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

Tongxu Lin, Junyu Lin, Guoheng Huang, Xiaochen Yuan, Guo Zhong, Fenfang Xie, Jiao Li

Research output: Contribution to journalArticlepeer-review


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 encourage existing segmentation networks to improve breast tumor segmentation by prior shape information. The source code will be made available at

Original languageEnglish
Article number145015
JournalPhysics in Medicine and Biology
Issue number14
Publication statusPublished - 21 Jul 2023


  • attention mechanism
  • breast tumor segmentation
  • deep convolution networks
  • prior shape information
  • superpixel


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