TY - GEN
T1 - Uncertainty-aware Mean Teacher Framework with Inception and Squeeze-and-Excitation Block for MICCAI FLARE22 Challenge
AU - Meng, Hui
AU - Zhao, Haochen
AU - Yu, Ziniu
AU - Li, Qingfeng
AU - Niu, Jianwei
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Semi-supervised learning has attracted extensive attention in the field of medical image analysis. However, as a fundamental task, semi-supervised segmentation has not been investigated sufficiently in the field of multi-organ segmentation from abdominal CT. Therefore, we propose a novel uncertainty-aware mean teacher framework with inception and squeeze-and-excitation block (UMT-ISE). Specifically, the UMT-ISE consists of a teacher model and a student model, in which the student model learns from the teacher model by minimizing segmentation loss and consistency loss. Additionaly, we adopt an uncertainty-aware algorithm to make the student model learn accurate and reliable targets by making full use of uncertainty information. To capture multi-scale features, the inception and squeeze-and-excitation block are incoporated into the UMT-ISE. It is worth noting that abdominal CT of test cases are first extracted before multi-organ segmentation in the inference phase, which significantly improves segmentation accuracy. We implement experiments on the FLARE22 challenge. Our method achieves mean DSC of 0.7465 on 13 abdominal organ segmentation tasks.
AB - Semi-supervised learning has attracted extensive attention in the field of medical image analysis. However, as a fundamental task, semi-supervised segmentation has not been investigated sufficiently in the field of multi-organ segmentation from abdominal CT. Therefore, we propose a novel uncertainty-aware mean teacher framework with inception and squeeze-and-excitation block (UMT-ISE). Specifically, the UMT-ISE consists of a teacher model and a student model, in which the student model learns from the teacher model by minimizing segmentation loss and consistency loss. Additionaly, we adopt an uncertainty-aware algorithm to make the student model learn accurate and reliable targets by making full use of uncertainty information. To capture multi-scale features, the inception and squeeze-and-excitation block are incoporated into the UMT-ISE. It is worth noting that abdominal CT of test cases are first extracted before multi-organ segmentation in the inference phase, which significantly improves segmentation accuracy. We implement experiments on the FLARE22 challenge. Our method achieves mean DSC of 0.7465 on 13 abdominal organ segmentation tasks.
KW - Multi-organ segmentation
KW - Multi-scale features
KW - Semi-supervised learning
KW - Uncertainty estimation
UR - https://www.scopus.com/pages/publications/85149705689
U2 - 10.1007/978-3-031-23911-3_22
DO - 10.1007/978-3-031-23911-3_22
M3 - 会议稿件
AN - SCOPUS:85149705689
SN - 9783031239106
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 245
EP - 259
BT - Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation - MICCAI 2022 Challenge, FLARE 2022, Held in Conjunction with MICCAI 2022, Proceedings
A2 - Ma, Jun
A2 - Wang, Bo
PB - Springer Science and Business Media Deutschland GmbH
T2 - International challenge on Fast and Lowresource Semi-supervised Abdominal Organ Segmentation in CT Scans, FLARE 2022 held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Y2 - 22 September 2022 through 22 September 2022
ER -