评估仅使用ADC的深度学习流程在独立扩散加权MRI中进行乳腺癌检测与分割
Evaluating ADC-only deep learning pipelines for breast cancer detection and segmentation using standalone diffusion-weighted MRI
- University of Oviedo（奥维耶多大学）
- University of A Coruña（拉科鲁尼亚大学）
- The University of Texas at Austin（德克萨斯大学奥斯汀分校）
- Purdue University（普渡大学）
- Asturias Central University Hospital (HUCA)（阿斯图里亚斯中央大学医院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究首次全面评估仅使用ADC图像的深度学习流程在乳腺癌分类、检测和分割任务中的性能，弥补了现有研究对DW-MRI和ADC图作为独立替代方案的探索不足。
AI中文摘要：
动态对比增强（DCE）成像是利用磁共振成像（MRI）检测和表征乳腺癌的金标准技术。然而，DCE-MRI需要较长的采集时间，并且需要向血液中注射造影剂，这可能引起过敏反应。相比之下，扩散加权MRI（DW-MRI）是乳腺MRI的标准补充技术，无需造影剂，采集时间更短，并且能够计算与肿瘤细胞密度相关的表观扩散系数（ADC）图。然而，尽管具有这些技术优势，深度学习研究一直集中于基于DCE的模型，几乎未探索DW-MRI和ADC图在单独使用或与DCE-MRI结合时的肿瘤检测性能。在此，我们评估了多种最先进的深度学习技术在仅使用ADC图像进行乳腺癌检测和分割中的应用。据我们所知，这是首次对仅使用ADC的乳腺癌流程在分类、检测和分割任务中进行全面评估。
英文摘要：
Dynamic contrast-enhanced (DCE) imaging is the gold standard technique for the detection and characterization of breast cancer using magnetic resonance imaging (MRI). However, DCE-MRI requires long acquisition times and the administration of contrast into the bloodstream, which can cause allergic reactions. Alternatively, diffusion-weighted MRI (DW-MRI) is a standard complementary technique for breast MRI that does not require contrast, has shorter acquisition times, and enables calculation of apparent diffusion coefficient (ADC) maps that correlate with tumor cellularity. Yet, despite these technical advantages, deep learning research has focused on DCE-based models and has barely explored the tumor detection performance of DW-MRI and ADC maps either in combination with DCE-MRI or as standalone alternatives. Here, we evaluate the application of different state-of-the-art deep learning techniques for detection and segmentation of breast cancer using ADC-only images. This is, to our knowledge, the first comprehensive evaluation of ADC-only breast cancer pipelines for classification, detection, and segmentation tasks.
