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| Format: | Preprint |
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2023
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| Online-Zugang: | https://arxiv.org/abs/2312.12429 |
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| _version_ | 1866915278698315776 |
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| author | Murali, Aditya Alapatt, Deepak Mascagni, Pietro Vardazaryan, Armine Garcia, Alain Okamoto, Nariaki Costamagna, Guido Mutter, Didier Marescaux, Jacques Dallemagne, Bernard Padoy, Nicolas |
| author_facet | Murali, Aditya Alapatt, Deepak Mascagni, Pietro Vardazaryan, Armine Garcia, Alain Okamoto, Nariaki Costamagna, Guido Mutter, Didier Marescaux, Jacques Dallemagne, Bernard Padoy, Nicolas |
| contents | This technical report provides a detailed overview of Endoscapes, a dataset of laparoscopic cholecystectomy (LC) videos with highly intricate annotations targeted at automated assessment of the Critical View of Safety (CVS). Endoscapes comprises 201 LC videos with frames annotated sparsely but regularly with segmentation masks, bounding boxes, and CVS assessment by three different clinical experts. Altogether, there are 11090 frames annotated with CVS and 1933 frames annotated with tool and anatomy bounding boxes from the 201 videos, as well as an additional 422 frames from 50 of the 201 videos annotated with tool and anatomy segmentation masks. In this report, we provide detailed dataset statistics (size, class distribution, dataset splits, etc.) and a comprehensive performance benchmark for instance segmentation, object detection, and CVS prediction. The dataset and model checkpoints are publically available at https://github.com/CAMMA-public/Endoscapes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_12429 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark Murali, Aditya Alapatt, Deepak Mascagni, Pietro Vardazaryan, Armine Garcia, Alain Okamoto, Nariaki Costamagna, Guido Mutter, Didier Marescaux, Jacques Dallemagne, Bernard Padoy, Nicolas Computer Vision and Pattern Recognition This technical report provides a detailed overview of Endoscapes, a dataset of laparoscopic cholecystectomy (LC) videos with highly intricate annotations targeted at automated assessment of the Critical View of Safety (CVS). Endoscapes comprises 201 LC videos with frames annotated sparsely but regularly with segmentation masks, bounding boxes, and CVS assessment by three different clinical experts. Altogether, there are 11090 frames annotated with CVS and 1933 frames annotated with tool and anatomy bounding boxes from the 201 videos, as well as an additional 422 frames from 50 of the 201 videos annotated with tool and anatomy segmentation masks. In this report, we provide detailed dataset statistics (size, class distribution, dataset splits, etc.) and a comprehensive performance benchmark for instance segmentation, object detection, and CVS prediction. The dataset and model checkpoints are publically available at https://github.com/CAMMA-public/Endoscapes. |
| title | The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.12429 |