Promptable segmentation with region exploration enables minimal-effort expert-level prostate cancer delineation

2026-02-19

AI Summary

Prostate cancer exhibits subtle appearances on MRI and suffers from scarce annotations, making it challenging for existing automatic segmentation methods to balance accuracy and efficiency. This work proposes an interactive segmentation framework that, for the first time, integrates reinforcement learning with region growing. Leveraging point-based prompts, an uncertainty-guided exploration strategy, and an adaptive reward mechanism, the method achieves high-precision segmentation with minimal human intervention. Evaluated on the PROMIS and PI-CAI datasets, it outperforms the current state-of-the-art fully automatic approaches by 9.9% and 8.9%, respectively, attaining segmentation performance comparable to that of radiologists while reducing annotation time by an order of magnitude.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Reinforcement Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Purpose: Accurate segmentation of prostate cancer on magnetic resonance (MR) images is crucial for planning image-guided interventions such as targeted biopsies, cryoablation, and radiotherapy. However, subtle and variable tumour appearances, differences in imaging protocols, and limited expert availability make consistent interpretation difficult. While automated methods aim to address this, they rely on large expertly-annotated datasets that are often inconsistent, whereas manual delineation remains labour-intensive. This work aims to bridge the gap between automated and manual segmentation through a framework driven by user-provided point prompts, enabling accurate segmentation with minimal annotation effort. Methods: The framework combines reinforcement learning (RL) with a region-growing segmentation process guided by user prompts. Starting from an initial point prompt, region-growing generates a preliminary segmentation, which is iteratively refined through RL. At each step, the RL agent observes the image and current segmentation to predict a new point, from which region growing updates the mask. A reward, balancing segmentation accuracy and voxel-wise uncertainty, encourages exploration of ambiguous regions, allowing the agent to escape local optima and perform sample-specific optimisation. Despite requiring fully supervised training, the framework bridges manual and fully automated segmentation at inference by substantially reducing user effort while outperforming current fully automated methods. Results: The framework was evaluated on two public prostate MR datasets (PROMIS and PICAI, with 566 and 1090 cases). It outperformed the previous best automated methods by 9.9% and 8.9%, respectively, with performance comparable to manual radiologist segmentation, reducing annotation time tenfold.

Problem

• prostate cancer segmentation
• magnetic resonance imaging
• expert annotation
• minimal-effort delineation
• image-guided intervention

Innovation

• promptable segmentation
• reinforcement learning
• region growing
• point prompts
• uncertainty-aware exploration