rater
+    data_files:
+      - split: train
+        path: multi_organ_multi_rater/train.parquet
+      - split: validation
+        path: multi_organ_multi_rater/validation.parquet
+      - split: test
+        path: multi_organ_multi_rater/test.parquet
+---
+
+![DSAD Banner](banner.gif)
+
+<h1 align="center">Dresden Surgical Anatomy Dataset (DSAD)</h1>
+
+<p align="center">
+  <a href="https://doi.org/10.1038/s41597-022-01719-2"><img src="https://img.shields.io/badge/DOI-10.1038%2Fs41597--022--01719--2-blue" alt="Paper DOI"/></a>
+  <a href="https://gitlab.com/nct_tso_public/dsad"><img src="https://img.shields.io/badge/GitLab-Dataset_Code-orange?logo=gitlab" alt="Dataset Code"/></a>
+  <a href="https://gitlab.com/nct_tso_public/anatomy-recognition-dsad"><img src="https://img.shields.io/badge/GitLab-Baseline_Code-orange?logo=gitlab" alt="Baseline Code"/></a>
+</p>
+
+The **Dresden Surgical Anatomy Dataset** provides semantic segmentation masks for
+eleven abdominal anatomical structures in laparoscopic images from robot-assisted
+rectal resections: abdominal wall, colon, inferior mesenteric artery, intestinal
+veins, liver, pancreas, small intestine, spleen, stomach, ureter, and vesicular
+glands. It contains 13,195 images from 32 surgeries, each annotated by three
+independent annotators and reviewed by an expert physician. Every image also
+includes weak presence labels indicating which structures are visible in the
+scene (12 labels — the 11 segmented organs plus uterus).
+
+This is the official HuggingFace mirror of the dataset published in
+[Nature Scientific Data](https://doi.org/10.1038/s41597-022-01719-2) (Carstens
+et al., 2023). The `single_organ` and `multi_organ` configs contain the final
+expert-reviewed masks. Per-annotator masks and STAPLE-merged masks are available
+in separate configs (`single_organ_multi_rater`, `multi_organ_multi_rater`) for
+inter-rater agreement analysis.
+
+## Quick start
+
+```python
+from datasets import load_dataset
+
+# Load single-organ segmentation (default)
+ds = load_dataset("nct-tso/dsad")
+
+# Load multi-organ segmentation
+ds_multi = load_dataset("nct-tso/dsad", "multi_organ")
+
+# Load only a specific organ (downloads only matching rows)
+ds_liver = load_dataset("nct-tso/dsad", split="train", filters=[("organ", "==", "liver")])
+
+# Load per-annotator masks for inter-rater analysis
+ds_raters = load_dataset("nct-tso/dsad", "single_organ_multi_rater")
+
+# Stream without downloading
+ds_stream = load_dataset("nct-tso/dsad", split="train", streaming=True)
+```
+
+## `single_organ` (default)
+
+13,195 images across 11 anatomical structures. Each row has one image and one
+binary segmentation mask for a single organ, plus weak presence labels for the
+11 segmented organs + uterus.
+
+| Column | Type | Description |
+|---|---|---|
+| `surgery_id` | string | Surgery identifier (01–32) |
+| `frame_id` | string | Frame number within the surgery |
+| `organ` | string | Segmented organ |
+| `image` | Image | Laparoscopic frame (1280x1024 RGB) |
+| `mask` | Image | Binary segmentation mask (0=background, 255=organ) |
+| `abdominal_wall` ... `vesicular_glands` | bool | Weak presence labels indicating which structures are visible in the frame (12 columns — 11 organs + uterus) |
+
+## `multi_organ`
+
+1,430 images from the stomach subset, with segmentation masks for up to 7 organs
+per image. These images were selected because stomach views frequently show
+multiple anatomical structures. Not all organs are visible in every frame —
+masks for non-visible organs are empty. Masks may overlap.
