SIP-UNet: Sequential Inputs Parallel UNet Architecture for Segmentation of Brain Tissues from Magnetic Resonance Images
Proper analysis of changes in brain structure can lead to a more accurate diagnosis of specific brain disorders. The accuracy of segmentation is crucial for quantifying changes in brain structure. In recent studies, UNet-based architectures have outperformed other deep learning architectures in biom...
Main Authors: | Rukesh Prajapati, Goo-Rak Kwon |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-08-01
|
Series: | Mathematics |
Subjects: | |
Online Access: | https://www.mdpi.com/2227-7390/10/15/2755 |
Similar Items
-
Potato Leaf Disease Segmentation Method Based on Improved UNet
by: Jun Fu, et al.
Published: (2023-10-01) -
Analysis of Swin-UNet vision transformer for Inferior Vena Cava filter segmentation from CT scans
by: Rahul Gomes, et al.
Published: (2023-12-01) -
ConvWin-UNet: UNet-like hierarchical vision Transformer combined with convolution for medical image segmentation
by: Xiaomeng Feng, et al.
Published: (2023-01-01) -
Automatic kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for malignant potential analysis in complex renal cyst based on CT images
by: Parin Kittipongdaja, et al.
Published: (2022-03-01) -
ResD-Unet Research and Application for Pulmonary Artery Segmentation
by: Hongfang Yuan, et al.
Published: (2021-01-01)