  Segmentation of certain hollow organs, such as the bladder, is especially
hard to automate due to their complex geometry, vague intensity gradients in
the soft tissues, and a tedious manual process of the data annotation routine.
Yet, accurate localization of the walls and the cancer regions in the
radiologic images of such organs is an essential step in oncology. To address
this issue, we propose a new class of hollow kernels that learn to 'mimic' the
contours of the segmented organ, effectively replicating its shape and
structural complexity. We train a series of the U-Net-like neural networks
using the proposed kernels and demonstrate the superiority of the idea in
various spatio-temporal convolution scenarios. Specifically, the dilated
hollow-kernel architecture outperforms state-of-the-art spatial segmentation
models, whereas the addition of temporal blocks with, e.g., Bi-LSTM,
establishes a new multi-class baseline for the bladder segmentation challenge.
Our spatio-temporal model based on the hollow kernels reaches the mean dice
scores of 0.936, 0.736, and 0.712 for the bladder's inner wall, the outer wall,
and the tumor regions, respectively. The results pave the way towards other
domain-specific deep learning applications where the shape of the segmented
object could be used to form a proper convolution kernel for boosting the
segmentation outcome.
