Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge
文献类型：期刊论文
| 作者 | Ross, Tobias23,24; Reinke, Annika23,24; Full, Peter M.22,23; Wagner, Martin21; Kenngott, Hannes21; Apitz, Martin21; Hempe, Hellena24; Mindroc-Filimon, Diana24; Scholz, Patrick20,24; Thuy Nuong Tran24 |
| 刊名 | MEDICAL IMAGE ANALYSIS
 |
| 出版日期 | 2021-05-01 |
| 卷号 | 70页码:26 |
| ISSN号 | 1361-8415 |
| 关键词 | Multi-instance instrument Minimally invasive surgery Robustness and generalization Surgical data science |
| DOI | 10.1016/j.media.2020.101920 |
| 通讯作者 | Ross, Tobias(t.ross@dkfz-heidelberg.de) |
| 英文摘要 | when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization ; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multiinstance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algo Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness , that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization ; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts). (c) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ) |
| 资助项目 | Surgical Oncology Program of the National Center for Tumor Diseases (NCT) Heidelberg ; German Federal Ministry of Economic Affairs and Energy[BMWI 01MT17001C] ; German Federal Ministry of Economic Affairs and Energy[BMWI 01MD15002E] ; UNDERSTAND.AI ; NVIDIA GmbH ; Digital Surgery ; Helmholtz Association under the joint research school HIDSS4Health (Helmholtz Information and Data Science School for Health) ; National Key Research and Development Program of China[2017YFB1302704] ; National Natural Science Fundation of China[81771921] ; National Natural Science Fundation of China[61901084] ; Research Council of Norway[263,248] ; FWF Austrian Science Fund[P 32010-N38] |
| WOS研究方向 | Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000639613600009 |
| 资助机构 | Surgical Oncology Program of the National Center for Tumor Diseases (NCT) Heidelberg ; German Federal Ministry of Economic Affairs and Energy ; UNDERSTAND.AI ; NVIDIA GmbH ; Digital Surgery ; Helmholtz Association under the joint research school HIDSS4Health (Helmholtz Information and Data Science School for Health) ; National Key Research and Development Program of China ; National Natural Science Fundation of China ; Research Council of Norway ; FWF Austrian Science Fund |
| 源URL | [http://ir.ia.ac.cn/handle/173211/44539]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 通讯作者 | Ross, Tobias |
| 作者单位 | 1.Xiamen Univ, Sch Informat, Dept Comp Sci, 422 Siming South Rd, Xiamen 361005, Peoples R China 2.Univ Calabria, Dept Math & Comp Sci, I-87036 Arcavacata Di Rende, Italy 3.German Canc Res Ctr, Div Biostat, Neuenheimer Feld 581, Heidelberg, Germany 4.Klagenfurt Univ, Inst Informat Technol, Univ Str 65-67, A-9020 Klagenfurt, Austria 5.Peking Univ, Inst Digital Media NELVT, 5 Yiheyuan Rd, Beijing 100871, Peoples R China 6.UIT Arctic Univ Norway, Dept Informat, Hansine Hansens Vei 54, N-9037 Tromso, Norway 7.Chinese Univ Hong Kong, Dept Comp Sci & Engn, Ma Liu Shui, Chung Chi Rd, Hong Kong, Peoples R China 8.Oslo Metropolitan Univ OsloMet, Pilestredet 52, N-0167 Oslo, Norway 9.SimulaMet, Pilestredet 52, N-0167 Oslo, Norway 10.Univ Elect Sci & Technol China, Sch Mech & Elect Engn, West Hitech Zone, Shahe Campus 4,North Jianshe Rd, Chengdu 610054, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ross, Tobias,Reinke, Annika,Full, Peter M.,et al. Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge[J]. MEDICAL IMAGE ANALYSIS,2021,70:26. |
| APA | Ross, Tobias.,Reinke, Annika.,Full, Peter M..,Wagner, Martin.,Kenngott, Hannes.,...&Maier-Hein, Lena.(2021).Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge.MEDICAL IMAGE ANALYSIS,70,26. |
| MLA | Ross, Tobias,et al."Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge".MEDICAL IMAGE ANALYSIS 70(2021):26. |
入库方式： OAI收割
来源：自动化研究所
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