Deep Learning for Brain MRI Tissue and Structure Segmentation: A Comprehensive Review
Abstract
1. Introduction
2. Methods
2.1. Literature Search
(brain[MeSH Terms]) AND (magnetic resonance imaging[MeSH Terms]) AND (segmentation[Title/Abstract] OR segmentations[Title/Abstract]) AND (“neural network”[Title/Abstract] OR “neural networks”[Title/Abstract] OR “deep learning”[Title/Abstract] OR transformer[Title/Abstract])
2.2. Definition and Applications
2.3. Traditional Segmentation Approaches
2.4. Deep Learning Segmentation
2.5. Segmentation Architectures
2.6. Patch-Based and Whole-Image Based Models
Method | Modalities | Labels | Dim. | Input | Arch. | Avail. | DSC (%) |
---|---|---|---|---|---|---|---|
Brébisson et al. [83] | T1 | 133 | 2.5D and 3D | Patch | CNN | No | 72.5 |
Shakeri et al. [88] | T1 | 8 subcortical | 2D | Full | FCN | Yes | 82.4 |
Moeskops et al. [84] | T1, T2 | 8 | 2D | Patch | CNN | No | 89.8 |
Milletari et al. [89] | QSM | 26 subcortical | 2D, 2.5D, or 3D | Patch | CNN | No | 77 |
Bao et al. [90] | T1 | 7 subcortical | 2D | Patch | CNN | No | 82.22 |
Moeskops et al. [58] | T1 | 7 | 2D | Patch | FCN | No | 92 |
Dolz et al. [91] | T1 | 8 subcortical | 3D | Patch | FCN | Yes | 89 |
Kushibar et al. [81] | T1 | 14 subcortical | 2.5D | Patch | CNN | Yes | 86.9 |
Mehta et al. [85] | T1 | 32-134 | 2D and 3D | Patch | CNN | No | 84.4 |
Mehta et al. [92] | T1 | 7 subcortical | 2D and 3D | Patch | U-Net | No | 83 |
Wachinger et al. [93] | T1 | 25 | 3D | Patch | CNN | Yes | 92 |
Li et al. [57] | T1 | 155 | 3D | Patch | Dilated | Yes | 84.3 |
Karani et al. [94] | T1, T2 | 7 subcortical | / | / | U-Net | No | 89.3 |
Roy et al. [47] | T1 | 27 | 2.5D | Full | U-Net | Yes | 90.1 |
Roy et al. [63] | T1 | 27 | / | / | U-Net + Attention | No | 86.2 |
Li et al. [59] | T1, T1-IR, FLAIR | 10 | 2D | Full | U-Net | No | 80.9 |
Kaku et al. [95] | T1 | 102 | 2D | Full | U-Net | Yes | 81.9 |
Huo et al. [96] | T1 | 133 | 3D | Patch | U-Net | Yes | 77.6 |
Jog et al. [97] | T1, T2 | 9, 12 | 3D | Patch | U-Net | Yes | 94 |
Novosad et al. [98] | T1 | 8 and 12 subcortical | 3D | Patch | FCN | Yes | 89.5 |
Novosad et al. [99] | T1 | 12 subcortical | 3D | Patch | CNN | Yes | 80.7 |
Sun et al. [64] | T1, T1-IR, FLAIR | Tissue, 25 | 3D | Patch | U-Net + Attention | No | 84.8 |
Dai et al. [100] | T1 | 15-138 | 3D | Patch | U-Net | No | 87.9 |
Roy et al. [101] | T1 | 33 | 2.5D | Full | U-Net | Yes | 88 |
Luna et al. [102] | T1, T1-IR, FLAIR | 8 | 3D | Patch | U-Net | No | 85.5 |
McClure et al. [61] | T1 | 50 | 3D | Patch | Dilated | Yes | 83.7 |
Dalca et al. [103] | T1, PD | 12 | 3D | Full | U-Net | Yes | 83.5 |
Coupe et al. [104] | T1 | 133 | 3D | Patch | U-Net | Yes | 79 |
Ramzan et al. [62] | T1, T1-IR, FLAIR | Tissues, 8 | 3D | Patch | Dilated | No | 91.4 |
Henschel et al. [26] | T1 | 95 | 2.5D | Full | U-Net | Yes | 89 |
