Choi J, Cho S, Chung JW, Kim N. Video recognition of
simple mastoidectomy using convolutional neural networks: Detection and segmentation of surgical tools and anatomical regions.
Comput Methods Programs Biomed 2021;
208:106251. [PMID:
34271262 DOI:
10.1016/j.cmpb.2021.106251]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 06/20/2021] [Indexed: 06/13/2023]
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.
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