Automatic Acute Ischemic Stroke Lesion Segmentation Using Semi-supervised Learning

Authors

Bin Zhao¹, Shuxue Ding^{1, 2,} , Hong Wu¹, Guohua Liu¹, Chen Cao³, Song Jin³, Zhiyang Liu^{1, *}

¹Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300350, China

²School of Artificial Intelligence, Guilin University of Electronic Technology, Guangxi, 541004, China

³Key Laboratory for Cerebral Artery and Neural Degeneration of Tianjin, Department of Medical Imaging, Tianjin Huanhu Hospital, Tianjin, 300350, China

^*Corresponding author. Email: liuzhiyang@nankai.edu.cn

DOI

10.2991/ijcis.d.210205.001How to use a DOI?

Keywords

Semi-supervised learning; Acute ischemic stroke lesion segmentation; Convolutional neural network (CNN); K-Means; Region growing

Abstract

Ischemic stroke has been a common disease in the elderly population, which can cause long-term disability and even death. However, the time window for treatment of ischemic stroke in its acute stage is very short. To fast localize and quantitively evaluate the acute ischemic stroke (AIS) lesions, many deep-learning-based lesion segmentation methods have been proposed in the literature, where a deep convolutional neural network (CNN) was trained on hundreds of fully-labeled subjects with accurate annotations of AIS lesions. Such methods, however, require a large number of subjects with pixel-by-pixel labels, making it very time-consuming in data collection and annotation. Therefore, in this paper, we propose to use a large number of weakly-labeled subjects with easy-obtained slice-level labels and a few fully-labeled ones with pixel-level annotations, and propose a semi-supervised learning method. In particular, a double-path classification network (DPC-Net) was proposed and trained using the weakly-labeled subjects to detect the suspicious AIS lesions. A K-means algorithm was used on the diffusion -weighted images (DWIs) to identify the potential AIS lesions due to the a priori knowledge that the AIS lesions appear as hyperintense. Finally, a region-growing algorithm combines the outputs of the DPC-Net and the K-means to obtain the precise lesion segmentation. By using 460 weakly-labeled subjects and 5 fully-labeled subjects to train and fine-tune the proposed method, our proposed method achieves a mean dice coefficient of 0.642, and a lesion-wise F1 score of 0.822 on a clinical dataset with 150 subjects.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Bin Zhao
AU  - Shuxue Ding
AU  - Hong Wu
AU  - Guohua Liu
AU  - Chen Cao
AU  - Song Jin
AU  - Zhiyang Liu
PY  - 2021
DA  - 2021/02/10
TI  - Automatic Acute Ischemic Stroke Lesion Segmentation Using Semi-supervised Learning
JO  - International Journal of Computational Intelligence Systems
SP  - 723
EP  - 733
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210205.001
DO  - 10.2991/ijcis.d.210205.001
ID  - Zhao2021
ER  -