Enhanced U-Net for Infant Brain MRI Segmentation: A (2+1)D Convolutional Approach.

Journal: Sensors (Basel, Switzerland)
PMID: 40096351

Abstract

BACKGROUND: Infant brain tissue segmentation from MRI data is a critical task in medical imaging, particularly challenging due to the evolving nature of tissue contrasts in the early months of life. The difficulty increases as gray matter (GM) and white matter (WM) intensities converge, making accurate segmentation challenging. This study aims to develop an improved U-net-based model to enhance the precision of automatic segmentation of cerebro-spinal fluid (CSF), GM, and WM in 10 infant brain MRIs using the iSeg-2017 dataset.

Authors

  • Lehel Dénes-Fazakas
    Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary.
  • Levente Kovács
    Physiological Controls Research Center, Research and Innovation Center of Óbuda University, Óbuda University, Budapest, Hungary. Electronic address: kovacs.levente@nik.uni-obuda.hu.
  • György Eigner
    Antal Bejczy Center for Intelligent Robotics, Robotics Special College, University Research and Innovation Center, Óbuda University, Bécsi út 96/B, H-1034 Budapest, Hungary.
  • László Szilágyi
    Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary.

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