Towards Better Surgical Instrument Segmentation in Endoscopic Vision: Multi-Angle Feature Aggregation and Contour Supervision
文献类型：期刊论文
| 作者 | Qin, Fangbo1 ; Lin, Shan2; Li, Yangming4; Bly, Randall A.3; Moe, Kris S.3; Hannaford, Blake2
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| 刊名 | IEEE ROBOTICS AND AUTOMATION LETTERS
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| 出版日期 | 2020-10-01 |
| 卷号 | 5期号:4页码:6639-6646 |
| ISSN号 | 2377-3766 |
| 关键词 | Computer vision for medical robotics medical robots and systems deep learning for visual perception object detection segmentation and categorization |
| DOI | 10.1109/LRA.2020.3009073 |
| 通讯作者 | Hannaford, Blake(blake@uw.edu) |
| 英文摘要 | Accurate and real-time surgical instrument segmentation is important in the endoscopic vision of robot-assisted surgery, and significant challenges are posed by frequent instrument-tissue contacts and continuous change of observation perspective. For these challenging tasks more and more deep neural networks (DNN) models are designed in recent years. We are motivated to propose a general embeddable approach to improve these current DNN segmentation models without increasing the model parameter number. Firstly, observing the limited rotation-invariance performance of DNN, we proposed the Multi-Angle Feature Aggregation (MAFA) method, leveraging active image rotation to gain richer visual cues and make the prediction more robust to instrument orientation changes. Secondly, in the end-to-end training stage, the auxiliary contour supervision is utilized to guide the model to learn the boundary awareness, so that the contour shape of segmentation mask is more precise. The proposed method is validated with ablation experiments on the novel Sinus-Surgery datasets collected from surgeons' operations, and is compared to the existing methods on a public dataset collected with a da Vinci Xi Robot. |
| WOS关键词 | SURGERY ; ROBOTICS |
| 资助项目 | National Science Foundation[IIS-1637444] ; National Natural Science Foundation of China[61703398] |
| WOS研究方向 | Robotics |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000564288600005 |
| 资助机构 | National Science Foundation ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/41529]  |
| 专题 | 精密感知与控制研究中心_精密感知与控制 |
| 通讯作者 | Hannaford, Blake |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China 2.Univ Washington UW, Dept Elect Engn, Seattle, WA 98195 USA 3.Univ Washington, Dept Otolaryngol Head & Neck Surg, Seattle, WA 98195 USA 4.Rochester Inst Technol, Dept Elect Comp & Telecommun Engn Technol, Rochester, NY 14623 USA |
| 推荐引用方式 GB/T 7714 | Qin, Fangbo,Lin, Shan,Li, Yangming,et al. Towards Better Surgical Instrument Segmentation in Endoscopic Vision: Multi-Angle Feature Aggregation and Contour Supervision[J]. IEEE ROBOTICS AND AUTOMATION LETTERS,2020,5(4):6639-6646. |
| APA | Qin, Fangbo,Lin, Shan,Li, Yangming,Bly, Randall A.,Moe, Kris S.,&Hannaford, Blake.(2020).Towards Better Surgical Instrument Segmentation in Endoscopic Vision: Multi-Angle Feature Aggregation and Contour Supervision.IEEE ROBOTICS AND AUTOMATION LETTERS,5(4),6639-6646. |
| MLA | Qin, Fangbo,et al."Towards Better Surgical Instrument Segmentation in Endoscopic Vision: Multi-Angle Feature Aggregation and Contour Supervision".IEEE ROBOTICS AND AUTOMATION LETTERS 5.4(2020):6639-6646. |
入库方式： OAI收割
来源：自动化研究所
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