Image Synthesis-Based Late Stage Cancer Augmentation and Semi-supervised Segmentation for MRI Rectal Cancer Staging

Saeko Sasuga*, Akira Kudo, Yoshiro Kitamura, Satoshi Iizuka, Edgar Simo-Serra, Atsushi Hamabe, Masayuki Ishii, Ichiro Takemasa

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Rectal cancer is one of the most common diseases and a major cause of mortality. For deciding rectal cancer treatment plans, T-staging is important. However, evaluating the index from preoperative MRI images requires high radiologists’ skill and experience. Therefore, the aim of this study is to segment the mesorectum, rectum, and rectal cancer region so that the system can predict T-stage from segmentation results. Generally, shortage of large and diverse dataset and high quality annotation are known to be the bottlenecks in computer aided diagnostics development. Regarding rectal cancer, advanced cancer images are very rare, and per-pixel annotation requires high radiologists’ skill and time. Therefore, it is not feasible to collect comprehensive disease patterns in a training dataset. To tackle this, we propose two kinds of approaches of image synthesis-based late stage cancer augmentation and semi-supervised learning which is designed for T-stage prediction. In the image synthesis data augmentation approach, we generated advanced cancer images from labels. The real cancer labels were deformed to resemble advanced cancer labels by artificial cancer progress simulation. Next, we introduce a T-staging loss which enables us to train segmentation models from per-image T-stage labels. The loss works to keep inclusion/invasion relationships between rectum and cancer region consistent to the ground truth T-stage. The verification tests show that the proposed method obtains the best sensitivity (0.76) and specificity (0.80) in distinguishing between over T3 stage and underT2. In the ablation studies, our semi-supervised learning approach with the T-staging loss improved specificity by 0.13. Adding the image synthesis-based data augmentation improved the DICE score of invasion cancer area by 0.08 from baseline. We expect that this rectal cancer staging AI can help doctors to diagnose cancer staging accurately.

本文言語English
ホスト出版物のタイトルData Augmentation, Labelling, and Imperfections - 2nd MICCAI Workshop, DALI 2022, Held in Conjunction with MICCAI 2022, Proceedings
編集者Hien V. Nguyen, Sharon X. Huang, Yuan Xue
出版社Springer Science and Business Media Deutschland GmbH
ページ1-10
ページ数10
ISBN(印刷版)9783031170263
DOI
出版ステータスPublished - 2022
イベント2nd MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Singapore, Singapore
継続期間: 2022 9月 222022 9月 22

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13567 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference2nd MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
国/地域Singapore
CitySingapore
Period22/9/2222/9/22

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータサイエンス一般

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