Comprehensive Segmentation of Gray Matter Structures on T1-Weighted Brain MRI: A Comparative Study of Convolutional Neural Network, Convolutional Neural Network Hybrid-Transformer or -Mamba Architectures

References

1. 1.↵
   1. Radue E-W,
   2. Barkhof F,
   3. Kappos L, et al

   . Correlation between brain volume loss and clinical and MRI outcomes in multiple sclerosis. Neurology 2015;84:784–93 doi:10.1212/WNL.0000000000001281 pmid:25632085

   CrossRefPubMed
2. 2.↵
   1. Mora F,
   2. Segovia G,
   3. del Arco A

   . Aging, plasticity and environmental enrichment: structural changes and neurotransmitter dynamics in several areas of the brain. Brain Res Rev 2007;55:78–88 doi:10.1016/j.brainresrev.2007.03.011 pmid:17561265

   CrossRefPubMedWeb of Science
3. 3.↵
   1. Yu Q,
   2. Mai Y,
   3. Ruan Y, et al

   . An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer’s disease. Alzheimers Res Ther 2021;13:23

   CrossRefPubMed
4. 4.↵
   1. Adegun AA,
   2. Viriri S,
   3. Ogundokun RO

   . Deep learning approach for medical image analysis. Comput Intell Neurosci 2021;2021:e6215281 doi:10.1155/2021/6215281

   CrossRef
5. 5.↵
   1. Chen S,
   2. Qiu C,
   3. Yang W, et al

   . Multiresolution aggregation transformer UNet based on multiscale input and coordinate attention for medical image segmentation. Sensors 2022;22:3820 doi:10.3390/s22103820

   CrossRefPubMed
6. 6.↵
   1. Strudel R,
   2. Garcia R,
   3. Laptev I, et al

   . Segmenter: transformer for semantic segmentation. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal, QC, Canada: 2021:7242–7252 doi:10.1109/ICCV48922.2021.00717

   CrossRef
7. 7.↵
   1. Dosovitskiy A,
   2. Beyer L,
   3. Kolesnikov A, et al

   . An image is worth 16x16 words: transformers for image recognition at scale. arxiv [Epub ahead of print] June 3, 2021. Available on: https://arxiv.org/abs/2010.11929.
8. 8.↵
   1. Hatamizadeh A,
   2. Tang Y,
   3. Nath V, et al

   . UNETR: transformers for 3D medical image segmentation. In: 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). Waikoloa, HI: 2022:1748–58 doi:10.1109/WACV51458.2022.00181

   CrossRef
9. 9.↵
   1. Crimi A,
   2. Bakas S

   1. Hatamizadeh A,
   2. Nath V,
   3. Tang Y, et al

   . Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images. In: Crimi A, Bakas S, eds. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer-Verlag International Publishing 2022:272–84
10. 10.↵
    1. Gu A,
    2. Goel K,
    3. Ré C

    . Efficiently modeling long sequences with structured state spaces. arxiv October 31, 2021 doi:10.48550/arXiv.2111.00396

    CrossRef
11. 11.↵
    1. Gu A,
    2. Dao T

    . Mamba: linear-time sequence modeling with selective state spaces. arxiv December 1, 2023 doi:10.48550/arXiv.2312.00752

    CrossRef
12. 12.↵
    1. Crimi A,
    2. Bakas S,
    3. Kuijf H

    1. Myronenko A, et al

    . 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi A, Bakas S, Kuijf H, eds. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer-Verlag International Publishing; 2019:311–20
13. 13.↵
    1. Ma J,
    2. Li F,
    3. U-Mamba WB

    . Enhancing long-range dependency for biomedical image segmentation. arxiv [Epub ahead of print] January 9, 2024. Available on: https://arxiv.org/pdf/2401.04722.
14. 14.↵
    1. Avants BB,
    2. Tustison NJ,
    3. Stauffer M, et al

    . The Insight ToolKit image registration framework. Front Neuroinform 2014;8:44 doi:10.3389/fninf.2014.00044 pmid:24817849

    CrossRefPubMed
15. 15.↵
    1. Isensee F,
    2. Schell M,
    3. Pflueger I, et al

    . Automated brain extraction of multisequence MRI using artificial neural networks. Hum Brain Mapp 2019;40:4952–64 doi:10.1002/hbm.24750 pmid:31403237

    CrossRefPubMed
16. 16.↵
    1. Isensee F,
    2. Jaeger PF,
    3. Kohl SAA, et al

    . nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 2021;18:203–11 doi:10.1038/s41592-020-01008-z pmid:33288961

    CrossRefPubMed
17. 17.↵
    1. Persson K,
    2. Bohbot VD,
    3. Bogdanovic N, et al

    . Finding of increased caudate nucleus in patients with Alzheimer’s disease. Acta Neurol Scand 2018;137:224–32 doi:10.1111/ane.12800 pmid:28741672

    CrossRefPubMed
18. 18.↵
    1. Kahraman AT,
    2. Fröding T,
    3. Toumpanakis D, et al

    . Enhanced classification performance using deep learning based segmentation for pulmonary embolism detection in CT angiography. Heliyon 2024:10:e38118.

