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Abstract #4353

Better Inter-observer agreement for Stroke Segmentation on DWI in Deep Learning Models than Human Experts

Shao Chieh Lin1,2, Chun-Jung Juan2,3,4, Ya-Hui Li2, Ming-Ting Tsai2, Chang-Hsien Liu2, Hsu-Hsia Peng5, Teng-Yi Huang6, Yi-Jui Liu7, and Chia-Ching Chang2,8
1Ph.D. program in Electrical and Communication Engineering, Feng Chia University, Taichung, Taiwan, 2Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, Taiwan, 3Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan, 4Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, 5Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, 6Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, 7Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan, 8Department of Management Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan


Inter-observer agreement is commonly used to evaluate the consistency of clinical diagnosis for two or more doctors. However, it is seldom to use to evaluate the consistency of clinical diagnosis for two or more deep learning models. In this study, four deep learning models for segmentation of stroke lesion were trained using GTs defined by two neuroradiologists with two ADC thresholds. We found the addition of an ADC threshold (0.6 × 10-3 mm2/s) helps eliminate inter-observer variation and achieve best segmentation performance. The inter-observer in two deep learning models shows the more consistent degree compared with inter-observer in two neuroradiologists.

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