  Prostate cancer is the second leading cause of cancer death among men in the
United States. The diagnosis of prostate MRI often relies on the accurate
prostate zonal segmentation. However, state-of-the-art automatic segmentation
methods often fail to produce well-contained volumetric segmentation of the
prostate zones since certain slices of prostate MRI, such as base and apex
slices, are harder to segment than other slices. This difficulty can be
overcome by accounting for the cross-slice relationship of adjacent slices, but
current methods do not fully learn and exploit such relationships. In this
paper, we propose a novel cross-slice attention mechanism, which we use in a
Transformer module to systematically learn the cross-slice relationship at
different scales. The module can be utilized in any existing learning-based
segmentation framework with skip connections. Experiments show that our
cross-slice attention is able to capture the cross-slice information in
prostate zonal segmentation and improve the performance of current
state-of-the-art methods. Our method improves segmentation accuracy in the
peripheral zone, such that the segmentation results are consistent across all
the prostate slices (apex, mid-gland, and base).
