Dresden Surgical Anatomy Dataset (DSAD)

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 (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

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:

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 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

@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}
}