TY - GEN
T1 - Semi-supervised Learning for Instrument Detection with a Class Imbalanced Dataset
AU - Yoon, Jihun
AU - Lee, Jiwon
AU - Park, Sung Hyun
AU - Hyung, Woo Jin
AU - Choi, Min Kook
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - The automated recognition of surgical instruments in surgical videos is an essential factor for the evaluation and analysis of surgery. The analysis of surgical instrument localization information can help in analyses related to surgical evaluation and decision making during surgery. To solve the problem of the localization of surgical instruments, we used an object detector with bounding box labels to train the localization of the surgical tools shown in a surgical video. In this study, we propose a semi-supervised learning-based training method to solve the class imbalance between surgical instruments, which makes it challenging to train the detectors of the surgical instruments. First, we labeled gastrectomy videos for gastric cancer performed in 24 cases of robotic surgery to detect the initial bounding box of the surgical instruments. Next, a trained instrument detector was used to discern the unlabeled videos, and new labels were added to the tools causing class imbalance based on the previously acquired statistics of the labeled videos. We also performed object tracking-based label generation in the spatio-temporal domain to obtain accurate label information from the unlabeled videos in an automated manner. We were able to generate dense labels for the surgical instruments lacking labels through bidirectional object tracking using a single object tracker; thus, we achieved improved instrument detection in a fully or semi-automated manner.
AB - The automated recognition of surgical instruments in surgical videos is an essential factor for the evaluation and analysis of surgery. The analysis of surgical instrument localization information can help in analyses related to surgical evaluation and decision making during surgery. To solve the problem of the localization of surgical instruments, we used an object detector with bounding box labels to train the localization of the surgical tools shown in a surgical video. In this study, we propose a semi-supervised learning-based training method to solve the class imbalance between surgical instruments, which makes it challenging to train the detectors of the surgical instruments. First, we labeled gastrectomy videos for gastric cancer performed in 24 cases of robotic surgery to detect the initial bounding box of the surgical instruments. Next, a trained instrument detector was used to discern the unlabeled videos, and new labels were added to the tools causing class imbalance based on the previously acquired statistics of the labeled videos. We also performed object tracking-based label generation in the spatio-temporal domain to obtain accurate label information from the unlabeled videos in an automated manner. We were able to generate dense labels for the surgical instruments lacking labels through bidirectional object tracking using a single object tracker; thus, we achieved improved instrument detection in a fully or semi-automated manner.
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U2 - 10.1007/978-3-030-61166-8_28
DO - 10.1007/978-3-030-61166-8_28
M3 - Conference contribution
AN - SCOPUS:85092913965
SN - 9783030611651
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 266
EP - 276
BT - Interpretable and Annotation-Efficient Learning for Medical Image Computing - 3rd International Workshop, iMIMIC 2020, 2nd International Workshop, MIL3iD 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Cardoso, Jaime
A2 - Silva, Wilson
A2 - Cruz, Ricardo
A2 - Van Nguyen, Hien
A2 - Roysam, Badri
A2 - Heller, Nicholas
A2 - Henriques Abreu, Pedro
A2 - Pereira Amorim, Jose
A2 - Isgum, Ivana
A2 - Patel, Vishal
A2 - Zhou, Kevin
A2 - Jiang, Steve
A2 - Le, Ngan
A2 - Luu, Khoa
A2 - Sznitman, Raphael
A2 - Cheplygina, Veronika
A2 - Abbasi, Samaneh
A2 - Mateus, Diana
A2 - Trucco, Emanuele
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the 2nd International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
Y2 - 4 October 2020 through 8 October 2020
ER -