# Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images

arXiv: https://arxiv.org/abs/2308.11376 · HTML: https://mlsanity.com/paper/2308.11376/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 18 of 18)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [AutoAugment: Learning Augmentation Policies from Data](https://mlsanity.com/paper/1805.09501/) (2018)
- [Data Valuation using Reinforcement Learning](https://mlsanity.com/paper/1909.11671/) (2019)
- [Adversarial AutoAugment](https://mlsanity.com/paper/1912.11188/) (2019)
- [Constrained Convolutional Neural Networks for Weakly Supervised Segmentation](https://mlsanity.com/paper/1506.03648/) (2015)
- [CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation](https://mlsanity.com/paper/1908.10555/) (2019)
- [Constrained-CNN losses for weakly supervised segmentation](https://mlsanity.com/paper/1805.04628/) (2018)
- [Constrained Deep Weak Supervision for Histopathology Image Segmentation](https://mlsanity.com/paper/1701.00794/) (2017)
- [Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labels](https://mlsanity.com/paper/2110.08048/) (2021)
- [Fully Convolutional Multi-Class Multiple Instance Learning](https://mlsanity.com/paper/1412.7144/) (2014)
- [Is segmentation uncertainty useful?](https://mlsanity.com/paper/2103.16265/) (2021)
- [Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound](https://mlsanity.com/paper/1907.01743/) (2019)
- [Image quality assessment for machine learning tasks using meta-reinforcement learning](https://mlsanity.com/paper/2203.14258/) (2022)
- [Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation](https://mlsanity.com/paper/2202.09798/) (2022)
- [Adaptable image quality assessment using meta-reinforcement learning of task amenability](https://mlsanity.com/paper/2108.04359/) (2021)
- [Development and evaluation of intraoperative ultrasound segmentation with negative image frames and multiple observer labels](https://mlsanity.com/paper/2108.04114/) (2021)

## Built on by (most cited first, 3 of 3)

- [A Narrative Review of Image Processing Techniques Related to Prostate Ultrasound](https://mlsanity.com/paper/2407.00678/) (2024)
- [SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours](https://mlsanity.com/paper/2505.18989/) (2025)
- [Competing for pixels: a self-play algorithm for weakly-supervised segmentation](https://mlsanity.com/paper/2405.16628/) (2024)

## Abstract (arXiv)

We propose Boundary-RL, a novel weakly supervised segmentation method that utilises only patch-level labels for training. We envision the segmentation as a boundary detection problem, rather than a pixel-level classification as in previous works. This outlook on segmentation may allow for boundary delineation under challenging scenarios such as where noise artefacts may be present within the region-of-interest (ROI) boundaries, where traditional pixel-level classification-based weakly supervised methods may not be able to effectively segment the ROI. Particularly of interest, ultrasound images, where intensity values represent acoustic impedance differences between boundaries, may also benefit from the boundary delineation approach. Our method uses reinforcement learning to train a controller function to localise boundaries of ROIs using a reward derived from a pre-trained boundary-presence classifier. The classifier indicates when an object boundary is encountered within a patch, as the controller modifies the patch location in a sequential Markov decision process. The classifier itself is trained using only binary patch-level labels of object presence, which are the only labels used during training of the entire boundary delineation framework, and serves as a weak signal to inform the boundary delineation. The use of a controller function ensures that a sliding window over the entire image is not necessary. It also prevents possible false-positive or -negative cases by minimising number of patches passed to the boundary-presence classifier. We evaluate our proposed approach for a clinically relevant task of prostate gland segmentation on trans-rectal ultrasound images. We show improved performance compared to other tested weakly supervised methods, using the same labels e.g., multiple instance learning.