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Prostate cancer is the second leading cause of cancer death among men in theUnited States. The diagnosis of prostate MRI often relies on the accurateprostate zonal segmentation. However, state-of-the-art automatic segmentationmethods often fail to produce well-contained volumetric segmentation of theprostate zones since certain slices of prostate MRI, such as base and apexslices, are harder to segment than other slices. This difficulty can beovercome by accounting for the cross-slice relationship of adjacent slices, butcurrent methods do not fully learn and exploit such relationships. In thispaper, we propose a novel cross-slice attention mechanism, which we use in aTransformer module to systematically learn the cross-slice relationship atdifferent scales. The module can be utilized in any existing learning-basedsegmentation framework with skip connections. Experiments show that ourcross-slice attention is able to capture the cross-slice information inprostate zonal segmentation and improve the performance of currentstate-of-the-art methods. Our method improves segmentation accuracy in theperipheral zone, such that the segmentation results are consistent across allthe prostate slices (apex, mid-gland, and base).

CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI