Many studies have been carried out to segmentation brain tumors on 3D Magnetic Resonance Imaging (MRI) images with 3D or 2D approaches. The 3D approach pays attention to the interrelationships between slices in a 3D image. However, this requires high resources, while the 2D approach requires lower resources but ignores the voxel relationship in 3D space. The 2.5D approach seeks to combine the lightness of the 2D approach and the voxel interconnection of the 3D approach. This article proposes SDA-UNet2.5D, a shallow UNet 2.5D architecture that pays attention to the interconnectedness of 3D images by involving five slices to get one slice of segmentation prediction results. The architecture is trained using the Brain Tumor Segmentation (BraTS) 2018, 2019, and 2020 datasets. Compared to other architectures, this proposed architecture has a high segmentation speed with 4.05-4.24 seconds to segment one patient data. Online validation resulted in superior average dice performance of 75.70, 88.82, 77.33 for the BraTS 2018, 71.29, 88.00, 76.55 for the BraTS 2019, and 70.80, 87.95, 75.89

Original languageEnglish
Pages (from-to)135-149
Number of pages15
JournalInternational Journal of Intelligent Engineering and Systems
Issue number2
Publication statusPublished - Apr 2022


  • 2.5d approach
  • Atrous convolution
  • Attention mechanisms
  • Brain tumor segmentation
  • Shallow unet 2.5d


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