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Abstract #3741

Deep slice-crossed network with local weighted loss for brain metastases detection and segmentation

Jiao Qu1, Xin Shu2, Wenjing Zhang1, Mengyuan Xu1, Ying Wang1, Lituan Wang2, Lei Zhang2, and Su Lui1
1Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China, 2College of Computer Science, Sichuan University, Chengdu, China


Brain metastases detection and segmentation on magnetic resonance images is laborious, error-prone and often irreproducible for radiologists and radiation oncologist. We present a specific deep slice-crossed network with local weighted loss to automatically detect and segment brain metastases on contrast-enhanced T1WI images. The results demonstrated the good performance, high robustness and generalizability of the model. In addition, compared with radiologists, the model showed higher sensitivity and increased efficiency in identifying and segmenting brain metastases. The results jointly suggested that the proposed model is a promising tool to assist the workflow in the clinical practice.

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