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The clinical management of breast cancer depends on an accurate understandingof the tumor and its anatomical context to adjacent tissues and landmarkstructures. This context may be provided by semantic segmentation methods;however, previous works have been largely limited to a singular focus on thetumor alone and rarely other tissue types. In contrast, we present a methodthat exploits tissue-tissue interactions to accurately segment every majortissue type in the breast including: chest wall, skin, adipose tissue,fibroglandular tissue, vasculature and tumor via standard-of-care DynamicContrast Enhanced MRI. Comparing our method to prior state-of-the-art, weachieved a superior Dice score on tumor segmentation while maintainingcompetitive performance on other studied tissues across multiple institutions.Briefly, our method proceeds by localizing the tumor using 2D object detectors,then segmenting the tumor and surrounding tissues independently using two 3DU-nets, and finally integrating these results while mitigating false positivesby checking for anatomically plausible tissue-tissue contacts. The objectdetection models were pre-trained on ImageNet and COCO, and operated on MIP(maximum intensity projection) images in the axial and sagittal planes,establishing a 3D tumor bounding box. By integrating multiple relevantperi-tumoral tissues, our work enables clinical applications in breast cancerstaging, prognosis and surgical planning.

Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI