波谱学杂志 ›› 2024, Vol. 41 ›› Issue (2): 224-244.doi: 10.11938/cjmr20233086
• 综述评论 • 上一篇
收稿日期:
2023-10-19
出版日期:
2024-06-05
在线发表日期:
2023-12-27
通讯作者:
*Tel:021-55271116, E-mail:lijiawangmri@163.com.
CHANG Bo, SUN Haoyun, GAO Qingyu, WANG Lijia*()
Received:
2023-10-19
Published:
2024-06-05
Online:
2023-12-27
Contact:
*Tel:021-55271116, E-mail:lijiawangmri@163.com.
摘要:
随着老龄化加剧,心血管疾病患病人数逐年增加,借助医学图像实现心脏功能的评估在诊疗过程中起着重要作用.心脏分割是评估心脏功能的前提,一直受到临床医生和科学研究者的密切关注.本文从传统方法和深度学习方法角度梳理了近十年以来关于心脏分割研究的文献.重点介绍了基于主动轮廓和图谱模型的传统分割方法,以及基于U-Net和全卷积神经网络(FCN)的深度学习算法.其中针对通过增加局部模块、优化损失函数、强化网络结构等方式改进深度学习网络以实现心脏特定区域精准分割这一主题进行了详细展开,并从心脏磁共振、X射线计算机断层扫描(CT)和超声3种成像模态对上述方法进行总结.最后总结了该领域目前的研究现状并对未来研究方向进行了展望.
中图分类号:
常博, 孙灏芸, 高清宇, 王丽嘉. 传统方法和深度学习用于不同模态心脏医学图像的分割研究进展[J]. 波谱学杂志, 2024, 41(2): 224-244.
CHANG Bo, SUN Haoyun, GAO Qingyu, WANG Lijia. Research Progress on Cardiac Segmentation in Different Modal Medical Images by Traditional Methods and Deep Learning[J]. Chinese Journal of Magnetic Resonance, 2024, 41(2): 224-244.
表2
基于CMRI图像的分割网络总结
方法 | 时间 | 学习框架 | 数据集 | Dice系数 | Hausdorff距离(HD)/mm | |||||
---|---|---|---|---|---|---|---|---|---|---|
左心室 (LV) | 右心室 (RV) | 心肌 (Myo) | 左心室 (LV) | 右心室 (RV) | 心肌 (Myo) | |||||
Active contour models[ | 2019 | TensorFlow | ACDC | 0.986 | 0.940 | 0.969 | 4.73 | 5.95 | 5.42 | |
MSU-Net[ | 2019 | TensorFlow | ACDC | 0.897 | 0.855 | 0.836 | - | - | - | |
3D high resolution[ | 2019 | TensorFlow | 1912例临床数据 | 0.8792 | - | - | 3.99 | - | ||
Dynamic pixel-wise weighting-FCN[ | 2020 | TensorFlow | MICCAI 2013 | - | - | 0.803 | - | - | - | |
FCN for left ventricle segmentation[ | 2020 | - | MICCAI2009 33例临床数据 | 0.95 | - | 0.914 | - | - | - | |
CNN incorporating domain-specific constraints[ | 2020 | TensorFlow | ACDC | 0.959 | 0.924 | 0.873 | - | - | - | |
Combined CNN and U-net[ | 2020 | PyTorch | MICCAI2009 | 0.951 | - | - | 3.641 | - | - | |
Automatic segmentation and quantification[ | 2020 | PyTorch | ACDC、临床数据 | 0.96 | - | 0.88 | 6.31 | - | 7.11 | |
Fully automatic segmentation of RV and LV[ | 2020 | PyTorch | ACDC、5570例 临床数据 | 0.927 | 0.873 | - | - | - | - | |
Deep CNN[ | 2020 | TensorFlow | MICCAI2009 | 0.961 | 0.949 | 0.867 | - | - | - | |
DMU-net[ | 2020 | Keras | 71例临床数据 | - | - | - | - | 4.445 | - | |
Semi-supervised[ | 2021 | PyTorch | M&Ms | 0.909 | 0.879 | 0.845 | 9.42 | 12.65 | 11.85 | |
Active contour models[ | 2021 | - | ACDC、LVQuan18 | 0.890 0.805 | - | - | 12.247 19.717 | - | - | |
Deep reinforcement learning[ | 2021 | - | ACDC、 Sunnybrook2009 | 0.9502 0.9351 | - | - | - | - | - | |
Attention guided U-Net[ | 2021 | TensorFlow | LVSC | - | - | 0.956 | - | - | 1.456 | |
Dens FCN[ | 2021 | TensorFlow | 210例临床数据 | 0.944 | 0.908 | 0.851 | 7.2 | 7.35 | 5.9 | |
SegNet[ | 2022 | TensorFlow | 1354例临床数据 | 0.878 | - | - | 10.163 | - | - | |
