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：本研究探讨了基于U-Net的模型在男性盆腔超声（US）图像中分割膀胱（UB）的适用性。US图像中的膀胱分割辅助放射科医师进行膀胱诊断。然而，US图像中的膀胱具有形状任意、边界模糊以及个体间与个体内差异显著的特点，使得分割任务极具挑战性。针对该问题，我们分析了当前最优（SOTA）分割网络U-Net的表现，发现由于噪声US图像中解剖结构的形状与尺度变化，U-Net常难以捕捉膀胱的显著特征。同时，U-Net含有过多可训练参数，导致训练过程中计算效率低下。为此，我们提出Slim U-Net以应对膀胱分割的挑战。该网络通过减少收缩路径中二维卷积层的层数来重塑U-Net结构，从而高效保留膀胱的显著特征并将其传递至扩展路径。为有效区分模糊边界，我们提出一种新型标注方法：在标注感兴趣区域（RoI）的边界处包含图像背景区域，从而引导模型聚焦于边界信息。此外，我们针对膀胱复杂分割任务，建议采用组合损失函数进行网络训练。实验结果表明，Slim U-Net在膀胱分割任务中统计性能显著优于U-Net。与标准U-Net相比，Slim U-Net在不降低分割精度的前提下，可训练参数与训练时间分别减少54%和57.7%。