Radiologist-Level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans (original) (raw)

2022, Radiology: Artificial Intelligence

S egmentation of breast tumors provides image features such as shape, morphologic structure, texture, and enhancement dynamics that can improve diagnosis and prognosis in patients with breast cancer (1-3). To our knowledge, reliable automated tumor segmentation does not yet exist, and manual segmentation is labor intensive; this has precluded routine clinical evaluation of tumor volume despite mounting evidence that it is a good predictor of patient survival (2). Automatic segmentation with modern deep network techniques has the potential to meet this clinical need. Deep learning methods have been applied in breast tumor segmentation (4,5) and diagnosis (6-11) on mammograms; large datasets of up to 1 million images are available, which greatly boosts the performance of the machine learning systems (12,13). Unlike MRI, however, mammography cannot depict the exact three-dimensional (3D) location and volumetric extent of a lesion. Breast MRI has a higher diagnostic accuracy than mammography (14-16) and outperforms mammography in detection of residual tumors after neoadjuvant therapy (17). Additionally, background parenchymal enhancement measured at MRI with dynamic contrast enhancement is predictive of cancer risk (18). Several studies have automated tumor segmentation in breast MRI by using modern deep networks such as U-Nets or DeepMedic

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