FCN[ | 2022 | - | 150例临床数据 | 0.930 | - | - | - | - | - | |
Cascade approach structures[ | 2022 | TensorFlow | ACDC | 0.963 | 0.900 | 0.894 | 8.062 | 14.660 | 7.906 | |
DEU-Net2.0[ | 2022 | PyTorch | ACDC | 0.970 | 0.949 | 0.904 | 7.0 | 12.2 | 9.0 | |
RNN with Atrous Spatial pyramid pooling[ | 2022 | PyTorch | 56例临床数据 | - | - | 0.8543 | - | - | - | |
OSFNet[ | 2022 | TensorFlow | ACDC | 0.946 | - | - | 3.976 | - | - | |
Deep Atlas network[ | 2023 | TensorFlow | 71例临床数据 | - | 0.902 | - | - | 4.358 | - |
表3
基于心脏CT图像的分割网络总结
方法 | 时间 | 学习框架 | 数据集 | Dice系数 | Hausdorff距离(HD)/mm | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
左心室 (LV) | 右心室 (RV) | 心肌 (Myo) | 左心室 (LV) | 右心室 (RV) | 心肌 (Myo) | |||||||
CNN[ | 2016 | - | 60例临床病例 | 0.85 | - | - | - | - | - | |||
Combining faster R-CNN and U-net[ | 2018 | PyTorch | MM-WHS2017 | 0.879 | 0.902 | 0.822 | - | - | - | |||
CNN[ | 2018 | TensorFlow | 11例临床病例 | 0.878 | 0.829 | - | - | - | - | |||
Hybrid loss guided CNN[ | 2018 | TensorFlow | MM-WHS2017 | 0.8680 | 0.7143 | 0.665 | - | - | - | |||
CNN and anatomical label configurations[ | 2018 | Caffe | MM-WHS2017 | 0.918 | 0.909 | 0.881 | - | - | - | |||
3D deeply-supervised U-Net[ | 2018 | - | MM-WHS2017 | 0.893 | 0.810 | 0.837 | - | - | - | |||
DL and shape context[ | 2018 | Keras | MM-WHS2017 | 0.935 | 0.825 | 0.879 | - | - | - | |||
Multi-planar deep segmentation networks[ | 2018 | TensorFlow | MM-WHS2017 | 0.904 | 0.883 | 0.851 | - | - | - | |||
3D CNN[ | 2018 | TensorFlow | MM-WHS2017 | 0.923 | 0.857 | 0.856 | - | - | - | |||
Two-stage 3D U-net[ | 2018 | TensorFlow | MM-WHS2017 | 0.800 | 0.786 | 0.729 | - | - | - | |||
Multi-depth fusion network[ | 2019 | TensorFlow | MICCAI 2017全心 CT数据集 | 0.944 | 0.895 | 0.889 | - | - | - | |||
3D deeply supervised attention U-net[ | 2020 | MATLAB | 100例临床病例 | 0.916 | - | - | 6.840 | - | - | |||
DL[ | 2020 | - | 1100例临床数据 | - | - | 0.883 | - | - | 13.4 | |||
Unet-GAN[ | 2021 | PyTorch | MM-WHS2017 | 整体平均0.889 | ||||||||
Multiple GAN guided by Self-attention mechanism[ | 2021 | - | MM-WHS2017 | 0.814 | - | 0.669 | - | - | - | |||
AttU_Net_conv1_5Mffp[ | 2021 | PyTorch | MM-WHS2017 | 0.907 | 0.842 | 0.906 | - | - | - | |||
PC-Unet[ | 2021 | - | 20例临床数据 | 0.885 | - | - | 7.05 | - | - | |||
Computer graphics imaging and DL[ | 2022 | - | 130例临床数据 | - | 0.81~0.95 | - | - | - | - | |||
DRLSE[ | 2022 | - | 5例临床数据 | 0.9253 | - | - | 7.874 | - | - | |||
4D contrast-enhanced[ | 2022 | PyTorch | 1509例临床数据 | 整体平均0.8 | - | - | - | |||||
MRDFF[ | 2022 | - | MM-WHS2017 | 0.899 | 0.823 | - | - | - | - | |||
Transnunet[ | 2022 | - | MM-WHS2017 | 0.921 | - | - | - | - | - | |||
Self-attention mechanism[ | 2023 | TensorFlow | 96例临床病例 | - | - | 0.9202 | - | - | - |
表4
基于UCG图像的分割网络总结
方法 | 时间 | 学习框架 | 数据集 | