AbstractBackgroundChemical exchange saturation transfer (CEST) MRI is a promising imaging modality in ischemic stroke detection due to its sensitivity in sensing postischemic pH alteration. However, the accurate segmentation of pH‐altered regions remains difficult due to the complicated sources in water signal changes of CEST MRI. Meanwhile, manual localization and quantification of stroke lesions are laborious and time‐consuming, which cannot meet the urgent need for timely therapeutic interventions.PurposeThe goal of this study was to develop an automatic lesion segmentation approach of the ischemic region based on CEST MR images. A novel segmentation framework based on the fully convolutional neural network was investigated in our task.MethodsZ‐spectra from 10 rats were manually labeled as ground truth and split into two datasets, where the training dataset including 3 rats was used to generate a segmentation model, and the remaining rats were used as test datasets to evaluate the model's performance. Then a 1D fully convolutional neural network equipped with bottleneck structures was set up, and a Grad‐CAM approach was used to produce a coarse localization map, which can reflect the relevancy to the “ischemia” class of each pixel.ResultsAs compared with the ground truth, the proposed network model achieved satisfying segmentation results with high values of evaluation metrics including specificity (SPE), sensitivity (SEN), accuracy (ACC), and Dice similarity coefficient (DSC), especially in some intractable situations where conventional MRI modalities and CEST quantitative method failed to distinguish between ischemic and normal tissues; also the model with augmentation was robust to input perturbations. The Grad‐CAM maps performed clear tissue change distributions and interpreted the segmentations, showed a strong correlation with the quantitative method, and gave extended thinking to the function of networks.ConclusionsThe proposed method can segment ischemia region from CEST images, with the Grad‐CAM maps giving access to interpretative information about the segmentations, which demonstrates great potential in clinical routines.