Video recognition of simple mastoidectomy using convolutional neural networks: Detection and segmentation of surgical tools and anatomical regions. (September 2021)

• Record Type: Journal Article
• Authors: Choi, Joonmyeong; Cho, Sungman; Chung, Jong Woo; Kim, Namkug
• Is Part Of: Computer methods and programs in biomedicine. Volume 208(2021)
• Publication Date: 2021-09
• Subjects: Anatomical region -- Object detection -- Semantic segmentation -- Simple mastoidectomy -- Surgical tool; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106251
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract: A simple mastoidectomy is used to remove inflammation of the mastoid cavity and to create a route to the skull base and middle ear. However, due to the complexity and difficulty of the simple mastoidectomy, implementing robot vision for assisted surgery is a challenge. To overcome this issue using a convolutional neural network architecture in a surgical environment, each surgical instrument and anatomical region must be distinguishable in real time. To meet this condition, we used the latest instance segmentation architecture, YOLACT. In this study, a data set comprising 5, 319 extracted frames from 70 simple mastoidectomy surgery videos were used. Six surgical tools and five anatomic regions were identified for the training. The YOLACT-based model in the surgical environment was trained and evaluated for real-time object detection and semantic segmentation. Detection accuracies of surgical tools and anatomic regions were 91.2% and 56.5% in mean average precision, respectively. Additionally, the dice similarity coefficient metric for segmentation of the five anatomic regions was 48.2%. The mean frames per second of this model was 32.3, which is sufficient for real-time robotic applications.