Bontempi et al. [80] | T1 | 8 | 3D | Full | U-Net | Yes | 91.3 |
Liu et al. [105] | T1 | 14 subcortical | 3D | Patch | U-Net + LSTM | No | 88.7 |
Lee et al. [49] | T1 | 33, 100+ | 3D | Patch | U-Net + Attention | No | 89.7 |
Zopes et al. [106] | T1, T2, DWI, CT | 27 | 3D | Patch | U-Net | No | 85.3 |
Li et al. [16] | T1 | 8 subcortical | 3D | Patch | C-LSTM | No | 97.6 |
Li et al. [107] | T1 | 8 subcortical | 3D | Patch | U-Net | No | 96.8 |
Svanera et al. [108] | T1 | 8 | 3D | Full | U-Net | Yes | 97.8 |
Li et al. [109] | T1 | 133 | 2D | Full | U-Net + Attention | Yes | 89.7 |
Greve et al. [110] | T1 | 12 subcortical | 3D | Full | U-Net | Yes | 77.8 |
Li et al. [40] | T1 | 54 | 3D | Full | FCN | Yes | 83.1 |
Meyer et al. [41] | T1 | 8 | 3D | Patch | U-Net | Yes | 93.2 |
Wu et al. [86] | T1 | 14, 54 | 3D | Patch | M-FCN | No | 92.2 |
Nejad et al. [111] | T1 | 12 | 2D | Patch | U-Net | Yes | 89.3 |
Liu et al. [112] | T1 | 5, 7 | 3D | Full | CLMorph | No | 76.3 |
Ghazi et al. [113] | T1 | 133 | 2.5D | Full | U-Net | Yes | 81 |
Henschel et al. [48] | T1 | 95 | 2.5D | Full | U-Net | Yes | 89.9 |
Laiton-Bonadiez et al. [70] | T1 | 37 | 3D | Patch | Transformer | No | 90 |
Wei et al. [114] | T1 | 136 | 2D | Full | U-Net + Attention | Yes | 86 |
Yee et al. [82] | T1 | 102 | 3D | Patch | U-Net | No | 84 |
Baniasadi et al. [115] | T1 | 30 subcortical | 3D | Patch | U-Net | Yes | 89 |
Billot et al. [116] | T1, T2, PD, DBS | 110 | 3D | Full | U-Net | Yes | 88 |
Billot et al. [117] | T1, T2, DBS, FLAIR, PD, CT | 33 | 3D | Full | U-Net | Yes | 88 |
Cao et al. [69] | T1 | 31 subcortical | 3D | Patch | Transformer | No | 87.2 |
Li et al. [46] | T1 | 28, 139 | 2D | Full | U-Net + Attention | Yes | 87.7 |
Moon et al. [118] | T1 | 109 | 3D | / | U-Net | No | / |
Cao et al. [74] | T1 | 31 subcortical | 3D | Patch | Mamba | Yes | 88.4 |
Diaz et al. [119] | T1, T2, FLAIR | 7 | 3D | Patch | U-Net | Yes | 88 |
Kujawa et al. [120] | T1 | 108 | 3D | Patch | U-Net | No | 87.5 |
Lorzel et al. [25] | T1 | 58 | 3D | Patch | U-Net | No | 81 |
Svanera et al. [52] | T1 | 7 | 3D | Full | LOD-brain | Yes | 93 |
Goto et al. [121] | T1 | 107 | 3D | Full | U-Net | No | / |
Le Bot et al. [122] | FLAIR | 133 | 3D | Patch | U-Net | No | 91 |
Li et al. [65] | T1, PET | 45 | 3D | Full | Transformer | No | 85.3 |
Li et al. [17] | T1 | 12 | 3D | Patch | U-Net | Yes | 90 |
Puzio et al. [123] | T1, T2 | 38 | 3D | Patch | U-Net | No | 87 |
Wei et al. [73] | T1 | 122 GM | 3D | Patch | Mamba | No | 91.1 |
2.7. Model Dimensionality
Method | Modality | Dim. | Input | Arch. | Avail. | DSC (%) |
---|---|---|---|---|---|---|
Stollenga et al. [128] | T1, T1-IR, T2-FLAIR | 3D | Patch | PyraMiD-LSTM | No | 85.7 |
Nguyen et al. [129] | T1 | 2.5D | Patch | CNN | No | 86 |
