波谱学杂志 ›› 2023, Vol. 40 ›› Issue (2): 220-238.doi: 10.11938/cjmr20223013
• 综述评论 • 上一篇
收稿日期:
2022-08-12
出版日期:
2023-06-05
在线发表日期:
2022-11-08
通讯作者:
王远军
E-mail:yjusst@126.com
基金资助:
Received:
2022-08-12
Published:
2023-06-05
Online:
2022-11-08
Contact:
WANG Yuanjun
E-mail:yjusst@126.com
摘要:
随着全球老龄化的加剧与深度学习的发展,基于深度学习的阿尔兹海默症(AD)影像学分类成为当前的一个研究热点.本文首先阐述了AD影像学分类任务中常用的深度学习模型、评估标准及公开数据集;接着讨论了不同图像模态在AD影像学分类中的应用;然后着重探讨了应用于AD影像学分类的深度学习模型改进方法;进一步引入了对模型可解释性研究的探讨;最后总结并比较了文中提及的分类模型,归纳了与AD影像分类相关的大脑区域,并对该领域未来的研究方向进行了展望.
中图分类号:
钱程一,王远军. 基于深度学习的阿尔兹海默症影像学分类研究进展[J]. 波谱学杂志, 2023, 40(2): 220-238.
QIAN Chengyi,WANG Yuanjun. Research Progress on Imaging Classification of Alzheimer’s Disease Based on Deep Learning[J]. Chinese Journal of Magnetic Resonance, 2023, 40(2): 220-238.
表2
各分类模型综合比较
第一作者 | 分类任务 | 数据模态 | 数据集 | 测试方法 | 分类模型 | 准确率% | |||
---|---|---|---|---|---|---|---|---|---|
郁松[ | AD/HC | sMRI | 1015 HC 575 AD 1709 MCI | 训练集60% 验证集20% 测试集20% | 3D ResNet-101 | 97.425 | |||
Parmar[ | AD/HC | fMRI | 30 AD 30 HC | 训练集60% 验证集20% 测试集20% | 3D CNN | 94.58 | |||
Bi[ | AD/HC MCI/HC AD/MCI AD/HC/MCI | fMRI | 118 AD 295 HC 335 MCI | 五折交叉验证 | RNN,ELM | 91.3 (AUC) 80.5 (AUC) 82.4 (AUC) 84.7 (AUC) | |||
Yi??i?T[ | AD/HC MCI/HC | sMRI | 训练集 30 AD 316 HC 70 MCI | 测试集 46 AD+MCI 23 HC | / | 2D CNN | 83 82 | ||
Punjabi[ | AD/HC | PET sMRI+PET | 共1299 | / | 3D CNN | 85.15 92.34 | |||
Zhang[ | AD/HC pMCI/sMCI | sMRI | 200 AD 231 HC 172 pMCI 232 sMCI | 五折交叉验证 | 3D ResAttNet | 91.3 82.1 | |||
Qiu[ | AD/HC | sMRI+性别+年龄+MMSE | / | 训练集60% 验证集20% 测试集20% | FCN | 96.8 | |||
Deng[ | AD/HC | DTI+sMRI | 98 AD 100 HC | 训练集60% 验证集20% 测试集20% | CNN | 90.00 | |||
Marzban[ | AD/HC MCI/HC | DTI+sMRI | 115 AD 185 HC 106 MCI | 十折交叉验证 | CNN | 93.5 79.6 | |||
Kang[ | EMCI/HC | DTI+sMRI | 50 HC 70 EMCI | 训练集80% 测试集20% | VGG-16,LASSO | 94.2 | |||
Kwak[ | AD/HC sMCI/pMCI | sMRI | 110 AD 109 HC 34 pMCI 81 sMCI | 五折交叉验证 | DenseNet | 93.75 73.90 | |||
Ju[ | MCI/HC | fMRI | 91 MCI 79 HC | 十折交叉验证 | AE | 86.47 | |||
Baydargil[ | AD/MCI/HC | PET | 141 AD 105 MCI 70 HC | 训练集80% 验证集10% 测试集10% | CAE | 98.67 | |||
Guan[ | AD/HC pMCI/sMCI | sMRI | 384 AD 392 HC 401 sMCI 197 pMCI | 训练集 90% 测试集 10% | ResNet、pABN | 90.7 79.3 | |||
Li[ | AD/MCI/HC | MRI | 237 AD 288 MCI 262 HC | 训练集65% 测试集 35% | ResNet-200 (迁移学习) | 83 | |||
Basaia[ | AD/HC sMCI/pMCI | sMRI | 294 AD 352 HC 253 pMCI 510 sMCI | 训练集90% 测试集10% | 3D FCN(迁移学习) | 99.2 75.1 | |||
孔伶旭[ | EMCI/HC | fMRI | 32 HC 32 EMCI | 五折交叉验证 | Mobilenet(迁移学习) | 73.67 | |||
Bin[ | AD/HC | sMRI | 100 AD 100 HC | 五折交叉验证 | Inception-v3 (迁移学习) | 99.45 | |||
Massalimova[ | AD/MCI/HC | DTI | 训练集 59 AD 308 HC 7 MCI | 测试集 16 AD 74 HC 1 MCI | / | ResNet-18(迁移学习) | 97 | ||
Mehmood[ | AD/HC EMCI/LMCI | sMRI | 75 AD 85 HC 70 EMCI 70 LMCI | 训练集64% 验证集16% 测试集20% | VGG-19(迁移学习) | 95.33 83.72 | |||
Raju[ | 非常轻度痴呆/轻度痴呆/中度痴呆/HC(四分类) | sMRI | 1013非常轻度痴呆 896轻度痴呆 64中度痴呆 3200 HC(训练集) | 334非常轻度痴呆 139轻度痴呆 10中度痴呆 530 HC(测试集) | VGG-16(迁移学习) | 99 | |||
Lian[ | AD/HC sMCI/pMCI | sMRI | 数据集1 199 AD 229 HC 167 pMCI 226 sMCI | 数据集2 159 AD 200 HC 38 pMCI 239 sMCI | 两个数据集间 交叉验证 | H-FCN(迁移学习) | 90.3 80.9 | ||
Oh[ | AD/HC sMCI/pMCI | sMRI | 198 AD 230 HC 166 pMCI 101 sMCI | 五折交叉验证 | 3D CNN,ICAE (迁移学习) | 88.6 73.95 | |||
金祝新[ | AD/MCI MCI/HC AD/HC | sMRI | 267 AD 574 HC 446 MCI | 训练集90% 测试集10% | 3D CNN(迁移学习) | 94.6 92.5 90.9 | |||
曾安[ | AD/HC pMCI/HC pMCI/sMCI | sMRI | 137 AD 162 HC 76 pMCI 134 sMCI | 五折交叉验证 | 2D CNN(集成学习) | 81 79 62 | |||
Bi[ | AD/HC MCI/HC AD/MCI AD/MCI/HC | sMRI | 243 AD 307 HC 525 MCI | / | PCANet,k-means (集成学习) | 89.15 92.6 97.01 91.25 | |||
Choi[ | AD/HC | sMRI | 715 AD 335 HC 305 MCI | 训练集60% 验证集20% 测试集20% | VGG-16,GoogLeNet,AlexNet(集成学习) | 93.84 | |||
Venugopalan[ | AD/HC | sMRI+电子病历+基因数据 | 共220 | 十折交叉验证 | AE,CNN,随机森林 (集成学习) | 88 | |||
Zeng[ | sMCI/pMCI AD/HC sMCI/HC pMCI/HC AD/sMCI AD/pMCI | sMRI | 92 AD 92 HC 92 sMCI 95 pMCI | 训练集70% 测试集30% | PCA,DBN(多任务) | 87.78 98.62 92.31 96.67 99.62 91.89 | |||
Spasov[ | sMCI/pMCI | sMRI | 192 AD 184 HC 409 MCI | 十折交叉验证 | CNN(多任务) | 86 | |||
Liu[ | AD/HC MCI/HC | sMRI | 97 AD 11 HC 233 MCI | 五折交叉验证 | V-Net,DenseNet (多任务) | 88.9 76.2 | |||
Abuhmed[ | AD/MCI/HC | PET+sMRI+神经心理学数据+神经病理学数据+认知评分 | 共1371 | 十折交叉验证 | BiLSTM,随机森林 (多任务) | 84.95 |
表3
可解释性方法及AD相关脑区
第一作者 | 任务 | 分类模型 | 准确率% | 解释性方法 | 脑区 |
---|---|---|---|---|---|
Guan[ | AD/HC sMCI/pMCI | ResNet、pABN | 90.7 79.3 | CAM | 海马体、杏仁核、脑室、额叶、颞下回、颞上沟、顶枕沟、外侧裂 |
Zhang[ | AD/HC | 3D-ResAttNet | 91.3 | Grad-CAM | 海马体、侧脑室、大部分皮质 |
Raju[ | 轻度痴呆/非常轻度痴呆/中度痴呆/HC(四分类) | VGG-16(迁移学习) | 99 | Grad-CAM | 海马体、杏仁核、顶叶 |
Oh[ | AD/HC sMCI/pMCI | 3D-CNN,ICAE | 86.6 73.95 | CSV | 内侧颞叶周围、左侧海马体 左侧杏仁核、角回、楔前回 |
Qiu[ | AD/HC | FCN | 96.8 | 基于斑块生成热力图 | 海马、中额叶、杏仁核、颞叶 |
金祝新[ | AD/HC | 3D-CNN(迁移学习) | 90.9 | 输入添加遮挡块 | 内侧颞叶、海马体 |
Kwak[ | sMCI/pMCI | DenseNet | 73.90 | 输入添加遮挡块 | 海马、梭状回、颞下回、楔前叶 |
Venugopalan[ | AD/HC | AE,CNN,随机森林 | 88 | 特征屏蔽 | 海马体、杏仁核 |
Shahamat[ | AD/HC | 3D-CNN | 85 | 遗传算法选取脑模板 | 左侧枕叶、左侧颞梭状皮层、右侧楔皮层、右额中回、右颞中回 |
表A1
中英文全称及对应缩写表
缩写 | 中文全称 | 英文全称 |
---|---|---|
