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

arXiv: https://arxiv.org/abs/1907.01743 · HTML: https://mlsanity.com/paper/1907.01743/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2019.

## Built on by (most cited first, 23 of 23)

- [AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images](https://mlsanity.com/paper/2204.12077/) (2022)
- [MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Images](https://mlsanity.com/paper/2305.19956/) (2023)
- [Non-Iterative Scribble-Supervised Learning with Pacing Pseudo-Masks for Medical Image Segmentation](https://mlsanity.com/paper/2210.10956/) (2022)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [Learning Multi-Modal Volumetric Prostate Registration with Weak Inter-Subject Spatial Correspondence](https://mlsanity.com/paper/2102.04938/) (2021)
- [Recurrent Feature Propagation and Edge Skip-Connections for Automatic Abdominal Organ Segmentation](https://mlsanity.com/paper/2201.00317/) (2022)
- [Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images](https://mlsanity.com/paper/2308.11376/) (2023)
- [A Narrative Review of Image Processing Techniques Related to Prostate Ultrasound](https://mlsanity.com/paper/2407.00678/) (2024)
- [Adaptive Multi-Resolution Attention with Linear Complexity](https://mlsanity.com/paper/2108.04962/) (2021)
- [Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images](https://mlsanity.com/paper/2205.13199/) (2022)
- [Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center Study](https://mlsanity.com/paper/2209.02126/) (2022)
- [Multi-modality transrectal ultrasound video classification for identification of clinically significant prostate cancer](https://mlsanity.com/paper/2402.08987/) (2024)
- [Towards Cross-Scale Attention and Surface Supervision for Fractured Bone Segmentation in CT](https://mlsanity.com/paper/2405.01204/) (2024)
- [Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development](https://mlsanity.com/paper/2408.10067/) (2024)
- [RegFreeNet: A Registration-Free Network for CBCT-based 3D Dental Implant Planning](https://mlsanity.com/paper/2601.14703/) (2026)
- [Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers](https://mlsanity.com/paper/2201.10981/) (2022)
- [Exploring Kinetic Curves Features for the Classification of Benign and Malignant Breast Lesions in DCE-MRI](https://mlsanity.com/paper/2404.13929/) (2024)
- [External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation with Limited Data](https://mlsanity.com/paper/2201.00942/) (2022)
- [The use of deep learning in interventional radiotherapy (brachytherapy): a review with a focus on open source and open data](https://mlsanity.com/paper/2205.07516/) (2022)
- [3D EAGAN: 3D edge-aware attention generative adversarial network for prostate segmentation in transrectal ultrasound images](https://mlsanity.com/paper/2311.04049/) (2023)
- [Contextual Embedding Learning to Enhance 2D Networks for Volumetric Image Segmentation](https://mlsanity.com/paper/2404.01723/) (2024)
- [ImplantMamba: Long-range Sequential Modeling Mamba For Dental Implant Position Prediction](https://mlsanity.com/paper/2605.07082/) (2026)
- [Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation](https://mlsanity.com/paper/2606.31198/) (2026)

## Abstract (arXiv)

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.