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

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 encourage existing segmentation networks to improve breast tumor segmentation by prior shape information. The source code will be made available at https://github.com/TxLin7/Shape-Seg.

Original languageEnglish
Article number145015
JournalPhysics in Medicine and Biology
Volume68
Issue number14
DOIs
Publication statusPublished - 21 Jul 2023

Keywords

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

Fingerprint

Dive into the research topics of 'Improving breast tumor segmentation via shape-wise prior-guided information on cone-beam breast CT images'. Together they form a unique fingerprint.

Cite this