AD | 阿尔兹海默症 | Alzheimer disease |
NFT | 神经原纤维缠结 | neurofibrillary tangles |
CDR | 临床痴呆分级 | clinical dementia rating |
MMSE | 简易智力状态检查量表 | mini-mental state examination |
MCI | 轻度认知障碍 | mild cognitive impairment |
EMCI | 早期轻度认知障碍 | early mild cognitive impairment |
LMCI | 晚期轻度认知障碍 | later mild cognitive impairment |
sMCI | 稳定型轻度认知障碍 | stable mild cognitive impairment |
pMCI | 渐近型轻度认知障碍 | progressive mild cognitive impairment |
HC | 健康对照组 | healthy control |
ADNI | 阿尔兹海默症神经影像学计划 | Alzheimer’s disease neuroimaging initiative |
OASIS | 开放存取影像研究 | open access series of imaging studies |
IXI | 从图像中提取信息数据集 | information eXtraction from images |
sMRI | 结构磁共振成像 | structural magnetic resonance imaging |
PET | 正电子发射型计算机断层显像 | positron emission computed tomography |
DTI | 扩散张量成像 | diffusion tensor imaging |
fMRI | 功能磁共振成像 | functional magnetic resonance imaging |
FA | 各向异性分数 | fractional anisotropy |
MD | 平均扩散率 | mean diffusivity |
RD | 径向扩散系数 | radial diffusivity |
DC | 中心度特征 | degree centrality |
ROI | 感兴趣区域 | region of interest |
AAL | 自动解剖标记 | automated anatomical labeling |
TP | 真阳性 | true positive |
FP | 假阳性 | false positive |
TN | 真阴性 | true negative |
FN | 假阴性 | false negative |
ROC | 接受者操作特征曲线 | receiver operating characteristic curve |
AUC | 曲线下面积 | area under curve |
CNN | 卷积神经网络 | convolutional neural networks |
ILSVRC | ImageNet大规模视觉识别挑战赛 | ImageNet large scale visual recognition challenge |
RNN | 循环神经网络 | recurrent neural network |
BRNN | 双向循环神经网络 | bidirectional recurrent neural network |
AE | 自动编码机 | autoencoder |
CAE | 卷积自动编码机 | convolutional autoencoder |
ICAE | 带Inception模块的卷积自动编码机 | inception modal based convolutional autoencoder |
ResNet | 残差网络 | residual network |
DenseNet | 密集连接网络 | densely connected convolutional network |
pABN | 并行注意力增强双线性网络 | parallel attention-augmented bilinear network |
k-means | k均值聚类 | k-means clustering algorithm |
PCA | 主成分分析法 | principal components analysis |
ELM | 极限学习机 | extreme learning machine |
H-FCN | 分层全卷积神经网络 | hierarchical fully convolutional network |
FCN | 全卷积神经网络 | fully convolutional network |
ADAS-cog | 阿尔兹海默症评定量表-认知分量表 | Alzheimer’s disease assessment scale |
BiLSTM | 双向长短期记忆网络 | bidirectional long short-term memory |
MAE | 平均绝对误差 | mean absolute error |
CAM | 类激活映射 | class activation mapping |
Grad-CAM | 梯度加权类激活映射 | gradient-weighted class activation mapping |
GAP | 全局平均池化 | global average pooling |
CSV | 类显著映射 | class saliency visualization |
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