识别脑卒中微小病灶的多维度图像分割模型和系统开发

陈锡程; 卫泽良; 叶伟; 王皓嘉; 陶永军; 易东; 伍亚舟

doi:10.16016/j.2097-0927.202212169

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识别脑卒中微小病灶的多维度图像分割模型和系统开发

生物医学工程 | 更新时间：2026-01-09

- 识别脑卒中微小病灶的多维度图像分割模型和系统开发
- Multi-dimensional segmentation model and system development for recognizing small lesions after acute ischemic stroke
- 陆军军医大学学报 2023年45卷第6期页码：570-578
- 作者机构：
  
  1.400038 重庆，陆军军医大学(第三军医大学)军事预防医学系军队卫生统计学教研室
  2.318000 浙江台州，浙江省台州市立医院神经内科
- 作者简介：
  
  [伍亚舟，E-mail: asiawu@tmmu.edu.cn WU Yazhou, E-mail: asiawu@tmmu.edu.cn
- 基金信息：
  
  国家自然科学基金项目(82173621);国家自然科学基金项目(81872716)
- DOI：10.16016/j.2097-0927.202212169
  中图分类号： R195.1;R741.04;R743.3
- 收稿：2022-12-28，
  
  修回：2023-01-15，
  
  纸质出版：2023-03-30
- 稿件说明：
移动端阅览
陈锡程, 卫泽良, 叶伟, 等. 识别脑卒中微小病灶的多维度图像分割模型和系统开发[J]. 陆军军医大学学报, 2023,45(6):570-578.

Xicheng CHEN, Zeliang WEI, Wei YE, et al. Multi-dimensional segmentation model and system development for recognizing small lesions after acute ischemic stroke[J]. Journal of Army Medical University, 2023, 45(6): 570-578.
陈锡程, 卫泽良, 叶伟, 等. 识别脑卒中微小病灶的多维度图像分割模型和系统开发[J]. 陆军军医大学学报, 2023,45(6):570-578. DOI： 10.16016/j.2097-0927.202212169.

Xicheng CHEN, Zeliang WEI, Wei YE, et al. Multi-dimensional segmentation model and system development for recognizing small lesions after acute ischemic stroke[J]. Journal of Army Medical University, 2023, 45(6): 570-578. DOI： 10.16016/j.2097-0927.202212169.

摘要

目的

开发一种基于深度学习的多维度分割系统，在磁共振成像(magnetic resonance imaging，MRI)影像中识别微小病灶，为急性缺血性脑卒中(acute ischemic stroke，AIS)的诊治提供决策依据。

方法

提取并融合2D、3D网络的特征，引入了联合损失函数，进而提出了一种新的2.5D方法——多维度多尺度注意力增强网络(multi-dimensional multi-scale attention enhanced network，MMAE-Net)。在AIS分割数据集(训练集171例、测试集43例)上训练和测试了所提方法的性能，并与其他方法进行比较。

结果

相较于2D、3D网络，2.5D网络(MMAE-Net)在各项评价指标上均可取得最优异的分割性能，获得了81.25%的Dice相似系数(dice similarity coefficient，DSC)和84.82%的灵敏度(sensitivity，SEN)。相较于U-Net、ResU-Net、DenseU-Net、AttentionU-Net和SEgmentation TRansformer(SETR)等经典方法和既往研究，MMAE-Net取得了更优异的分割性能。我们还开发了可视化自动分割系统以提升方法的实践和推广能力。

结论

本文基于2D、3D特征融合的理念设计了2.5D多维度分割模型MMAE-Net，其在MRI微小病灶的识别中取得了优异性能，为AIS疾病诊治提供了有效的解决方案。

Abstract

Objective

To develop a deep learning-based multi-dimensional segmentation system to recognize small lesions in magnetic resonance imaging (MRI) images in order to provide a decision-making basis for the diagnosis and treatment of acute ischemic stroke (AIS).

Methods

We extracted and fused the features from 2D and 3D network

introduced the joint loss function

and then proposed a new 2.5D method

a multi-dimensional multi-scale attention enhanced network (MMAE-Net). On AIS segmentation datasets (171 cases in training set and 43 cases in testing set)

the proposed method was trained and tested

and its performance was compared with other methods.

Results

When compared to 2D and 3D networks

our 2.5D network (MMAE-Net) achieved the best segmentation performance in all evaluation indicators

with a dice similarity coefficient (DSC) of 81.25% and a sensitivity of 84.82%. MMAE-Net achieved better segmentation performance when compared to U-Net

ResU-Net

DenseU-Net

AttentionU-Net

Segmentation TRansformer (SETR)

and other classical methods and previous research. In addition

we also created a visual and automated clinical application system to improve the practical and promotive capability of methods.

Conclusion

Based on fusing the features from 2D and 3D network

a 2.5D multi-dimensional segmentation model MMAE-Net is developed

which has achieved excellent performance in the recognition of MRI small lesions and provides an effective solution for the diagnosis and treatment of AIS diseases.

关键词

Keywords

references

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