Mini Review - Journal of Neuroinformatics and Neuroimaging (2025) Volume 10, Issue 1

Deep Learning Approaches for Automated Segmentation of Brain MRI in Neurodegenerative Disorders

Department of Biomedical Engineering, Hong Kong Institute of Neuroscience, Hong Kong.

Corresponding Author:

Isabella Chen Department of Biomedical Engineering, Hong Kong Institute of Neuroscience, Hong Kong. E-mail: isabella.chen@neurotech.hk

Received: 03-Jan-2025, Manuscript No. AANN-25-169283; Editor assigned: 04-Jan-2025, PreQC No. AANN-25-1692835(PQ); Reviewed: 18-Jan-2025, QC No AANN-25-1692835; Revised: 21-Jan-2025, Manuscript No. AANN-25-1692835(R); Published: 28-Jan-2025, DOI:10.35841/aann -10.1.177

Citation: Chen I. Deep learning approaches for automated segmentation of brain MRI in neurodegenerative disorders. J NeuroInform Neuroimaging. 2025;10(1):177.

Introduction

Automated segmentation of brain MRI plays a critical role in the study and diagnosis of neurodegenerative disorders, enabling precise quantification of structural changes and identification of disease-related patterns. Traditional manual segmentation methods, while accurate, are time-consuming, labor-intensive, and prone to inter-rater variability. Recent advances in artificial intelligence, particularly deep learning, have transformed the segmentation process, allowing for rapid, consistent, and highly accurate delineation of brain structures. Deep learning algorithms, especially convolutional neural networks (CNNs), have shown exceptional performance in recognizing complex patterns within MRI data, making them particularly suited for identifying subtle morphological changes associated with disorders such as Alzheimer’s disease, Parkinson’s disease, and frontotemporal dementia. These approaches promise to enhance early detection, disease monitoring, and research into structural brain changes over time [1].

One of the main advantages of deep learning in brain MRI segmentation is its ability to learn hierarchical feature representations directly from raw imaging data without the need for extensive manual feature engineering. CNN architectures, including U-Net and its variants, are commonly used for medical image segmentation due to their ability to capture both global contextual information and fine-grained structural details. In neurodegenerative disorders, where changes in brain anatomy can be subtle and diffuse, these models excel at detecting variations in gray matter, white matter, and cerebrospinal fluid distribution. Furthermore, deep learning methods can be trained on large-scale annotated datasets to generalize across diverse patient populations and imaging protocols, reducing the bias and inconsistency associated with manual approaches [2].

The application of deep learning for brain MRI segmentation has been further enhanced by the incorporation of three-dimensional convolutional architectures and multi-modal imaging data. 3D CNNs can process volumetric MRI scans in their entirety, capturing spatial dependencies and anatomical relationships that may be missed by 2D slice-based approaches. Incorporating multiple imaging modalities, such as T1-weighted, T2-weighted, and diffusion-weighted MRI, provides complementary information that can improve segmentation accuracy and robustness. Data augmentation strategies, including rotation, scaling, and intensity variation, help mitigate overfitting and improve the generalization of models to unseen datasets. Transfer learning, in which models pre-trained on large medical imaging datasets are fine-tuned for specific neurodegenerative disorder segmentation tasks, has also proven valuable in overcoming limitations posed by small annotated datasets [3].

Deep learning-based segmentation has demonstrated clinical relevance in the context of neurodegenerative disorders by enabling quantitative assessment of brain atrophy and other structural biomarkers. For example, automated segmentation of hippocampal volume and cortical thickness has been used to differentiate between healthy aging and Alzheimer’s disease, as well as to track disease progression over time. Similarly, segmentation of subcortical structures and white matter tracts has aided in understanding the neuroanatomical changes in Parkinson’s disease and multiple sclerosis. Automated tools based on deep learning can generate these measurements within minutes, facilitating their use in both clinical workflows and large-scale research studies. These advancements not only improve efficiency but also allow for more consistent and reproducible results, which are essential for longitudinal monitoring and multi-center trials [4].

Despite the considerable promise of deep learning approaches for automated brain MRI segmentation, several challenges remain. High-quality annotated datasets are crucial for training accurate models, yet such datasets are limited due to the labor-intensive nature of manual segmentation and privacy concerns surrounding medical data sharing. Variability in MRI acquisition parameters, scanner hardware, and patient demographics can lead to domain shift, reducing model performance when applied to external datasets. Strategies such as domain adaptation, harmonization techniques, and federated learning are being explored to address these issues. Additionally, while deep learning models can achieve impressive accuracy, their “black-box” nature raises concerns about interpretability and trust in clinical decision-making. Efforts to incorporate explainable AI techniques and validate models across diverse real-world settings will be essential for widespread clinical adoption [5].

Conclusion

Deep learning approaches have revolutionized automated brain MRI segmentation, offering unprecedented accuracy, speed, and scalability in the study and diagnosis of neurodegenerative disorders. By leveraging advanced CNN architectures, multi-modal data integration, and robust training strategies, these methods enable precise quantification of structural brain changes critical for early detection and disease monitoring. While challenges related to data availability, domain generalization, and interpretability remain, ongoing research and methodological innovations are steadily addressing these barriers. As these technologies mature, deep learning-based segmentation is poised to become an integral component of both clinical neuroimaging practice and large-scale neuroscience research, ultimately improving diagnostic precision and patient care in neurodegenerative diseases.

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