Fedorov et al. [130] | T1 | 3D | Patch | Dilated | No | 86.5 |
Chen et al. [43] | T1, T1-IR, T2-FLAIR | 3D | Patch | FCN | No | 86.6 |
Khagi et al. [131] | T1 | 2D | Full | SegNet | No | 76.2 |
Rajchl et al. [132] | T1 | 3D | Patch | FCN | Yes | 93 |
Kumar et al. [53] | T1 | 2D | Patch | SegNet | No | 80 |
Gottapu et al. [133] | T1 | 2D | Patch | CNN | No | 67.3 |
Mahbod et al. [134] | T1, T1-IR, FLAIR | / | / | ANN | No | 85.3 |
Chen et al. [135] | T1, T1-IR, T2-FLAIR | 2D | Patch | OctopusNet | No | 82.9 |
Kong et al. [136] | T1 | 2D | Patch | CNN | No | / |
Bernal et al. [77] | T1, and T1 and T2 | 2D, 3D | Patch | FCN, U-Net | Yes | 92.9 |
Dolz et al. [45] | T1, T1-IR, T2-FLAIR | 3D | Patch | FCN | Yes | 87.2 |
Gabr et al. [137] | T1, T2, T2-FLAIR, PD | 2D | Full | U-Net | Yes | 93 |
Ito et al. [138] | T1 | 3D | Patch | FCN | No | 86 |
Yogananda et al. [139] | T1 | 3D | Patch | U-Net | No | 86.6 |
Mujica-Vargas et al. [140] | T1, T2, T2-FLAIR | 2D | Full | U-Net | No | 93.1 |
Wang et al. [141] | T1 | 3D | Patch | U-Net | No | 90.4 |
Xie et al. [142] | T1, T2, PD | 2D | Full | LSTM | No | 98.7 |
Yan et al. [143] | T1 | 3D | Full | GCN | No | 91.6 |
Li et al. [60] | T1, T1-IR, T2-FLAIR | 2.5D | Full | Dilated | Yes | 87 |
Wei et al. [51] | T1 | 2.5D | Patch | U-Net | No | 96.3 |
Sun et al. [64] | T1, T1-IR and T2-FLAIR | 3D | Patch | U-Net + Attention | No | 87 |
Ramzan et al. [62] | T1, T1-IR, T2-FLAIR | 3D | Patch | Dilated | No | 88 |
Lee et al. [79] | T1 | 2D | Patch | U-Net | Yes | 93.6 |
Mostapha et al. [39] | T1 | 3D | Full | U-Net | No | 90.3 |
Narayana et al. [144] | T1, T2, T2-FLAIR, PD | 2D | Full | U-Net | Yes | 92.5 |
Yamanakkanavar et al. [78] | T1 | 2D | Patch | U-Net | No | 95.2 |
Sendra-Balcells et al. [24] | T1 | 2D | Full | U-Net | No | 88.5 |
Dayananda et al. [54] | T1 | 2D | Patch | Squeeze U-Net | No | 95.3 |
Basnet et al. [10] | T1, T2 | 3D | Patch | U-Net | Yes | 93.1 |
Long et al. [145] | T1, T1-IR, T2-FLAIR | 3D | Patch | MSCD-UNet | No | 88.5 |
Woo et al. [125] | T1 | 3D | Full | U-Net | No | 94.9 |
Yamanakkanavar et al. [50] | T1 | 2D | Patch | U-Net | No | 95.7 |
Zhang et al. [146] | Diffusion, T1, T2 | 2.5D | Full | U-Net | No | 85 |
Zhang et al. [147] | T1 | 3D | Full | GCN | No | 92.3 |
Wei et al. [66] | T1 | 3D | Patch | Nes-Net | No | 88.5 |
Niu et al. [67] | T1 | 2D | Full | U-Net | Yes | 91.1 |
Goyal et al. [148] | T1 | 2D | Patch | SegNet | No | 83 |
Prajapati et al. [124] | T1 | 2D | Full | U-Net | No | 95.7 |
Rao et al. [68] | T1 | 3D | Full | Transformer | Yes | 95.6 |
Yamanakkanavar et al. [50] | T1 | 2D | Patch | Squeeze U-Net | No | 96 |
Yamanakkanavar et al. [149] | T1 | 2D | Patch | MF2-Net | No | 95.3 |
Dayananda et al. [44] | T1 | 2D | Patch | Squeeze U-Net | No | 95.7 |
Clerigues et al. [150] | T1, T2-FLAIR | 3D | Patch | U-Net | Yes | 94.6 |
