DeepVinci: Organ and Tool Segmentation with Edge Supervision and a Densely Multi-Scale Pyramid Module for Robot-Assisted Surgery
<b>Background</b>: Automated surgical navigation can be separated into three stages: (1) organ identification and localization, (2) identification of the organs requiring further surgery, and (3) automated planning of the operation path and steps. With its ideal visual and operating syst...
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MDPI AG
2025-07-01
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| author | Li-An Tseng Yuan-Chih Tsai Meng-Yi Bai Mei-Fang Li Yi-Liang Lee Kai-Jo Chiang Yu-Chi Wang Jing-Ming Guo |
| author_facet | Li-An Tseng Yuan-Chih Tsai Meng-Yi Bai Mei-Fang Li Yi-Liang Lee Kai-Jo Chiang Yu-Chi Wang Jing-Ming Guo |
| author_sort | Li-An Tseng |
| collection | DOAJ |
| description | <b>Background</b>: Automated surgical navigation can be separated into three stages: (1) organ identification and localization, (2) identification of the organs requiring further surgery, and (3) automated planning of the operation path and steps. With its ideal visual and operating system, the da Vinci surgical system provides a promising platform for automated surgical navigation. This study focuses on the first step in automated surgical navigation by identifying organs in gynecological surgery. <b>Methods</b>: Due to the difficulty of collecting da Vinci gynecological endoscopy data, we propose DeepVinci, a novel end-to-end high-performance encoder–decoder network based on convolutional neural networks (CNNs) for pixel-level organ semantic segmentation. Specifically, to overcome the drawback of a limited field of view, we incorporate a densely multi-scale pyramid module and feature fusion module, which can also enhance the global context information. In addition, the system integrates an edge supervision network to refine the segmented results on the decoding side. <b>Results</b>: Experimental results show that DeepVinci can achieve state-of-the-art accuracy, obtaining dice similarity coefficient and mean pixel accuracy values of 0.684 and 0.700, respectively. <b>Conclusions</b>: The proposed DeepVinci network presents a practical and competitive semantic segmentation solution for da Vinci gynecological surgery. |
| format | Article |
| id | doaj-art-d602f57ae3e541f3b902a0830d0b0a5e |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-d602f57ae3e541f3b902a0830d0b0a5e2025-08-20T04:00:50ZengMDPI AGDiagnostics2075-44182025-07-011515191710.3390/diagnostics15151917DeepVinci: Organ and Tool Segmentation with Edge Supervision and a Densely Multi-Scale Pyramid Module for Robot-Assisted SurgeryLi-An Tseng0Yuan-Chih Tsai1Meng-Yi Bai2Mei-Fang Li3Yi-Liang Lee4Kai-Jo Chiang5Yu-Chi Wang6Jing-Ming Guo7Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106335, TaiwanGraduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei 106335, TaiwanGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanDepartment of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114202, TaiwanDepartment of Nursing, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, TaiwanDepartment of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114202, TaiwanDepartment of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan<b>Background</b>: Automated surgical navigation can be separated into three stages: (1) organ identification and localization, (2) identification of the organs requiring further surgery, and (3) automated planning of the operation path and steps. With its ideal visual and operating system, the da Vinci surgical system provides a promising platform for automated surgical navigation. This study focuses on the first step in automated surgical navigation by identifying organs in gynecological surgery. <b>Methods</b>: Due to the difficulty of collecting da Vinci gynecological endoscopy data, we propose DeepVinci, a novel end-to-end high-performance encoder–decoder network based on convolutional neural networks (CNNs) for pixel-level organ semantic segmentation. Specifically, to overcome the drawback of a limited field of view, we incorporate a densely multi-scale pyramid module and feature fusion module, which can also enhance the global context information. In addition, the system integrates an edge supervision network to refine the segmented results on the decoding side. <b>Results</b>: Experimental results show that DeepVinci can achieve state-of-the-art accuracy, obtaining dice similarity coefficient and mean pixel accuracy values of 0.684 and 0.700, respectively. <b>Conclusions</b>: The proposed DeepVinci network presents a practical and competitive semantic segmentation solution for da Vinci gynecological surgery.https://www.mdpi.com/2075-4418/15/15/1917artificial intelligenceda Vinci Robotgynecological surgeryorgan semantic segmentationdeep learning |
| spellingShingle | Li-An Tseng Yuan-Chih Tsai Meng-Yi Bai Mei-Fang Li Yi-Liang Lee Kai-Jo Chiang Yu-Chi Wang Jing-Ming Guo DeepVinci: Organ and Tool Segmentation with Edge Supervision and a Densely Multi-Scale Pyramid Module for Robot-Assisted Surgery Diagnostics artificial intelligence da Vinci Robot gynecological surgery organ semantic segmentation deep learning |
| title | DeepVinci: Organ and Tool Segmentation with Edge Supervision and a Densely Multi-Scale Pyramid Module for Robot-Assisted Surgery |
| title_full | DeepVinci: Organ and Tool Segmentation with Edge Supervision and a Densely Multi-Scale Pyramid Module for Robot-Assisted Surgery |
| title_fullStr | DeepVinci: Organ and Tool Segmentation with Edge Supervision and a Densely Multi-Scale Pyramid Module for Robot-Assisted Surgery |
| title_full_unstemmed | DeepVinci: Organ and Tool Segmentation with Edge Supervision and a Densely Multi-Scale Pyramid Module for Robot-Assisted Surgery |
| title_short | DeepVinci: Organ and Tool Segmentation with Edge Supervision and a Densely Multi-Scale Pyramid Module for Robot-Assisted Surgery |
| title_sort | deepvinci organ and tool segmentation with edge supervision and a densely multi scale pyramid module for robot assisted surgery |
| topic | artificial intelligence da Vinci Robot gynecological surgery organ semantic segmentation deep learning |
| url | https://www.mdpi.com/2075-4418/15/15/1917 |
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