| Title: | Deep learning for brain MRI tissue and structure segmentation : a comprehensive review |
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| Authors: | ID Šišić, Nedim (Author)
ID Rogelj, Peter (Author) |
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| Files: |  RAZ_Sisic_Nedim_2025.pdf (956,69 KB)
MD5: A024811209FB6DDF28E81EB9F2FB5D82
 https://www.mdpi.com/1999-4893/18/10/636
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.02 - Review Article |
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| Organization: | FAMNIT - Faculty of Mathematics, Science and Information Technologies
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| Abstract: | Brain MRI segmentation plays a crucial role in neuroimaging studies and clinical trials by enabling the precise localization and quantification of brain tissues and structures. The advent of deep learning has transformed the field, offering accurate and fast tools for MRI segmentation. Nevertheless, several challenges limit the widespread applicability of these methods in practice. In this systematic review, we provide a comprehensive analysis of developments in deep learning-based segmentation of brain MRI in adults, segmenting the brain into tissues, structures, and regions of interest. We explore the key model factors influencing segmentation performance, including architectural design, choice of input size and model dimensionality, and generalization strategies. Furthermore, we address validation practices, which are particularly important given the scarcity of manual annotations, and identify the limitations of current methodologies. We present an extensive compilation of existing segmentation works and highlight the emerging trends and key results. Finally, we discuss the challenges and potential future directions in the field. |
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| Keywords: | magnetic resonance imaging, brain, image segmentation, deep learning |
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| Publication date: | 09.10.2025 |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 1-27 |
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| Numbering: | Vol. 18, iss. 10, [article no.] 636 |
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| PID: | 20.500.12556/RUP-21893  |
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| UDC: | 004.8:537.635 |
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| ISSN on article: | 1999-4893 |
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| DOI: | 10.3390/a18100636 |
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| COBISS.SI-ID: | 252706563 |
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| Publication date in RUP: | 10.10.2025 |
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| Views: | 506 |
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| Downloads: | 6 |
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| Metadata: |    |
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