BMA-Net: A 3D bidirectional multi-scale feature aggregation network for prostate region segmentation.

Journal: Computer methods and programs in biomedicine
PMID: 39813938

Abstract

BACKGROUND AND OBJECTIVE: Accurate segmentation of the prostate region in magnetic resonance imaging (MRI) is crucial for prostate-related diagnoses. Recent studies have incorporated Transformers into prostate region segmentation to better capture long-range global feature representations. However, due to the computational complexity of Transformers, these studies have been limited to processing single slices. Incorporating multiple slices can facilitate more precise segmentation, but existing methods fail to effectively utilize both intra-slice and inter-slice multi-scale information.

Authors

  • Bangkang Fu
    Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, No. 83, Zhongshan Dong Road, Guiyang, 550002, Guizhou, China.
  • Feng Liu
    Department of Vascular and Endovascular Surgery, The First Medical Center of Chinese PLA General Hospital, 100853 Beijing, China.
  • Junjie He
    Department of Thoracic Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
  • Zi Xu
    Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China.
  • Yunsong Peng
  • Xiaoli Zhang
    School of Life Sciences, Zhengzhou University Zhengzhou 450001 Henan China pingaw@126.com.
  • Rongpin Wang
    Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, 550002 China.

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