Li, Ya HuiYa HuiLiLin, Shao ChiehShao ChiehLinHSIAO-WEN CHUNGChang, Chia ChingChia ChingChangPeng, Hsu HsiaHsu HsiaPengHuang, Teng YiTeng YiHuangShen, Wu ChungWu ChungShenTsai, Chon HawChon HawTsaiLo, Yu ChienYu ChienLoLee, Tung YangTung YangLeeJuan, Cheng HsuanCheng HsuanJuanJuan, Cheng EnCheng EnJuanChang, Hing ChiuHing ChiuChangLiu, Yi JuiYi JuiLiuJuan, Chun JungChun JungJuan2023-05-122023-05-122023-01-0109387994https://scholars.lib.ntu.edu.tw/handle/123456789/631008Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10–3 mm2/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10–3 mm2/s and 0.8 × 10–3 mm2/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10–3 mm2/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10–3 mm2/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10–3 mm2/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10–3mm2/s.enDeep Learning | Diffusion Magnetic Resonance Imaging | Ischemic Stroke | Neural Networks, Computer | Retrospective Studyjournal article10.1007/s00330-023-09622-z370953612-s2.0-85153362523https://api.elsevier.com/content/abstract/scopus_id/85153362523