Brain MRI Segmentation

A 3D transformer-based model for whole brain segmentation from T1W MRI image

Detailed whole brain segmentation is an essential quantitative technique in medical image analysis, which provides a non-invasive way of measuring brain regions from a clinical acquired structural magnetic resonance imaging (MRI). We provide the pre-trained model for training and inferencing whole brain segmentation with 133 structures. Training pipeline is provided to support active learning in MONAI Label and training with bundle.

A tutorial and release of model for whole brain segmentation using the 3D transformer-based segmentation model UNEST.

Authors: Xin Yu ([email protected] )

Yinchi Zhou ([email protected] ) | Yucheng Tang ([email protected] )

Fig.1 - The demonstration of T1w MRI images registered in MNI space and the whole brain segmentation labels with 133 classes

Model Overview

A pre-trained UNEST base model [1] for volumetric (3D) whole brain segmentation with T1w MR images. To leverage information across embedded sequences, ”shifted window” transformers are proposed for dense predictions and modeling multi-scale features. However, these attempts that aim to complicate the self-attention range often yield high computation complexity and data inefficiency. Inspired by the aggregation function in the nested ViT, we propose a new design of a 3D U-shape medical segmentation model with Nested Transformers (UNesT) hierarchically with the 3D block aggregation function, that learn locality behaviors for small structures or small dataset. This design retains the original global self-attention mechanism and achieves information communication across patches by stacking transformer encoders hierarchically.

Fig.2 - The network architecture of UNEST Base model

Data

The training data is from the Vanderbilt University and Vanderbilt University Medical Center with public released OASIS and CANDI datsets. Training and testing data are MRI T1-weighted (T1w) 3D volumes coming from 3 different sites. There are a total of 133 classes in the whole brain segmentation task. Among 50 T1w MRI scans from Open Access Series on Imaging Studies (OASIS) (Marcus et al., 2007) dataset, 45 scans are used for training and the other 5 for validation. The testing cohort contains Colin27 T1w scan (Aubert-Broche et al., 2006) and 13 T1w MRI scans from the Child and Adolescent Neuro Development Initiative (CANDI) (Kennedy et al., 2012). All data are registered to the MNI space using the MNI305 (Evans et al., 1993) template and preprocessed follow the method in (Huo et al., 2019). Input images are randomly cropped to the size of 96 × 96 × 96.

Important

The brain MRI images for training are registered to Affine registration from the target image to the MNI305 template using NiftyReg. The data should be in the MNI305 space before inference.

If your images are already in MNI space, skip the registration step.

You could use any resitration tool to register image to MNI space. Here is an example using ants. Registration to MNI Space: Sample suggestion. E.g., use ANTS or other tools for registering T1 MRI image to MNI305 Space.

pip install antspyx

#Sample ANTS registration

import ants
import sys
import os

fixed_image = ants.image_read('<fixed_image_path>')
moving_image = ants.image_read('<moving_image_path>')
transform = ants.registration(fixed_image,moving_image,'Affine')

reg3t = ants.apply_transforms(fixed_image,moving_image,transform['fwdtransforms'][0])
ants.image_write(reg3t,output_image_path)

Training configuration

The training and inference was performed with at least one 24GB-memory GPU.

Actual Model Input: 96 x 96 x 96

Input and output formats

Input: 1 channel T1w MRI image in MNI305 Space.

commands example

Download trained checkpoint model to ./model/model.pt:

Add scripts component: To run the workflow with customized components, PYTHONPATH should be revised to include the path to the customized component:

export PYTHONPATH=$PYTHONPATH: '<path to the bundle root dir>/scripts'

Execute Training:

python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf

Execute inference:

python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf

More examples output

Fig.3 - The output prediction comparison with variant and ground truth

Training/Validation Benchmarking

A graph showing the training accuracy for fine-tuning 600 epochs.

With 10 fine-tuned labels, the training process converges fast.

Complete ROI of the whole brain segmentation

133 brain structures are segmented.

