書誌事項
- タイトル別名
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- Tracking of surgical forceps using YOLACT++
抄録
<p>Forceps tracking in laparoscopic surgery contributes to improved surgical outcomes. We identified forceps by deep learning. Since it is important to identify forceps in real-time, we selected YOLACT++ for fast and accurate segmentation and verified whether the detection speed can be maintained in the video. We annotated a total of 2537 images combining multiple datasets including various surgical environments and divided them into training, validation, and test data at a ratio of approximately 8:1:1. The training was conducted with a batch size of 32, iterations of 100106, and epochs of 1588, and the results showed that the forceps identification speed was 25.79 fps and accuracy was 84.31 %. The results of the test using the trained model with this hyperparameter showed that the forceps identification speed was 28.01 fps and accuracy was 71.42 % for images, and the forceps identification speed was 17.70 fps for the video.</p>
収録刊行物
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- ロボティクス・メカトロニクス講演会講演概要集
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ロボティクス・メカトロニクス講演会講演概要集 2023 (0), 2P1-B23-, 2023
一般社団法人 日本機械学会
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キーワード
詳細情報 詳細情報について
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- CRID
- 1390017444754838528
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- ISSN
- 24243124
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- Crossref
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- 抄録ライセンスフラグ
- 使用不可