Combine unlabeled with labeled MR images to measure acute ischemic stroke lesion by stepwise learning. Issue 14 (19th August 2022) - Zhao, Bin; Liu, Zhiyang; Liu, Guohua; Wu, Mengran; Cao, Chen; Jin, Song; Wu, Hong; Ding, Shuxue
Abstract: Acute ischemic stroke is a common threat to human health and may obtain timely treatment by fast localizing and quantitatively evaluating the lesions. Most CNN‐based methods try to segment and measure the lesions, however, they require a training on a large number of labeled subjects that are labor‐intensive and time‐consuming to obtain. In this paper, a method is proposed that can combine limited labeled subjects with abundant unlabeled subjects to alleviate the problem. The proposed method consists of two stages: stepwise learning process and segmentation process. Stepwise learning is used to obtain the pretrained encoder. The pretrained encoder and the proposed decoder are connected into a new end‐to‐end segmentation network, which is retrained on the labeled subjects in the segmentation process. By using 5 labeled subjects and 79 unlabeled subjects, the proposed method achieves a mean dice coefficient of 0.663 ± ± 0.205, a mean average symmetric surface distance (ASSD) of 2.17 mm and a mean 95 percentile Hausdorff distance (HD) of 18.38 mm on a clinical MR dataset with 179 subjects. More importantly, it achieves lesion‐wise F1 score of 0.857 and a subject‐wise detection rate of 0.966.