  Precise segmentation of bladder walls and tumor regions is an essential step
towards non-invasive identification of tumor stage and grade, which is critical
for treatment decision and prognosis of patients with bladder cancer (BC).
However, the automatic delineation of bladder walls and tumor in magnetic
resonance images (MRI) is a challenging task, due to important bladder shape
variations, strong intensity inhomogeneity in urine and very high variability
across population, particularly on tumors appearance. To tackle these issues,
we propose to use a deep fully convolutional neural network. The proposed
network includes dilated convolutions to increase the receptive field without
incurring extra cost nor degrading its performance. Furthermore, we introduce
progressive dilations in each convolutional block, thereby enabling extensive
receptive fields without the need for large dilation rates. The proposed
network is evaluated on 3.0T T2-weighted MRI scans from 60 pathologically
confirmed patients with BC. Experiments shows the proposed model to achieve
high accuracy, with a mean Dice similarity coefficient of 0.98, 0.84 and 0.69
for inner wall, outer wall and tumor region, respectively. These results
represent a very good agreement with reference contours and an increase in
performance compared to existing methods. In addition, inference times are less
than a second for a whole 3D volume, which is between 2-3 orders of magnitude
faster than related state-of-the-art methods for this application. We showed
that a CNN can yield precise segmentation of bladder walls and tumors in
bladder cancer patients on MRI. The whole segmentation process is
fully-automatic and yields results in very good agreement with the reference
standard, demonstrating the viability of deep learning models for the automatic
multi-region segmentation of bladder cancer MRI images.