Guven et al. [151] | T1 | 2D | Full | GAN | No | / |
Gi et al. [8] | T2 | 2.5D, 3D | Patch, full | U-Net | No | 91.3 |
Oh et al. [152] | T1 | 3D | Full | U-Net | Yes | 88.5 |
Simarro et al. [9] | T1 | 3D | Patch | U-Net | Yes | / |
Hossain et al. [127] | T1 | 3D | Patch | U-Net | Yes | 93.7 |
Liu et al. [38] | T1 | 3D | Patch | U-Net + Attention | Yes | 98.9 |
Mohammadi et al. [72] | T1, T2 | 2D | Full | Transformer | No | 84.4 |
2.8. Generalization Strategies
3. Results
3.1. Validation Strategies
3.2. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Segmentation Type | Comparison | Method 1 | Method 2 | Mean | p-Value |
---|---|---|---|---|---|
Structure | Input Size | Patch | Full | 0.3 | 0.41 |
2D | 2.5D | −1.67 | 0.56 | ||
Dimensionality | 2D | 3D | −1.1 | 0.58 | |
2.5D | 3D | 0.56 | 0.83 | ||
Tissue | Input Size | Patch | Full | −0.02 | 0.97 |
2D | 2.5D | 6.1 | 0.7 | ||
Dimensionality | 2D | 3D | 2.7 | 0.36 | |
2.5D | 3D | −3.4 | 0.34 |
Reference | Method | Training | Labels | DSC (%) |
---|---|---|---|---|
Kaku et al. [95] | DenseUNet | No | 102 | 74.31 |
Yes | 102 | 81.9 | ||
U-Net | No | 102 | 73.29 | |
Yes | 102 | 80 | ||
Henschel et al. [26] | FastSurfer | No | 33 subcortical | 80.19 |
62 cortical | 80.65 | |||
3D U-Net | 33 subcortical | 78.65 | ||
62 cortical | 79 | |||
Li et al. [109] | AceNet QuickNat v2 | Yes | 62 cortical | 82.5 77.7 |
Laiton-bonaidez et al. [70] | Proposed | Yes | 37 | 75 |
Liu et al. [112] | CLMorph | No | 5 combined | 64.6 |
Henschel et al. [48] | FastSurferVINN | No | 33 subcortical | 80.06 |
62 cortical | 81.89 | |||
FastSurfer | 33 subcortical | 80.06 | ||
62 cortical | 81.23 | |||
Cao et al. [69] | TABSurfer | Yes | 31 subcortical | 79.2 |
FastSurfer | No | 75.8 | ||
FreeSurfer | \ | 74 | ||
Kujawa et al. [120] | Proposed | No | 108 | 74 |
Lorzel et al. [25] | AutoParch | Yes | 58 combined | 77.8 |
FreeSurfer | 85.7 | |||
Svanera et al. [52] | LOD-Brain | No | 7 combined | 95.5 |
FastSurfer | 96.5 | |||
Diaz et al. [119] | e3nn | Yes | 7 subcortical | 0.88 |
nnUnet | 0.89 |
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Šišić, N.; Rogelj, P. Deep Learning for Brain MRI Tissue and Structure Segmentation: A Comprehensive Review. Algorithms 2025, 18, 636. https://doi.org/10.3390/a18100636
Šišić N, Rogelj P. Deep Learning for Brain MRI Tissue and Structure Segmentation: A Comprehensive Review. Algorithms. 2025; 18(10):636. https://doi.org/10.3390/a18100636
Chicago/Turabian StyleŠišić, Nedim, and Peter Rogelj. 2025. "Deep Learning for Brain MRI Tissue and Structure Segmentation: A Comprehensive Review" Algorithms 18, no. 10: 636. https://doi.org/10.3390/a18100636
APA StyleŠišić, N., & Rogelj, P. (2025). Deep Learning for Brain MRI Tissue and Structure Segmentation: A Comprehensive Review. Algorithms, 18(10), 636. https://doi.org/10.3390/a18100636