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TACA-RNet: Tri-Axis Based Context-Aware Reverse Network for Multimodal Brain Tumor Segmentation.

Authors :
Kim, Hyunjin
Jo, Youngwan
Lee, Hyojeong
Park, Sanghyun
Source :
Electronics (2079-9292); May2024, Vol. 13 Issue 10, p1997, 19p
Publication Year :
2024

Abstract

Brain tumor segmentation using Magnetic Resonance Imaging (MRI) is vital for clinical decision making. Traditional deep learning-based studies using convolutional neural networks have predominantly processed MRI data as two-dimensional slices, leading to the loss of contextual information. While three-dimensional (3D) convolutional layers represent an advancement, they have not fully exploited pathological information according to the three-axis nature of 3D MRI data—axial, coronal, and sagittal. Recognizing these limitations, we introduce a Tri-Axis based Context-Aware Reverse Network (TACA-RNet). This innovative approach leverages the unique 3D spatial orientations of MRI, learning crucial information on brain anatomy and pathology. We incorporated three specialized modules: a Tri-Axis Channel Reduction module for optimizing feature dimensions, a MultiScale Contextual Fusion module for aggregating multi-scale features and enhancing spatial discernment, and a 3D Axis Reverse Attention module for the precise delineation of tumor boundaries. The TACA-RNet leverages three specialized modules to enhance the understanding of tumor characteristics and spatial relationships within MRI data by fully utilizing its tri-axial structure. Validated on the Brain Tumor Segmentation Challenge 2018 and 2020 datasets, the TACA-RNet demonstrated superior performances over contemporary methodologies. This underscores the critical role of leveraging the three-axis structure of MRI to enhance segmentation accuracy. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20799292
Volume :
13
Issue :
10
Database :
Complementary Index
Journal :
Electronics (2079-9292)
Publication Type :
Academic Journal
Accession number :
177488106
Full Text :
https://doi.org/10.3390/electronics13101997