    CrossRef
19. 19.↵
    1. Yu W,
    2. Wang X

    . MambaOut: do we really need Mamba for vision? May 13, 2024. doi:10.48550/arXiv.2405.07992

    CrossRef
20. 20.↵
    1. Kim R,
    2. Müller S,
    3. Garcia TP

    . svReg: structural varying-coefficient regression to differentiate how regional brain atrophy affects motor impairment for Huntington disease severity groups. Biom J 2021;63:1254–71 doi:10.1002/bimj.202000312 pmid:33871905

    CrossRefPubMed
21. 21.↵
    1. Moazzami K,
    2. Shao IY,
    3. Chen LY, et al

    . Atrial fibrillation, brain volumes, and subclinical cerebrovascular disease (from the Atherosclerosis Risk in Communities Neurocognitive Study [ARIC-NCS]). Am J Cardiol 2020;125:222–28 doi:10.1016/j.amjcard.2019.10.010 pmid:31771759

    CrossRefPubMed
22. 22.↵
    1. Walker KA,
    2. Gottesman RF,
    3. Wu A, et al

    . Association of hospitalization, critical illness, and infection with brain structure in older adults. J Am Geriatr Soc 2018;66:1919–26 doi:10.1111/jgs.15470 pmid:30251380

    CrossRefPubMed
23. 23.↵
    1. Rovaris M,
    2. Inglese M,
    3. van Schijndel RA, et al

    . Sensitivity and reproducibility of volume change measurements of different brain portions on magnetic resonance imaging in patients with multiple sclerosis. J Neurol 2000;247:960–65 doi:10.1007/s004150070054 pmid:11200690

    CrossRefPubMedWeb of Science
24. 24.↵
    1. He H,
    2. Liang L,
    3. Tang T, et al

    . Progressive brain changes in Parkinson’s disease: a meta-analysis of structural magnetic resonance imaging studies. Brain Res 2020;1740:146847 doi:10.1016/j.brainres.2020.146847 pmid:32330518

    CrossRefPubMed
25. 25.↵
    1. Chen X-Y,
    2. Chen Z-Y,
    3. Dong Z, et al

    . Regional volume changes of the brain in migraine chronification. Neural Regen Res 2020;15:1701–08 doi:10.4103/1673-5374.276360 pmid:32209774

    CrossRefPubMed
26. 26.↵
    1. Poulin SP,
    2. Dautoff R,
    3. Morris JC, et al

    ; Alzheimer’s Disease Neuroimaging Initiative. Amygdala atrophy is prominent in early Alzheimer’s disease and relates to symptom severity. Psychiatry Res 2011;194:7–13 doi:10.1016/j.pscychresns.2011.06.014 pmid:21920712

    CrossRefPubMedWeb of Science
27. 27.↵
    1. Holroyd S,
    2. Shepherd ML,
    3. Downs JH

    . Occipital atrophy is associated with visual hallucinations in Alzheimer’s disease. J Neuropsychiatry Clin Neurosci 2000;12:25–28 doi:10.1176/jnp.12.1.25 pmid:10678508

    CrossRefPubMedWeb of Science
28. 28.↵
    1. Convit A,
    2. De Leon MJ,
    3. Tarshish C, et al

    . Specific hippocampal volume reductions in individuals at risk for Alzheimer’s disease. Neurobiol Aging 1997;18:131–38 doi:10.1016/s0197-4580(97)00001-8 pmid:9258889

    CrossRefPubMedWeb of Science
29. 29.↵
    1. Foundas AL,
    2. Leonard CM,
    3. Mahoney SM, et al

    . Atrophy of the hippocampus, parietal cortex, and insula in Alzheimer’s disease: a volumetric magnetic resonance imaging study. Cogn Behav Neurol 1997;10:81
30. 30.↵
    1. Visser P,
    2. Verhey F,
    3. Hofman P, et al

    . Medial temporal lobe atrophy predicts Alzheimer’s disease in patients with minor cognitive impairment. J Neurol Neurosurg Psychiatry 2002;72:491–97 doi:10.1136/jnnp.72.4.491 pmid:11909909

    Abstract/FREE Full Text
31. 31.↵
    1. Devanand DP,
    2. Pradhaban G,
    3. Liu X, et al

    . Hippocampal and entorhinal atrophy in mild cognitive impairment: prediction of Alzheimer disease. Neurology 2007;68:828–36 doi:10.1212/01.wnl.0000256697.20968.d7 pmid:17353470

    Abstract/FREE Full Text
32. 32.↵
    1. Köhler S,
    2. Black SE,
    3. Sinden M, et al

    . Memory impairments associated with hippocampal versus parahippocampal-gyrus atrophy: an MR volumetry study in Alzheimer’s disease. Neuropsychologia 1998;36:901–14 doi:10.1016/s0028-3932(98)00017-7 pmid:9740363

    CrossRefPubMedWeb of Science
33. 33.↵
    1. Schuff N,
    2. Woerner N,
    3. Boreta L, et al

    ; Alzheimer’s Disease Neuroimaging Initiative. MRI of hippocampal volume loss in early Alzheimer’s disease in relation to ApoE genotype and biomarkers. Brain 2009;132:1067–77 doi:10.1093/brain/awp007 pmid:19251758

    CrossRefPubMedWeb of Science
34. 34.↵
    1. Schwarz CG,
    2. Gunter JL,
    3. Wiste HJ, et al

    ; Alzheimer’s Disease Neuroimaging Initiative. A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer’s disease severity. Neuroimage Clin 2016;11:802–12 doi:10.1016/j.nicl.2016.05.017 pmid:28050342

    CrossRefPubMed
35. 35.↵
    1. Collins GS,
    2. Reitsma JB,
    3. Altman DG, et al

    . Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 2015;162:55–63 doi:10.7326/M14-0697 pmid:25560714

    CrossRefPubMed