# Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images

arXiv: https://arxiv.org/abs/1612.01655 · HTML: https://mlsanity.com/paper/1612.01655/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2016.

## Built on by (most cited first, 10 of 10)

- [Temporal Memory Relation Network for Workflow Recognition from Surgical Video](https://mlsanity.com/paper/2103.16327/) (2021)
- [MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Images](https://mlsanity.com/paper/2305.19956/) (2023)
- [Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images](https://mlsanity.com/paper/2104.01896/) (2021)
- [Context-Aware Self-Supervised Learning of Whole Slide Images](https://mlsanity.com/paper/2306.04763/) (2023)
- [A Narrative Review of Image Processing Techniques Related to Prostate Ultrasound](https://mlsanity.com/paper/2407.00678/) (2024)
- [Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center Study](https://mlsanity.com/paper/2209.02126/) (2022)
- [Catalyzing Clinical Diagnostic Pipelines Through Volumetric Medical Image Segmentation Using Deep Neural Networks: Past, Present, & Future](https://mlsanity.com/paper/2103.14969/) (2021)
- [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)
- [Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation](https://mlsanity.com/paper/2606.31198/) (2026)

## Abstract (arXiv)

Boundary incompleteness raises great challenges to automatic prostate segmentation in ultrasound images. Shape prior can provide strong guidance in estimating the missing boundary, but traditional shape models often suffer from hand-crafted descriptors and local information loss in the fitting procedure. In this paper, we attempt to address those issues with a novel framework. The proposed framework can seamlessly integrate feature extraction and shape prior exploring, and estimate the complete boundary with a sequential manner. Our framework is composed of three key modules. Firstly, we serialize the static 2D prostate ultrasound images into dynamic sequences and then predict prostate shapes by sequentially exploring shape priors. Intuitively, we propose to learn the shape prior with the biologically plausible Recurrent Neural Networks (RNNs). This module is corroborated to be effective in dealing with the boundary incompleteness. Secondly, to alleviate the bias caused by different serialization manners, we propose a multi-view fusion strategy to merge shape predictions obtained from different perspectives. Thirdly, we further implant the RNN core into a multiscale Auto-Context scheme to successively refine the details of the shape prediction map. With extensive validation on challenging prostate ultrasound images, our framework bridges severe boundary incompleteness and achieves the best performance in prostate boundary delineation when compared with several advanced methods. Additionally, our approach is general and can be extended to other medical image segmentation tasks, where boundary incompleteness is one of the main challenges.