深度学习自动分割乳腺组织，提升癌症风险评估效率与准确性。
Validation of Fully-Automated Deep Learning-Based Fibroglandular Tissue Segmentation for Efficient and Reliable Quantitation of Background Parenchymal Enhancement in Breast MRI
- 用深度学习自动分割乳腺腺体组织，替代人工标注。
- 深度学习方法与医生评分更一致，定量结果更可靠。
- 适合医学影像分析、乳腺癌风险评估研究者使用。
乳腺动态对比增强磁共振成像（DCE-MRI）中的背景实质强化（BPE）具有作为乳腺癌风险标志物的潜力。临床上，BPE通常由放射科医生进行定性评估，而定量测量可提供更精确的风险评估。本研究评估了一种开源的全自动深度学习（DL）方法在分割乳腺腺体组织（FGT）以量化BPE方面的表现，并与半自动模糊c均值方法进行比较。基于100名女性的乳腺MRI数据，我们评估了分割一致性、定量BPE指标的一致性以及与定性BPE评估的相关性。两位放射科医生对两种方法的分割质量进行评分。结果显示，两种方法在定量BPE测量上具有良好的一致性，但深度学习方法与定性BPE评估的相关性更强，且其分割结果被医生评为更高质量。研究提示，基于深度学习的FGT分割可提高客观化BPE量化效率，有助于标准化乳腺癌风险评估。
原文摘要 · Abstract (English)
Background parenchymal enhancement (BPE) on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) shows potential as a breast cancer risk marker. Clinically, BPE is qualitatively assessed by radiologists, but quantitative BPE measures offer potential for more precise risk evaluation. This study evaluated an existing open-source, fully-automated deep learning-based (DL-based) method for segmenting fibroglandular tissue (FGT) to quantify BPE and compared it to a semi-automated fuzzy c-means method. Using breast MRI examinations from 100 women, we evaluated segmentation agreement, concordance across quantitative BPE metrics, and associations with qualitative BPE. The quality of FGT segmentations from both methods was scored by a radiologist. While the DL-based and semi-automated methods showed good agreement for quantitative BPE measurements, DL-based measures more strongly correlated with qualitative BPE assessments and DL-based segmentations were scored as higher quality by the radiologist. Our findings suggest that DL-based FGT segmentation enhances efficiency for objective BPE quantification and may improve standardized breast cancer risk assessment.
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