Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missing/ambiguous boundary and inhomogeneous intensity distribution of the prostate in TRUS, as well as the large variability in prostate shapes. This paper develops a novel 3D deep neural network equipped with attention modules for better prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the convolutional neural network (CNN). Our attention module utilizes the attention mechanism to selectively leverage the multilevel features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers. Experimental results on challenging 3D TRUS volumes show that our method attains satisfactory segmentation performance. The proposed attention mechanism is a general strategy to aggregate multi-level deep features and has the potential to be used for other medical image segmentation tasks. The code is publicly available at https://github.com/wulalago/DAF3D.

该论文开发了一种新颖的3D深度神经网络，配备了注意力模块，以便更好地在TRUS中进行前列腺分割，通过充分利用卷积神经网络的不同层次所编码的互补信息选择性地利用来自不同层次集成的多级特征，抑制CNN浅层的非前列腺噪声，并在深层次中增加更多的前列腺细节。实验结果表明，该方法在具有挑战性的3D TRUS数据上达到了令人满意的分割性能。

3D 经直肠超声前列腺分割的深度关注特征