计算机科学
分割
人工智能
渲染(计算机图形)
计算机视觉
稳健性(进化)
图像分割
模式识别(心理学)
乳腺超声检查
边界(拓扑)
深度学习
乳腺摄影术
乳腺癌
数学
医学
数学分析
生物化学
化学
癌症
内科学
基因
作者
Ruobing Huang,Min Lin,Haoran Dou,Zhiping Lin,Qilong Ying,Xiaohong Jia,Wenwen Xu,Zihan Mei,Xin Yang,Yijie Dong,Jianqiao Zhou,Dong Ni
标识
DOI:10.1016/j.media.2022.102478
摘要
Breast Ultrasound (BUS) has proven to be an effective tool for the early detection of cancer in the breast. A lesion segmentation provides identification of the boundary, shape, and location of the target, and serves as a crucial step toward accurate diagnosis. Despite recent efforts in developing machine learning algorithms to automate this process, problems remain due to the blurry or occluded edges and highly irregular nodule shapes. Existing methods often produce over-smooth or inaccurate results, failing the need of identifying detailed boundary structures which are of clinical interest. To overcome these challenges, we propose a novel boundary-rendering framework that explicitly highlights the importance of boundary for automated nodule segmentation in BUS images. It utilizes a boundary selection module to automatically focuses on the ambiguous boundary region and a graph convolutional-based boundary rendering module to exploit global contour information. Furthermore, the proposed framework embeds nodule classification via semantic segmentation and encourages co-learning across tasks. Validation experiments were performed on different BUS datasets to verify the robustness of the proposed method. Results show that the proposed method outperforms states-of-art segmentation approaches (Dice=0.854, IOU=0.919, HD=17.8) in nodule delineation, as well as obtains a higher classification accuracy than classical classification models.
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