通过质量筛选与分阶段训练，提升乳腺癌分割在多中心MRI中的鲁棒性。
Selective Phase-Aware Training of nnU-Net for Robust Breast Cancer Segmentation in Multi-Center DCE-MRI
- 基于nnU-Net设计选择性分阶段训练策略，聚焦图像质量与中心差异影响。
- 仅使用杜克和纳特数据集的早期相位图像（0000-0002）时，分割性能更稳定。
- 适合需要高鲁棒性医学图像分割的临床研究与多中心数据应用。
乳腺癌是女性中最常见的癌症，也是主要死因之一。动态对比增强MRI（DCE-MRI）是评估乳腺肿瘤的重要影像工具，但目前缺乏标准化基准来分析治疗反应并指导个性化诊疗。我们参与了MAMA-MIA挑战赛的原发肿瘤分割任务，提出一种针对nnU-Net的有选择性的、分阶段感知的训练框架，强调以质量为导向的数据筛选以增强模型鲁棒性和泛化能力。采用nnU-Net框架，通过系统分析图像质量与中心特异性变异对分割性能的影响。在DUKE、NACT、ISPY1和ISPY2数据集上的受控实验表明，包含存在运动伪影和对比度降低的ISPY扫描会损害分割性能，即使经过先进预处理（如对比度受限自适应直方图均衡化，CLAHE）。相反，在杜克和纳特数据集上训练，这些数据具有更清晰的对比度和更少运动伪影，尽管分辨率各异，且使用早期相位图像（0000–0002），能提供更稳定的训练条件。结果表明，分阶段敏感与质量感知的训练策略对于在异质临床数据集中实现可靠分割至关重要，揭示了盲目扩充数据集的局限性，并推动未来自动化质量驱动的数据选择策略发展。
原文摘要 · Abstract (English)
Breast cancer remains the most common cancer among women and is a leading cause of female mortality. Dynamic contrast-enhanced MRI (DCE-MRI) is a powerful imaging tool for evaluating breast tumors, yet the field lacks a standardized benchmark for analyzing treatment responses and guiding personalized care. We participated in the MAMA-MIA Challenge's Primary Tumor Segmentation task and this work presents a proposed selective, phase-aware training framework for the nnU-Net architecture, emphasizing quality-focused data selection to strengthen model robustness and generalization. We employed the No New Net (nnU-Net) framework with a selective training strategy that systematically analyzed the impact of image quality and center-specific variability on segmentation performance. Controlled experiments on the DUKE, NACT, ISPY1, and ISPY2 datasets revealed that including ISPY scans with motion artifacts and reduced contrast impaired segmentation performance, even with advanced preprocessing, such as contrast-limited adaptive histogram equalization (CLAHE). In contrast, training on DUKE and NACT data, which exhibited clearer contrast and fewer motion artifacts despite varying resolutions, with early phase images (0000-0002) provided more stable training conditions. Our results demonstrate the importance of phase-sensitive and quality-aware training strategies in achieving reliable segmentation performance in heterogeneous clinical datasets, highlighting the limitations of the expansion of naive datasets and motivating the need for future automation of quality-based data selection strategies.
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