  We investigate the applicability of U-Net based models for segmenting Urinary
Bladder (UB) in male pelvic view UltraSound (US) images. The segmentation of UB
in the US image aids radiologists in diagnosing the UB. However, UB in US
images has arbitrary shapes, indistinct boundaries and considerably large
inter- and intra-subject variability, making segmentation a quite challenging
task. Our study of the state-of-the-art (SOTA) segmentation network, U-Net, for
the problem reveals that it often fails to capture the salient characteristics
of UB due to the varying shape and scales of anatomy in the noisy US image.
Also, U-net has an excessive number of trainable parameters, reporting poor
computational efficiency during training. We propose a Slim U-Net to address
the challenges of UB segmentation. Slim U-Net proposes to efficiently preserve
the salient features of UB by reshaping the structure of U-Net using a less
number of 2D convolution layers in the contracting path, in order to preserve
and impose them on expanding path. To effectively distinguish the blurred
boundaries, we propose a novel annotation methodology, which includes the
background area of the image at the boundary of a marked region of interest
(RoI), thereby steering the model's attention towards boundaries. In addition,
we suggested a combination of loss functions for network training in the
complex segmentation of UB. The experimental results demonstrate that Slim
U-net is statistically superior to U-net for UB segmentation. The Slim U-net
further decreases the number of trainable parameters and training time by 54%
and 57.7%, respectively, compared to the standard U-Net, without compromising
the segmentation accuracy.
