Objective To observe the value of SS-3DUNet model based on spatially separable convolutions for automatically segmenting anal fistula in enhanced MR T1WI.Methods Totally 2 405 pelvic axial enhanced MR T1WI of 29 patients with anal fistula were retrospectively analyzed,and 1 537 images from 19 cases were randomly selected as training set,424 images from 5 cases were as validation set,444 images from 5 cases were as test set.A SS-3DUNet model was constructed based on spatially separable convolutions to automatically segment anal fistula in enhanced MR T1WI,and inter-layer feature enhancement module was incorporated to improve the location of fistula features.The model was trained in training set and the best one was selected based on validation set.Taking the results of manual labeling by clinicians,the efficacy of SS-3DUNet model for automatically segmenting anal fistulas was observed based on test set.Results The time of SS-3DUNet automatically segmenting anal fistula in a single image in test set was 0.59-0.61 s,and the coincidence of the boundary of fistula segmented by the model and manual label was high.The average Dice similarity coefficient,sensitivity and accuracy of SS-3DUNet for automatically segmenting anal fistula in test set was 0.746,70.04%and 82.93%,respectively.Conclusion SS-3DUNet model based on spatially separable convolutions could effectively automatically segmenting anal fistula in enhanced T1WI.