Abstract #0817

Automatic Segmentation and Diagnosis of Breast Cancer in Multicenter Data based on Deep Learning and Tissue-specific Histogram Normalization

Yansong Bai¹, Rencheng Zheng¹, Weibo Chen², Chao You³, Chengyan Wang⁴, and He Wang¹

¹Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, ²Philips Healthcare, Shanghai, China, ³Shanghai Cancer Center, Fudan University, Shanghai, China, ⁴Human Phenome Institute, Fudan University, Shanghai, China

Synopsis

Keywords: Segmentation, Breast

This study presented an intelligent diagnosis system to segment breast tumors in dynamic contrast-enhanced (DCE) images from multicenter dataset and determine the risk of triple-negative breast cancer (TNBC) with a four-step model: a) breast segmentation with no-new Unet (nnUnet); b) multicenter data normalization using Tissue-specific Histogram Normalization (TSHN); c) tumor segmentation with nnUnet model and d) automatic diagnosis of breast cancer with radiomics analysis based on segmented masks. The proposed model exhibited a superior performance in segmentation and diagnosis of breast cancer in multicenter data.

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