Benchmarking and Enhancing Surgical Phase Recognition Models for Robotic-Assisted Esophagectomy
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915049450242048 |
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| author | Li, Yiping van Jaarsveld, Romy de Jong, Ronald Bongers, Jasper Kuiper, Gino van Hillegersberg, Richard Ruurda, Jelle Breeuwer, Marcel Khalil, Yasmina Al |
| author_facet | Li, Yiping van Jaarsveld, Romy de Jong, Ronald Bongers, Jasper Kuiper, Gino van Hillegersberg, Richard Ruurda, Jelle Breeuwer, Marcel Khalil, Yasmina Al |
| contents | Robotic-assisted minimally invasive esophagectomy (RAMIE) is a recognized treatment for esophageal cancer, offering better patient outcomes compared to open surgery and traditional minimally invasive surgery. RAMIE is highly complex, spanning multiple anatomical areas and involving repetitive phases and non-sequential phase transitions. Our goal is to leverage deep learning for surgical phase recognition in RAMIE to provide intraoperative support to surgeons. To achieve this, we have developed a new surgical phase recognition dataset comprising 27 videos. Using this dataset, we conducted a comparative analysis of state-of-the-art surgical phase recognition models. To more effectively capture the temporal dynamics of this complex procedure, we developed a novel deep learning model featuring an encoder-decoder structure with causal hierarchical attention, which demonstrates superior performance compared to existing models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04039 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Benchmarking and Enhancing Surgical Phase Recognition Models for Robotic-Assisted Esophagectomy Li, Yiping van Jaarsveld, Romy de Jong, Ronald Bongers, Jasper Kuiper, Gino van Hillegersberg, Richard Ruurda, Jelle Breeuwer, Marcel Khalil, Yasmina Al Computer Vision and Pattern Recognition Robotic-assisted minimally invasive esophagectomy (RAMIE) is a recognized treatment for esophageal cancer, offering better patient outcomes compared to open surgery and traditional minimally invasive surgery. RAMIE is highly complex, spanning multiple anatomical areas and involving repetitive phases and non-sequential phase transitions. Our goal is to leverage deep learning for surgical phase recognition in RAMIE to provide intraoperative support to surgeons. To achieve this, we have developed a new surgical phase recognition dataset comprising 27 videos. Using this dataset, we conducted a comparative analysis of state-of-the-art surgical phase recognition models. To more effectively capture the temporal dynamics of this complex procedure, we developed a novel deep learning model featuring an encoder-decoder structure with causal hierarchical attention, which demonstrates superior performance compared to existing models. |
| title | Benchmarking and Enhancing Surgical Phase Recognition Models for Robotic-Assisted Esophagectomy |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.04039 |