#1

#2

#3

#4

0: background

1 : 3rd-Ventricle

2 : 4th-Ventricle

3 : Right-Accumbens-Area

4 : Left-Accumbens-Area

5 : Right-Amygdala

6 : Left-Amygdala

7 : Brain-Stem

8 : Right-Caudate

9 : Left-Caudate

10 : Right-Cerebellum-Exterior

11 : Left-Cerebellum-Exterior

12 : Right-Cerebellum-White-Matter

13 : Left-Cerebellum-White-Matter

14 : Right-Cerebral-White-Matter

15 : Left-Cerebral-White-Matter

16 : Right-Hippocampus

17 : Left-Hippocampus

18 : Right-Inf-Lat-Vent

19 : Left-Inf-Lat-Vent

20 : Right-Lateral-Ventricle

21 : Left-Lateral-Ventricle

22 : Right-Pallidum

23 : Left-Pallidum

24 : Right-Putamen

25 : Left-Putamen

26 : Right-Thalamus-Proper

27 : Left-Thalamus-Proper

28 : Right-Ventral-DC

29 : Left-Ventral-DC

30 : Cerebellar-Vermal-Lobules-I-V

31 : Cerebellar-Vermal-Lobules-VI-VII

32 : Cerebellar-Vermal-Lobules-VIII-X

33 : Left-Basal-Forebrain

34 : Right-Basal-Forebrain

35 : Right-ACgG–anterior-cingulate-gyrus

36 : Left-ACgG–anterior-cingulate-gyrus

37 : Right-AIns–anterior-insula

38 : Left-AIns–anterior-insula

39 : Right-AOrG–anterior-orbital-gyrus

40 : Left-AOrG–anterior-orbital-gyrus

41 : Right-AnG—angular-gyrus

42 : Left-AnG—angular-gyrus

43 : Right-Calc–calcarine-cortex

44 : Left-Calc–calcarine-cortex

45 : Right-CO—-central-operculum

46 : Left-CO—-central-operculum

47 : Right-Cun—cuneus

48 : Left-Cun—cuneus

49 : Right-Ent—entorhinal-area

50 : Left-Ent—entorhinal-area

51 : Right-FO—-frontal-operculum

52 : Left-FO—-frontal-operculum

53 : Right-FRP—frontal-pole

54 : Left-FRP—frontal-pole

55 : Right-FuG—fusiform-gyrus

56 : Left-FuG—fusiform-gyrus

57 : Right-GRe—gyrus-rectus

58 : Left-GRe—gyrus-rectus

59 : Right-IOG—inferior-occipital-gyrus ,

60 : Left-IOG—inferior-occipital-gyrus

61 : Right-ITG—inferior-temporal-gyrus

62 : Left-ITG—inferior-temporal-gyrus

63 : Right-LiG—lingual-gyrus

64 : Left-LiG—lingual-gyrus

65 : Right-LOrG–lateral-orbital-gyrus

66 : Left-LOrG–lateral-orbital-gyrus

67 : Right-MCgG–middle-cingulate-gyrus

68 : Left-MCgG–middle-cingulate-gyrus

69 : Right-MFC—medial-frontal-cortex

70 : Left-MFC—medial-frontal-cortex

71 : Right-MFG—middle-frontal-gyrus

72 : Left-MFG—middle-frontal-gyrus

73 : Right-MOG—middle-occipital-gyrus

74 : Left-MOG—middle-occipital-gyrus

75 : Right-MOrG–medial-orbital-gyrus

76 : Left-MOrG–medial-orbital-gyrus

77 : Right-MPoG–postcentral-gyrus

78 : Left-MPoG–postcentral-gyrus

79 : Right-MPrG–precentral-gyrus

80 : Left-MPrG–precentral-gyrus

81 : Right-MSFG–superior-frontal-gyrus

82 : Left-MSFG–superior-frontal-gyrus

83 : Right-MTG—middle-temporal-gyrus

84 : Left-MTG—middle-temporal-gyrus

85 : Right-OCP—occipital-pole

86 : Left-OCP—occipital-pole

87 : Right-OFuG–occipital-fusiform-gyrus

88 : Left-OFuG–occipital-fusiform-gyrus

89 : Right-OpIFG-opercular-part-of-the-IFG

90 : Left-OpIFG-opercular-part-of-the-IFG

91 : Right-OrIFG-orbital-part-of-the-IFG

92 : Left-OrIFG-orbital-part-of-the-IFG

93 : Right-PCgG–posterior-cingulate-gyrus

94 : Left-PCgG–posterior-cingulate-gyrus

95 : Right-PCu—precuneus

96 : Left-PCu—precuneus

97 : Right-PHG—parahippocampal-gyrus

98 : Left-PHG—parahippocampal-gyrus

99 : Right-PIns–posterior-insula

100 : Left-PIns–posterior-insula

101 : Right-PO—-parietal-operculum

102 : Left-PO—-parietal-operculum

103 : Right-PoG—postcentral-gyrus

104 : Left-PoG—postcentral-gyrus

105 : Right-POrG–posterior-orbital-gyrus

106 : Left-POrG–posterior-orbital-gyrus

107 : Right-PP—-planum-polare

108 : Left-PP—-planum-polare

109 : Right-PrG—precentral-gyrus

110 : Left-PrG—precentral-gyrus

111 : Right-PT—-planum-temporale

112 : Left-PT—-planum-temporale

113 : Right-SCA—subcallosal-area

114 : Left-SCA—subcallosal-area

115 : Right-SFG—superior-frontal-gyrus

116 : Left-SFG—superior-frontal-gyrus

117 : Right-SMC—supplementary-motor-cortex

118 : Left-SMC—supplementary-motor-cortex

119 : Right-SMG—supramarginal-gyrus

120 : Left-SMG—supramarginal-gyrus

121 : Right-SOG—superior-occipital-gyrus

122 : Left-SOG—superior-occipital-gyrus

123 : Right-SPL—superior-parietal-lobule

124 : Left-SPL—superior-parietal-lobule

125 : Right-STG—superior-temporal-gyrus

126 : Left-STG—superior-temporal-gyrus

127 : Right-TMP—temporal-pole

128 : Left-TMP—temporal-pole

129 : Right-TrIFG-triangular-part-of-the-IFG

130 : Left-TrIFG-triangular-part-of-the-IFG

131 : Right-TTG—transverse-temporal-gyrus

132 : Left-TTG—transverse-temporal-gyrus

Bundle Integration in MONAI Lable

The inference and training pipleine can be easily used by the MONAI Label server and 3D Slicer for fast labeling T1w MRI images in MNI space.

Disclaimer

This is an example, not to be used for diagnostic purposes.

References

[1] Yu, Xin, Yinchi Zhou, Yucheng Tang et al. Characterizing Renal Structures with 3D Block Aggregate Transformers. arXiv preprint arXiv:2203.02430 (2022). https://arxiv.org/pdf/2203.02430.pdf

[2] Zizhao Zhang et al. Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding. AAAI Conference on Artificial Intelligence (AAAI) 2022

[3] Huo, Yuankai, et al. 3D whole brain segmentation using spatially localized atlas network tiles. NeuroImage 194 (2019): 105-119.

License

Copyright (c) MONAI Consortium

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.