电子学报 ›› 2022, Vol. 50 ›› Issue (10): 2433-2442.DOI: 10.12263/DZXB.20200984
张淑军, 彭中, 李辉
收稿日期:
2020-09-06
修回日期:
2021-12-04
出版日期:
2022-10-25
作者简介:
基金资助:
ZHANG Shu-jun, PENG Zhong, LI Hui
Received:
2020-09-06
Revised:
2021-12-04
Online:
2022-10-25
Published:
2022-10-11
摘要:
基于深度学习的生物医学图像分割由于其精度的提高,可以更好地辅助医生做精确的诊断.目前主流的基于U-Net的分割模型通过多层卷积进行局部特征的提取,缺失了全局信息,使分割过于局部化而产生误差.本文通过自注意力机制和分解卷积策略对U-Net模型进行改进,提出一种新的深度分割网络SAU-Net,使用自注意力模块增加全局信息,将原U-Net中的级联结构改为逐像素相加,减小维度,降低计算量;提出一种快速简洁的分解卷积方法,将传统卷积分解为两路一维卷积,并加入残差连接强化上下文信息.在BRATS和Kaggle两个脑肿瘤数据集上进行的实验结果表明,SAU-Net在参数量和Dice系数上都有更优的性能.
中图分类号:
张淑军, 彭中, 李辉. SAU-Net:基于U-Net和自注意力机制的医学图像分割方法[J]. 电子学报, 2022, 50(10): 2433-2442.
ZHANG Shu-jun, PENG Zhong, LI Hui. SAU-Net: Medical Image Segmentation Method Based on U-Net and Self-Attention[J]. Acta Electronica Sinica, 2022, 50(10): 2433-2442.
数据量 | 方法 | Train Dice loss | Train IoU | Test Dice loss | Test IoU |
---|---|---|---|---|---|
50例 | U-Net | 0.094 8 | 0.839 0 | 0.236 6 | 0.737 9 |
SAU-Net | 0.050 1 | 0.865 7 | 0.126 1 | 0.850 3 | |
210例 | U-Net | 0.058 9 | 0.857 4 | 0.185 5 | 0.790 1 |
SAU-Net | 0.052 0 | 0.866 8 | 0.171 5 | 0.802 3 |
表1 不同数据量下分割结果对比
数据量 | 方法 | Train Dice loss | Train IoU | Test Dice loss | Test IoU |
---|---|---|---|---|---|
50例 | U-Net | 0.094 8 | 0.839 0 | 0.236 6 | 0.737 9 |
SAU-Net | 0.050 1 | 0.865 7 | 0.126 1 | 0.850 3 | |
210例 | U-Net | 0.058 9 | 0.857 4 | 0.185 5 | 0.790 1 |
SAU-Net | 0.052 0 | 0.866 8 | 0.171 5 | 0.802 3 |
分割方法 | Train Dice Loss (均值±标准差) | Test Dice Loss (均值±标准差) |
---|---|---|
U-Net | 0.396 11±0.000 23 | 0.441 89±0.000 25 |
SAU-Net-1 | 0.362 89±0.000 29 | 0.458 29±0.000 43 |
SAU-Net-FCB-1(Concat) | 0.402 72±0.000 17 | 0.450 73±0.000 27 |
SAU-Net-FCB-1(Add) | 0.406 21±0.000 20 | 0.427 71±0.000 30 |
SAU-Net-FCB-2(Concat) | 0.362 81±0.000 32 | 0.469 14±0.000 40 |
SAU-Net-FCB-2(Add) | 0.370 14±0.000 20 | 0.467 17±0.000 39 |
表2 BRATS 2017 Flair单像分割消融实验
分割方法 | Train Dice Loss (均值±标准差) | Test Dice Loss (均值±标准差) |
---|---|---|
U-Net | 0.396 11±0.000 23 | 0.441 89±0.000 25 |
SAU-Net-1 | 0.362 89±0.000 29 | 0.458 29±0.000 43 |
SAU-Net-FCB-1(Concat) | 0.402 72±0.000 17 | 0.450 73±0.000 27 |
SAU-Net-FCB-1(Add) | 0.406 21±0.000 20 | 0.427 71±0.000 30 |
SAU-Net-FCB-2(Concat) | 0.362 81±0.000 32 | 0.469 14±0.000 40 |
SAU-Net-FCB-2(Add) | 0.370 14±0.000 20 | 0.467 17±0.000 39 |
模型 | 参数量 |
---|---|
U-Net | 39, 401, 026 |
SAU-Net | 40, 699, 523 |
SAU-Net-FCB-1(Concat) | 11, 087, 555 |
SAU-Net-FCB-1(Add) | 8, 273, 091 |
SAU-Net-FCB-2(Concat) | 11, 169, 796 |
SAU-Net-FCB-2(Add) | 8, 355, 332 |
表3 各模型参数量大小
模型 | 参数量 |
---|---|
U-Net | 39, 401, 026 |
SAU-Net | 40, 699, 523 |
SAU-Net-FCB-1(Concat) | 11, 087, 555 |
SAU-Net-FCB-1(Add) | 8, 273, 091 |
SAU-Net-FCB-2(Concat) | 11, 169, 796 |
SAU-Net-FCB-2(Add) | 8, 355, 332 |
模型 | Dice Loss | ||
---|---|---|---|
WT(均值±标准差) | TC(均值±标准差) | ET(均值±标准差) | |
U-Net | 0.069 57±0.006 7 | 0.086 11±0.010 6 | 0.081 59±0.009 2 |
SAU-Net-FCB-1(Add) | 0.068 53±0.006 2 | 0.054 78±0.008 0 | 0.056 27±0.010 5 |
表4 BRATS 2017的3项分割任务的Dice Loss
模型 | Dice Loss | ||
---|---|---|---|
WT(均值±标准差) | TC(均值±标准差) | ET(均值±标准差) | |
U-Net | 0.069 57±0.006 7 | 0.086 11±0.010 6 | 0.081 59±0.009 2 |
SAU-Net-FCB-1(Add) | 0.068 53±0.006 2 | 0.054 78±0.008 0 | 0.056 27±0.010 5 |
分割方法 | Train Dice Loss (均值±标准差) | Test Dice Loss (均值±标准差) |
---|---|---|
U-Net | 0.265 63±0.007 60 | 0.274 03±0.011 77 |
SAU-Net-1 | 0.239 88±0.007 58 | 0.276 76±0.008 41 |
SAU-Net-FCB-1(Add) | 0.238 56±0.008 87 | 0.272 58±0.011 19 |
表5 Kaggle LGG数据分割Dice Loss
分割方法 | Train Dice Loss (均值±标准差) | Test Dice Loss (均值±标准差) |
---|---|---|
U-Net | 0.265 63±0.007 60 | 0.274 03±0.011 77 |
SAU-Net-1 | 0.239 88±0.007 58 | 0.276 76±0.008 41 |
SAU-Net-FCB-1(Add) | 0.238 56±0.008 87 | 0.272 58±0.011 19 |
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