Improving breast tumor segmentation via shape-wise prior-guided information on cone-beam breast CT images

作者：Tongxu Lin, Junyu Lin, Guoheng Huang, Xiaochen Yuan, Guo Zhong, Fenfang Xie, Jiao Li · 发表于：Physics in Medicine and Biology · 年份：2023 · DOI：10.1088/1361-6560/ace1cf · 被引用次数：3 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、Advanced Neural Network Applications

Abstract Objective . Due to the blurry edges and uneven shape of breast tumors, breast tumor segmentation can be a challenging task. Recently, deep convolution networks based approaches achieve satisfying segmentation results. However, the learned shape information of breast tumors might be lost owing to the successive convolution and down-sampling operations, resulting in limited performance. Approach . To this end, we propose a novel shape-guided segmentation (SGS) framework that guides the segmentation networks to be shape-sensitive to breast tumors by prior shape information. Different from usual segmentation networks, we guide the networks to model shape-shared representation with the assumption that shape information of breast tumors can be shared among samples. Specifically, on the one hand, we propose a shape guiding block (SGB) to provide shape guidance through a superpixel pooling-unpooling operation and attention mechanism. On the other hand, we further introduce a shared classification layer (SCL) to avoid feature inconsistency and additional computational costs. As a result, the proposed SGB and SCL can be effortlessly incorporated into mainstream segmentation networks (e.g. UNet) to compose the SGS, facilitating compact shape-friendly representation learning. Main results . Experiments conducted on a private dataset and a public dataset demonstrate the effectiveness of the SGS compared to other advanced methods. Significance . We propose a united framework to en...