Magnetic resonance imaging bias field estimation and tissue segmentation via convolutional neural networks and multiplicative and additive intrinsic components optimization

Magnetic resonance imaging (MRI) brain tissue segmentation is essential for the diagnosis and treatment of neurological diseases; however, intensity inhomogeneity poses a significant challenge, particularly in data-limited scenarios. This study aimed to explore an algorithm designed to robustly addr...

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Bibliographic Details
Published in:Intelligent medicine
Main Authors: Liu, Jun, Peng, Yuliang, Pu, Mingshu, Tang, Ling, Shao, Lizhi, Wang, Pengxiang, Yang, Lan, Huang, Furong, Shen, Zijie, Li, Chunming
Format: Journal Article
Language:English
Published: Elsevier B.V 2025
Subjects:
Attention mechanism
Brain segmentation
Deep learning
Intensity inhomogeneity
Internal Medicine
Magnetic resonance imaging
Deep learning
Brain segmentation
Attention mechanism
Intensity inhomogeneity
Magnetic resonance imaging
ISSN:2667-1026
Online Access:Get full text
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Summary:Magnetic resonance imaging (MRI) brain tissue segmentation is essential for the diagnosis and treatment of neurological diseases; however, intensity inhomogeneity poses a significant challenge, particularly in data-limited scenarios. This study aimed to explore an algorithm designed to robustly address intensity inhomogeneity for brain MRI tissue segmentation under dataset constraints. We propose a two-stage framework that leverages data-driven and knowledge-driven approaches. Initially, a data-driven model was employed for skull-stripping, where a lightweight module, named multi-view dilated convolution attention (MDCA), is integrated into skip connections. The MDCA module eliminates the effect of intensity inhomogeneity enriched in shallow features at multiple scales, thus avoiding the negative impact on deeper abstract features. Furthermore, we introduced multiplicative and additive intrinsic components optimization (MAICO) algorithm, which decomposes MRI images into their real anatomical structures, multiplicative and additive bias fields, and zero-mean Gaussian noise, thus enabling precise anatomical segmentation. Experiments on MRBrainS13 and MRBrainS18 public datasets involved the random introduction of intensity inhomogeneity to generate training, validation, and testing sets with 60%, 20%, and 20% splits, respectively. Segmentation performance was measured using Dice coefficients and compared to methods such as MICO, FSL, and UNet. An ablation study further validated the efficacy of the MDCA module. Our approach improved MRI brain tissue segmentation accuracy, achieving a mean Dice coefficient of 0.7733 across tissue types. With MDCA and MAICO, it reached 0.8163 for white matter, 0.7402 for gray matter, and 0.7634 for cerebrospinal fluid, outperforming other algorithms. Additionally, MDCA module integration in skip connections yielded a 5% average accuracy boost. This study effectively combined knowledge-driven and data-driven techniques to enhance MRI brain segmentation stability and accuracy, thereby demonstrating strong potential for clinical application in managing intensity inhomogeneity in data-constrained settings.
ISSN:2667-1026
DOI:10.1016/j.imed.2025.02.002