Semi-supervised Learning for Instrument Detection with a Class Imbalanced Dataset

Jihun Yoon, Jiwon Lee, Sung Hyun Park, Woo Jin Hyung, Min Kook Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationInterpretable 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
EditorsJaime Cardoso, Wilson Silva, Ricardo Cruz, Hien Van Nguyen, Badri Roysam, Nicholas Heller, Pedro Henriques Abreu, Jose Pereira Amorim, Ivana Isgum, Vishal Patel, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Samaneh Abbasi, Diana Mateus, Emanuele Trucco
PublisherSpringer Science and Business Media Deutschland GmbH
Pages266-276
Number of pages11
ISBN (Print)9783030611651
DOIs
Publication statusPublished - 2020
Event3rd 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 - Lima, Peru
Duration: 2020 Oct 42020 Oct 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12446 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd 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
CountryPeru
CityLima
Period20/10/420/10/8

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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