Al Hajj, Hassan ; Lamard, Mathieu ; Conze, Pierre-Henri ; Roychowdhury, Soumali ; Hu, Xiaowei ; Maršalkaitė, Gabija ; Zisimopoulos, Odysseas ; Dedmari, Muneer Ahmad ; Zhao, Fenqiang ; Prellberg, Jonas ; Sahu, Manish ; Galdran, Adrian ; Araújo, Teresa ; Vo, Duc My ; Panda, Chandan ; Dahiya, Navdeep ; Kondo, Satoshi ; Bian, Zhengbing ; Vahdat, Arash ; Bialopetravičius, Jonas ; Flouty, Evangello ; Qiu, Chenhui ; Dill, Sabrina ; Mukhopadhyay, Anirban ; Costa, Pedro ; Aresta, Guilherme ; Ramamurthy, Senthil ; Lee, Sang-Woong ; Campilho, Aurélio ; Zachow, Stefan ; Xia, Shunren ; Conjeti, Sailesh ; Stoyanov, Danail ; Armaitis, Jogundas ; Heng, Pheng-Ann ; Macready, William G. ; Cochener, Béatrice ; Quellec, Gwenolé (2019)
CATARACTS: Challenge on automatic tool annotation for cataRACT surgery.
In: Medical Image Analysis, 52
doi: 10.1016/j.media.2018.11.008
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design e_cient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the di_erential analysis of these solutions are discussed. We expect that they will guide the design of e_cient surgery monitoring tools in the near future.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Al Hajj, Hassan ; Lamard, Mathieu ; Conze, Pierre-Henri ; Roychowdhury, Soumali ; Hu, Xiaowei ; Maršalkaitė, Gabija ; Zisimopoulos, Odysseas ; Dedmari, Muneer Ahmad ; Zhao, Fenqiang ; Prellberg, Jonas ; Sahu, Manish ; Galdran, Adrian ; Araújo, Teresa ; Vo, Duc My ; Panda, Chandan ; Dahiya, Navdeep ; Kondo, Satoshi ; Bian, Zhengbing ; Vahdat, Arash ; Bialopetravičius, Jonas ; Flouty, Evangello ; Qiu, Chenhui ; Dill, Sabrina ; Mukhopadhyay, Anirban ; Costa, Pedro ; Aresta, Guilherme ; Ramamurthy, Senthil ; Lee, Sang-Woong ; Campilho, Aurélio ; Zachow, Stefan ; Xia, Shunren ; Conjeti, Sailesh ; Stoyanov, Danail ; Armaitis, Jogundas ; Heng, Pheng-Ann ; Macready, William G. ; Cochener, Béatrice ; Quellec, Gwenolé |
| Art des Eintrags: | Bibliographie |
| Titel: | CATARACTS: Challenge on automatic tool annotation for cataRACT surgery |
| Sprache: | Englisch |
| Publikationsjahr: | Februar 2019 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Medical Image Analysis |
| Jahrgang/Volume einer Zeitschrift: | 52 |
| DOI: | 10.1016/j.media.2018.11.008 |
| URL / URN: | https://doi.org/10.1016/j.media.2018.11.008 |
| Kurzbeschreibung (Abstract): | Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design e_cient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the di_erential analysis of these solutions are discussed. We expect that they will guide the design of e_cient surgery monitoring tools in the near future. |
| Freie Schlagworte: | Video analysis Deep learning |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 09 Apr 2020 13:40 |
| Letzte Änderung: | 09 Apr 2020 13:40 |
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