+
+| Column | Type | Description |
+|---|---|---|
+| `surgery_id` | string | Surgery identifier |
+| `frame_id` | string | Frame number within the surgery |
+| `image` | Image | Laparoscopic frame (1280x1024 RGB) |
+| `mask_abdominal_wall` | Image | Binary mask for abdominal wall |
+| `mask_colon` | Image | Binary mask for colon |
+| `mask_liver` | Image | Binary mask for liver |
+| `mask_pancreas` | Image | Binary mask for pancreas |
+| `mask_small_intestine` | Image | Binary mask for small intestine |
+| `mask_spleen` | Image | Binary mask for spleen |
+| `mask_stomach` | Image | Binary mask for stomach |
+
+## `single_organ_multi_rater`
+
+Per-annotator masks and STAPLE-merged masks for inter-rater agreement analysis.
+Same rows as `single_organ` - join on `surgery_id` + `frame_id` + `organ`.
+Does not include images to avoid duplication.
+
+| Column | Type | Description |
+|---|---|---|
+| `surgery_id` | string | Surgery identifier |
+| `frame_id` | string | Frame number within the surgery |
+| `organ` | string | Segmented organ |
+| `mask_anno_1` | Image | Annotator 1 mask |
+| `mask_anno_2` | Image | Annotator 2 mask |
+| `mask_anno_3` | Image | Annotator 3 mask |
+| `mask_staple` | Image | STAPLE-merged mask (before expert review) |
+
+Rows are aligned with `single_organ` — combine by concatenating columns:
+
+```python
+from datasets import load_dataset, concatenate_datasets
+
+ds = load_dataset("nct-tso/dsad", split="train")
+ds_raters = load_dataset("nct-tso/dsad", "single_organ_multi_rater", split="train")
+ds_raters = ds_raters.remove_columns(["surgery_id", "frame_id", "organ"])
+combined = concatenate_datasets([ds, ds_raters], axis=1)
+```
+
+## `multi_organ_multi_rater`
+
+Per-annotator masks and STAPLE-merged masks for the multi-organ subset.
+Same rows as `multi_organ` - join on `surgery_id` + `frame_id`.
+Does not include images to avoid duplication.
+
+| Column | Type | Description |
+|---|---|---|
+| `surgery_id` | string | Surgery identifier |
+| `frame_id` | string | Frame number within the surgery |
+| `mask_anno_{1,2,3}_{organ}` | Image | Per-annotator masks (3 × 7 = 21 columns) |
+| `mask_staple_{organ}` | Image | STAPLE-merged masks (7 columns) |
+
+Where `{organ}` is one of: `abdominal_wall`, `colon`, `liver`, `pancreas`,
+`small_intestine`, `spleen`, `stomach`.
+
+## Splits
+
+Split by surgery ID following
+[Kolbinger et al. 2024](https://doi.org/10.1097/JS9.0000000000000595) to
+prevent patient leakage:
+
+| Split | Surgery IDs | single_organ | multi_organ |
+|---|---|---|---|
+| train | 01, 04–06, 08–10, 12, 15–17, 19, 22–25, 27–31 | 7,889 | 863 |
+| validation | 03, 21, 26 | 1,978 | 202 |
+| test | 02, 07, 11, 13–14, 18, 20, 32 | 3,328 | 365 |
+
+## Citation
+
+```bibtex
+@article{carstens2023dresden,
+  title={The dresden surgical anatomy dataset for abdominal organ segmentation in surgical data science},
+  author={Carstens, Matthias and Rinner, Franziska M and Bodenstedt, Sebastian and Jenke, Alexander C and Weitz, J{\"u}rgen and Distler, Marius and Speidel, Stefanie and Kolbinger, Fiona R},
+  journal={Scientific Data},
+  volume={10},
+  number={1},
+  pages={3},
+  year={2023},
+  publisher={Nature Publishing Group UK London}
+}
+```

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