Comprehensive Segmentation of Gray Matter Structures on T1-Weighted Brain MRI: A Comparative Study of Convolutional Neural Network, Convolutional Neural Network Hybrid-Transformer or -Mamba Architectures.

Journal: AJNR. American journal of neuroradiology
PMID: 39433334

Abstract

BACKGROUND AND PURPOSE: Recent advances in deep learning have shown promising results in medical image analysis and segmentation. However, most brain MRI segmentation models are limited by the size of their data sets and/or the number of structures they can identify. This study evaluates the performance of 6 advanced deep learning models in segmenting 122 brain structures from T1-weighted MRI scans, aiming to identify the most effective model for clinical and research applications.

Authors

  • Yujia Wei
    From the Department of Radiology, Mayo Clinic, Rochester, Minnesota.
  • Jaidip Manikrao Jagtap
    From the Department of Radiology, Mayo Clinic, Rochester, Minnesota.
  • Yashbir Singh
    Biomedical Engineering, Chung Yuan Christian University, Taoyuan.
  • Bardia Khosravi
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.
  • Jason Cai
    Radiology Informatics Lab, Department of Radiology, Mayo Clinic, Rochester, Minnesota.
  • Jeffrey L Gunter
    Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, USA. Electronic address: gunter.jeffrey@mayo.edu.
  • Bradley J Erickson
    Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.

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