# Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound

## TL;DR

This paper introduces a novel 3D deep neural network with attention modules for improved prostate segmentation in challenging transrectal ultrasound images, leveraging multi-level features to enhance accuracy.

## Contribution

The paper presents a new attention-based 3D CNN architecture that effectively exploits multi-level features for better prostate segmentation in TRUS images, addressing boundary ambiguity and shape variability.

## Key findings

- Achieved satisfactory segmentation performance on challenging 3D TRUS volumes.
- Proposed attention mechanism effectively suppresses noise and enhances prostate details.
- Method demonstrates potential for other medical image segmentation tasks.

## Abstract

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.