结合多尺度与注意力机制的脑组织分割方法

doi:10.13229/j.cnki.jdxbgxb.20231419

Abstract

Abstract:

Due to the small size and low contrast of subcortical brain structures（such as the striatum）in medical images， their segmentation is challenging， making their application in automated medical diagnosis difficult，this article proposes a medical image segmentation network based on deep learning methods to segment the three parts of the striatum， namely the globus pallidus， caudate nucleus， and putamen， in magnetic resonance imaging. The network model proposed in this article has the ability to capture global and local features and establish the correlation between global and local information， and effectively fuses deep semantic features and shallow detail features at different scales without degrading the depth， achieving accurate segmentation of the striatum. The model is validated on publicly available brain datasets and compared with other state-of-the-art methods. The model achieved dice similarity coefficient， average intersection ratio， and 95% Hausdorff distance are 94.26%， 90.94%， and 3.82 respectively， which are better than several other methods and have reached the advanced level. This shows that the model proposed in this article can improve the segmentation accuracy of the striatum and provide a basis for research on related diseases.

Key words: biomedical engineering, medical image segmentation, deep learning, multi-scale feature extraction, striatum

CLC Number:

TP391.4

Xiu-feng ZHANG,Yun-fei JIANG,Sheng-jin GUO,Yan-song LIU,Ling-zhuo TIAN,Shi-chen ZHANG. Brain tissue segmentation method combining multi-scale and attention mechanisms[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(10): 3352-3360.

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模型	苍白球	壳核	尾状核	平均
U-Net^［12］	91.64	94.35	92.53	92.84
Yee等^［22］	92.27	94.62	93.06	93.32
Ramzan等^［23］	92.70	95.21	92.55	93.49
CAN^［26］	92.94	95.51	93.20	93.88
TransUNet^［27］	92.69	94.40	93.57	93.55
CoTr^［28］	91.79	95.50	93.57	93.62
TFCNs^［29］	91.81	94.68	92.07	92.85
EA-Net^［30］	91.03	94.45	93.18	92.89
本文	93.32	95.58	93.87	94.26

模型	苍白球	壳核	尾状核	平均
U-Net^［12］	86.52	89.45	89.11	88.36
Yee等^［22］	88.65	90.38	89.83	89.62
Ramzan等^［23］	89.20	91.10	89.35	89.88
CAN^［26］	89.14	91.84	90.06	90.35
TransUNet^［27］	88.48	90.25	88.97	89.23
CoTr^［28］	87.33	91.76	89.28	89.46
TFCNs^［29］	87.47	90.93	89.50	89.30
EA-Net^［30］	87.46	90.61	90.04	89.37
本文	90.29	91.79	90.73	90.94

模型	苍白球	壳核	尾状核	平均
U-Net^［12］	3.66	4.63	6.45	4.91
Yee等^［22］	3.86	5.04	5.18	4.69
Ramzan 等^［23］	3.36	5.54	6.07	4.99
CAN^［26］	2.98	4.58	6.23	4.60
TransUNet^［27］	3.92	3.89	5.61	4.47
CoTr^［28］	4.08	4.60	5.66	4.78
TFCNs^［29］	4.47	5.41	5.69	5.19
EA-Net^［30］	4.19	5.77	5.21	4.06
本文	2.91	3.47	5.08	3.82

模型	参数量/M	计算量/G
U-Net^［12］	25.76	77.36
Yee等^［22］	31.54	138.47
Ramzan等^［23］	43.71	151.30
CAN^［26］	60.20	178.65
TransUNet^［27］	93.19	138.68
CoTr^［28］	41.92	142.26
TFCNs^［29］	64.21	165.45
EA-Net^［30］	51.07	173.62
本文	36.04	117.63

模型	Dice	IoU	HD
Baseline	92.84	88.36	4.91
BL+DFE	93.65	89.30	4.68
BL+AMFE	94.04	89.69	4.25
本文	94.26	90.94	3.82

Brain tissue segmentation method combining multi-scale and attention mechanisms

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