CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery
Abstract In laparoscopic and robotic surgery, precise tool instance segmentation is an essential technology for advanced computer-assisted interventions. Although publicly available procedures of routine surgeries exist, they often lack comprehensive annotations for tool instance segmentation. Addit...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05163-w |
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| _version_ | 1849731974935085056 |
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| author | Oluwatosin Alabi Ko Ko Zayar Toe Zijian Zhou Charlie Budd Nicholas Raison Miaojing Shi Tom Vercauteren |
| author_facet | Oluwatosin Alabi Ko Ko Zayar Toe Zijian Zhou Charlie Budd Nicholas Raison Miaojing Shi Tom Vercauteren |
| author_sort | Oluwatosin Alabi |
| collection | DOAJ |
| description | Abstract In laparoscopic and robotic surgery, precise tool instance segmentation is an essential technology for advanced computer-assisted interventions. Although publicly available procedures of routine surgeries exist, they often lack comprehensive annotations for tool instance segmentation. Additionally, the majority of standard datasets for tool segmentation are derived from porcine(pig) surgeries. To address this gap, we introduce CholecInstanceSeg, the largest open-access tool instance segmentation dataset to date. Derived from the existing CholecT50 and Cholec80 datasets, CholecInstanceSeg provides novel annotations for laparoscopic cholecystectomy procedures in patients. Our dataset comprises 41.9k annotated frames extracted from 85 clinical procedures and 64.4k tool instances, each labelled with semantic masks and instance IDs. To ensure the reliability of our annotations, we perform extensive quality control, conduct label agreement statistics, and benchmark the segmentation results with various instance segmentation baselines. CholecInstanceSeg aims to advance the field by offering a comprehensive and high-quality open-access dataset for the development and evaluation of tool instance segmentation algorithms. |
| format | Article |
| id | doaj-art-fe782bbccb7d4643bd5ee7c4e5800afc |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-fe782bbccb7d4643bd5ee7c4e5800afc2025-08-20T03:08:22ZengNature PortfolioScientific Data2052-44632025-05-0112111210.1038/s41597-025-05163-wCholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic SurgeryOluwatosin Alabi0Ko Ko Zayar Toe1Zijian Zhou2Charlie Budd3Nicholas Raison4Miaojing Shi5Tom Vercauteren6Kings College London, Surgical & Interventional EngineeringKings College Hospital Denmark Hill, departmentDepartment of Informatics, King’s College LondonKings College London, Surgical & Interventional EngineeringKings College London, Surgical & Interventional EngineeringTongji University, College of Electronic and Information EngineeringKings College London, Surgical & Interventional EngineeringAbstract In laparoscopic and robotic surgery, precise tool instance segmentation is an essential technology for advanced computer-assisted interventions. Although publicly available procedures of routine surgeries exist, they often lack comprehensive annotations for tool instance segmentation. Additionally, the majority of standard datasets for tool segmentation are derived from porcine(pig) surgeries. To address this gap, we introduce CholecInstanceSeg, the largest open-access tool instance segmentation dataset to date. Derived from the existing CholecT50 and Cholec80 datasets, CholecInstanceSeg provides novel annotations for laparoscopic cholecystectomy procedures in patients. Our dataset comprises 41.9k annotated frames extracted from 85 clinical procedures and 64.4k tool instances, each labelled with semantic masks and instance IDs. To ensure the reliability of our annotations, we perform extensive quality control, conduct label agreement statistics, and benchmark the segmentation results with various instance segmentation baselines. CholecInstanceSeg aims to advance the field by offering a comprehensive and high-quality open-access dataset for the development and evaluation of tool instance segmentation algorithms.https://doi.org/10.1038/s41597-025-05163-w |
| spellingShingle | Oluwatosin Alabi Ko Ko Zayar Toe Zijian Zhou Charlie Budd Nicholas Raison Miaojing Shi Tom Vercauteren CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery Scientific Data |
| title | CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery |
| title_full | CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery |
| title_fullStr | CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery |
| title_full_unstemmed | CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery |
| title_short | CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery |
| title_sort | cholecinstanceseg a tool instance segmentation dataset for laparoscopic surgery |
| url | https://doi.org/10.1038/s41597-025-05163-w |
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