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.

翻译：我们调查了U-Net基于U-Net的模型在男性骨盆视图Ultary Bladder(UB)图像中分割 Urinary Bladder(UB)的实用性。美国图像中UB的分解有助于放射学家对UB进行诊断。然而,美国图像中的UB具有任意的形状,界限模糊,而且内部和内部差异大得多,使得分解成为相当具有挑战性的任务。我们对U-Net(UB-UTA)状态的UNet分解网络(UB)结构的研究显示,由于美国形象的音响,U-Net(UB)的参数参数往往无法捕捉到 UBUB的显著特征。美国图像中的UB的分解形状和比例不同。美国图像中的U-net(U-Net)的分解形状和比例不同。此外,U-net(U-Net)的分解因美国形象的形状和比例不同而不同,UBU-Net的分解度也由于美国形象的形状不同而不同,因此无法捕捉捉摸。