The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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. 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${}^{-3}$ mm${}^2$/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. 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${}^{-3}$ mm${}^2$/s and 0.8 × 10${}^{-3}$ mm${}^2$/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${}^{-3}$ mm${}^2$/s (p = .062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10${}^{-3}$ mm${}^2$/s achieved the highest DSC in the segmentation of AIS lesion. 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${}^{-3}$ mm${}^2$/s in segmentating AIS lesion with highest DSC. 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${}^{-3}$ mm${}^2$/s.