20.500.12556/RUP-21893 Deep learning for brain MRI tissue and structure segmentation a comprehensive review 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. magnetic resonance imaging brain image segmentation deep learning MRI segmentacija slik možgani globoko učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2025-10-10 13:02:38 2025-10-10 13:02:39 2025-11-03 03:03:14 0000-00-00 00:00:00 2025 0 0 str. 1-27 iss. 10, [article no.] 636 Vol. 18 Oct. 2025 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 2025-10-09 004.8:537.635 1999-4893 10.3390/a18100636 252706563 RAZ_Sisic_Nedim_2025.pdf RAZ_Sisic_Nedim_2025.pdf 1 A024811209FB6DDF28E81EB9F2FB5D82 f6b6f2e36950951c2850333f01837c7b692e62d16997c54a65689e97af1cf4a8 e4ffdfc5-a5c8-11f0-8f0b-005056ac49c0 https://repozitorij.upr.si/Dokument.php?lang=slv&id=31806 https://www.mdpi.com/1999-4893/18/10/636 1 caf20764-a5c8-11f0-8f0b-005056ac49c0 https://repozitorij.upr.si/Dokument.php?lang=slv&id=31805 Fakulteta za matematiko, naravoslovje in informacijske tehnologije 0 0 0