1. Deep learning for brain MRI tissue and structure segmentation : a comprehensive reviewNedim Šišić, Peter Rogelj, 2025, review article 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.
Keywords: magnetic resonance imaging, brain, image segmentation, deep learning
Published in RUP: 10.10.2025; Views: 606; Downloads: 6
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4. Kidney segmentation in renal magnetic resonance imaging - current status and prospectsFrank G. Zöllner, Marek Kociński, Laura L. Hansen, Alena-Kathrin Golla, Amira Šerifović-Trbalić, A. Lundervold, Andrzej Materka, Peter Rogelj, 2021, review article Keywords: renal MRI, image segmentation, deep learning
Published in RUP: 04.06.2021; Views: 3223; Downloads: 48
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6. Image registration in dynamic renal MRI-current status and prospectsFrank G. Zöllner, Amira Šerifović-Trbalić, Gordian Kabelitz, Marek Kociński, Andrzej Materka, Peter Rogelj, 2020, review article Keywords: kidney disease, image registration, dynamic MRI, DCE-MRI, ASL
Published in RUP: 18.03.2020; Views: 2458; Downloads: 93
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7. Thermally modified (TM) beech wood : compression properties, fracture toughness and cohesive law in mode II obtained from the three-point end-notched flexure (3ENF) testVáclav Sebera, Miguel Redon, Martin Brabec, David Děcký, Petr Čermák, Jan Tippner, Jaromír Milch, 2019, original scientific article Keywords: beech, brittleness of wood, cohesive law, compliance-based beam method (CBBM), compressive elastic modulus, digital image correlation (DIC), equiva-lent crack length approach (ECLA), fracture, mode II, thermal modification, thermally modified wood (TMW), three-point end-notched flexure (3ENF)
Published in RUP: 18.07.2019; Views: 7585; Downloads: 120
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