Abstract #4353

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

Shao Chieh Lin^1,2, Chun-Jung Juan^2,3,4, Ya-Hui Li², Ming-Ting Tsai², Chang-Hsien Liu², Hsu-Hsia Peng⁵, Teng-Yi Huang⁶, Yi-Jui Liu⁷, and Chia-Ching Chang^2,8

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

Synopsis

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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