ArticleScientific data2023
The Dresden Surgical Anatomy Dataset for Abdominal Organ Segmentation in Surgical Data Science.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 57 citations in OpenAlex.
- [Application of artificial intelligence for surgical skill assessment and quality control in minimally invasive surgery: progress, problems and prospect].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Review
- HFSUNet: A UNet architecture optimized for precise segmentation of pneumonia lesion boundaries.iScience · 2026Article
- A High-Quality Endoscopic Image Dataset with Annotated Recurrent Laryngeal Nerve for AI-Assisted Thyroid Surgery.Scientific data · 2026Article
- Interdisciplinary Dialogues on Surgical Data Science: Revising Its Benefits for Surgical Stakeholders and Patients.IEEE transactions on medical robotics and bionics · 2026Article
- Label tree semantic losses for rich multi-class medical image segmentation.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence for surgical scene understanding: a systematic review and reporting quality meta-analysis.NPJ digital medicine · 2025Article
- AUGUR-AI: a real-time artificial intelligence interpreter for indocyanine green fluorescence angiography in colorectal surgery.Surgical endoscopy · 2025Article
- Artificial Intelligence in Gastrointestinal Surgery: A Systematic Review of Its Role in Laparoscopic and Robotic Surgery.Journal of personalized medicine · 2025Review
- [Translational challenges and clinical potential of artificial intelligence in minimally invasive surgery].Chirurgie (Heidelberg, Germany) · 2025Article
- A fine-tuned foundational model SurgiSAM2 for surgical video anatomy segmentation and detection.Scientific reports · 2025Article
- Automated multimodel segmentation and tracking for AR-guided open liver surgery using scene-aware self-prompting.International journal of computer assisted radiology and surgery · 2025Article
- Diffusion-driven distillation and contrastive learning for class-incremental semantic segmentation of laparoscopic images.International journal of computer assisted radiology and surgery · 2025Article
- Adaptive sensitivity-fisher regularization for heterogeneous transfer learning of vascular segmentation in laparoscopic videos.International journal of computer assisted radiology and surgery · 2025Article
- Deep learning-based intraoperative visual guidance model for ureter identification in laparoscopic sigmoidectomy.Surgical endoscopy · 2025Article
- Dynamic key vascular anatomy dataset for D2 lymph node dissection during laparoscopic gastric cancer surgery.Scientific data · 2025Article
- Object Detection in Laparoscopic Surgery: A Comparative Study of Deep Learning Models on a Custom Endometriosis Dataset.Diagnostics (Basel, Switzerland) · 2025Article
- Semantic segmentation dataset authoring with simplified labels.International journal of computer assisted radiology and surgery · 2025Article
- Automated assessment of simulated laparoscopic surgical skill performance using deep learning.Scientific reports · 2025Article
- LapEx: A new multimodal dataset for context recognition and practice assessment in laparoscopic surgery.Scientific data · 2025Article
- Artificial intelligence assisted real-time recognition of intra-abdominal metastasis during laparoscopic gastric cancer surgery.NPJ digital medicine · 2025Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Laparoscopy is an imaging technique that enables minimally-invasive procedures in various medical disciplines including abdominal surgery, gynaecology and urology. To date, publicly available laparoscopic image datasets are mostly limited to general classifications of data, semantic segmentations of surgical instruments and low-volume weak annotations of specific abdominal organs. The Dresden Surgical Anatomy Dataset provides semantic segmentations of eight abdominal organs (colon, liver, pancreas, small intestine, spleen, stomach, ureter, vesicular glands), the abdominal wall and two vessel structures (inferior mesenteric artery, intestinal veins) in laparoscopic view. In total, this dataset comprises 13195 laparoscopic images. For each anatomical structure, we provide over a thousand images with pixel-wise segmentations. Annotations comprise semantic segmentations of single organs and one multi-organ-segmentation dataset including segments for all eleven anatomical structures. Moreover, we provide weak annotations of organ presence for every single image. This dataset markedly expands the horizon for surgical data science applications of computer vision in laparoscopic surgery and could thereby contribute to a reduction of risks and faster translation of Artificial Intelligence into surgical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.