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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