用合成相关扩散成像提升前列腺癌病灶分割精度
Cancer-Net PCa-MultiSeg: Multimodal Enhancement of Prostate Cancer Lesion Segmentation Using Synthetic Correlated Diffusion Imaging
- 引入合成相关扩散成像（CDI^s）增强标准DWI序列
- 94%配置下性能提升，最高相对改进达72.5%
- 无需额外扫描时间，可直接融入临床流程
当前深度学习方法在前列腺癌病灶分割上表现有限，大型患者队列中Dice分数低于0.32。为解决此问题，我们研究了合成相关扩散成像（CDI^s）对标准扩散成像协议的增强作用。在200例配准了CDI^s、扩散加权成像（DWI）和表观扩散系数（ADC）序列的患者中，对六种先进分割架构进行全面评估。结果表明，CDI^s集成在94%的配置中可靠提升或保持性能，部分模型相对于基线模态实现高达72.5%的统计显著相对提升。CDI^s + DWI成为最安全的增强路径，在一半架构中实现显著改进且无任何退化实例。由于CDI^s源自现有DWI采集，无需额外扫描时间或架构修改，可立即部署于临床工作流。研究确立了CDI^s在多种深度学习架构中的可验证整合路径，使其成为前列腺癌病灶分割任务的实际即插即用增强方案。
原文摘要 · Abstract (English)
Current deep learning approaches for prostate cancer lesion segmentation achieve limited performance, with Dice scores of 0.32 or lower in large patient cohorts. To address this limitation, we investigate synthetic correlated diffusion imaging (CDI$^s$) as an enhancement to standard diffusion-based protocols. We conduct a comprehensive evaluation across six state-of-the-art segmentation architectures using 200 patients with co-registered CDI$^s$, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) sequences. We demonstrate that CDI$^s$ integration reliably enhances or preserves segmentation performance in 94% of evaluated configurations, with individual architectures achieving up to 72.5% statistically significant relative improvement over baseline modalities. CDI$^s$ + DWI emerges as the safest enhancement pathway, achieving significant improvements in half of evaluated architectures with zero instances of degradation. Since CDI$^s$ derives from existing DWI acquisitions without requiring additional scan time or architectural modifications, it enables immediate deployment in clinical workflows. Our results establish validated integration pathways for CDI$^s$ as a practical drop-in enhancement for PCa lesion segmentation tasks across diverse deep learning architectures.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。