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.

翻译：目的：磁共振（MR）图像上前列腺癌的精确分割对于规划靶向活检、冷冻消融和放射治疗等图像引导介入至关重要。然而，肿瘤外观的细微多变、成像协议的差异以及专家资源的有限性使得一致判读变得困难。虽然自动化方法旨在解决此问题，但它们依赖于大规模专家标注数据集（这些数据常存在不一致性），而手动勾画仍属劳动密集型工作。本研究旨在通过用户提供点提示驱动的框架，以最小标注工作量实现精确分割，从而弥合自动化与手动分割之间的差距。方法：该框架将强化学习（RL）与用户提示引导的区域生长分割过程相结合。从初始点提示出发，区域生长生成初步分割结果，随后通过RL进行迭代优化。在每一步中，RL智能体观察图像及当前分割结果以预测新点，区域生长基于该点更新掩模。通过平衡分割精度与体素不确定性的奖励机制，鼓励对模糊区域的探索，使智能体能够跳出局部最优解并执行样本特异性优化。尽管需要全监督训练，该框架在推理阶段通过大幅降低用户工作量，同时超越当前全自动方法，有效衔接了手动与全自动分割流程。结果：该框架在两个公开前列腺MR数据集（PROMIS与PICAI，分别包含566例和1090例）上进行了评估。其性能分别超越先前最佳自动化方法9.9%和8.9%，达到与放射科医师手动分割相当的水平，并将标注时间减少至十分之一。