Chan-Vese aided fuzzy C-means approach for whole breast and fibroglandular tissue segmentation: Preliminary application to real-world breast MRI.

Journal: Medical physics
Published Date: Feb 5, 2025

Abstract

BACKGROUND: Magnetic resonance imaging (MRI) is a highly sensitive modality for diagnosing breast cancer, providing an expanding range of clinical usages that are crucial for the care of women at elevated risk of breast cancer development. Segmentation of the whole breast and fibroglandular tissue (FGT), used to evaluate breast cancer risk, is often manually delineated by radiologists in clinical practice. In this paper, we aim to substitute handcrafted breast density segmentation and categorization. The traditional fuzzy C-means (FCM) enable automatic segmentation but may be susceptible to heterogeneity or sparse FGT distribution in MRI.

Authors

  • Syed Furqan Qadri
    College of Computer Science and Software Engineering, Computer Vision Institute, Shenzhen University, Shenzhen 518060, Guangdong, China.
  • Chao Rong
    Zhejiang Lab, Hangzhou, China.
  • Mubashir Ahmad
    Department of Computer Science and IT, The University of Lahore, Sargodha Campus, Sargodha 40100, Pakistan.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Salman Qadri
    Computer Science Department, MNS-University of Agriculture, Multan 60650, Pakistan.
  • Syeda Shamaila Zareen
    Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
  • Zeyu Zhuang
    Zhejiang Lab, Hangzhou, China.
  • Salabat Khan
    College of Computer Science and Software Engineering, Computer Vision Institute, Shenzhen University, Shenzhen, Guangdong Province 518060, China.
  • Hongxiang Lin
    Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China.

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