用新型影像技术提升前列腺癌分割精度，深度学习模型表现优异。
Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging
- 采用合成相关扩散成像数据，对比多种深度学习模型分割前列腺腺体。
- SegResNet达76.68%的分割准确率（DSC），性能最优。
- Attention U-Net兼顾精度与效率，适合临床实时应用。
前列腺癌（PCa）是美国男性中最常见的癌症，2024年约有30万例新发病例，占所有诊断的29%，导致3.5万例死亡。传统筛查手段如前列腺特异性抗原（PSA）检测和磁共振成像（MRI）在特异性和泛化能力上存在局限。本文探索了一种新型MRI模态——合成相关扩散成像（CDI$^s$）在提升前列腺腺体分割中的潜力。我们使用U-Net、SegResNet、Swin UNETR、Attention U-Net和LightM-UNet等先进深度学习模型，在200例CDI$^s$患者队列上进行分割实验。结果显示，SegResNet表现最佳，分割准确率（DSC）达76.68 ± 0.8；Attention U-Net虽略低（74.82 ± 2.0），但兼具精度与计算效率。研究证实，深度学习结合CDI$^s$可有效提升前列腺癌管理与临床支持能力。
原文摘要 · Abstract (English)
Prostate cancer (PCa) is the most prevalent cancer among men in the United States, accounting for nearly 300,000 cases, 29\% of all diagnoses and 35,000 total deaths in 2024. Traditional screening methods such as prostate-specific antigen (PSA) testing and magnetic resonance imaging (MRI) have been pivotal in diagnosis, but have faced limitations in specificity and generalizability. In this paper, we explore the potential of enhancing PCa gland segmentation using a novel MRI modality called synthetic correlated diffusion imaging (CDI$^s$). We employ several state-of-the-art deep learning models, including U-Net, SegResNet, Swin UNETR, Attention U-Net, and LightM-UNet, to segment prostate glands from a 200 CDI$^s$ patient cohort. We find that SegResNet achieved superior segmentation performance with a Dice-Sorensen coefficient (DSC) of $76.68 \pm 0.8$. Notably, the Attention U-Net, while slightly less accurate (DSC $74.82 \pm 2.0$), offered a favorable balance between accuracy and computational efficiency. Our findings demonstrate the potential of deep learning models in improving prostate gland segmentation using CDI$^s$ to enhance PCa management and clinical support.
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