分割
计算机科学
人工智能
乳腺肿瘤
模式识别(心理学)
图像分割
磁共振成像
像素
班级(哲学)
乳房磁振造影
乳腺摄影术
乳腺癌
放射科
医学
癌症
内科学
作者
Chengtao Peng,Yue Zhang,You Meng,Yang Yang,Bensheng Qiu,Yuzhu Cao,Jian Zheng
标识
DOI:10.1016/j.compbiomed.2022.105685
摘要
Breast tumor segmentation plays a critical role in the diagnosis and treatment of breast diseases. Current breast tumor segmentation methods are mainly deep learning (DL) based methods, which exacted the contrast information between tumors and backgrounds, and produced tumor candidates. However, all these methods were developed based on traditional standard convolutions, which may not be able to model various tumor shapes and extract pure information of tumors (the extracted information usually contain non-tumor information). Besides, the loss functions used in these methods mainly aimed to minimize the intra-class distances, while ignoring the influence of inter-class distances upon segmentation. In this paper, we propose a novel lesion morphology aware network to segment breast tumors in 2D magnetic resonance images (MRI). The proposed network employs a hierarchical structure that contains two stages: breast segmentation stage and tumor segmentation stage. In the tumor segmentation stage, we devise a tumor morphology aware network to incorporate pure tumor characteristics, which facilitates contrastive information extraction. Further, we propose a hybrid intra- and inter-class distance optimization loss to supervise the network, which can minimize intra-class distances meanwhile maximizing inter-class distances, hence reducing the potential false positive/negative pixels in segmentation results. Verified on a clinical 2D MRI breast tumor dataset, our proposed method achieves eminent segmentation results and outperforms state-of-the-art methods, implying that the proposed method has a good prospect for clinical use.
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