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Abstract #4131

ADC and Size Dependent Segmentation Performance using Deep Learning

Chun-Jung Juan1, Yi-Jui Liu2, Shao-Chieh Lin3, and Yi-Hung Jeng4
1Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, Taiwan, 2Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan, 3Ph.D. program in Electrical and Communication Engineering, Feng Chia University, Taichung, Taiwan, 4Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, Hsinchu, Taiwan

Accurate automatic segmentation of acute ischemic infarction on diffusion-weighted images (DWI) is clinically important. However, the accuracy of automatic segmentation of stroke lesions is affected by a lot of factors. By applying graded ADC thresholds, our study verifies the value of ADC threshold on the performance of the deep learning models in segmenting acute ischemic infarction with increasing the Dice similarity coefficient (DSC) by the lowering the ADC threshold. In addition, our study provides a new window to distinguish cytotoxic edema and vasogenic edema in acute stroke. Moreover, our results further show a size-dependent influence of DSC for stroke segmentation.

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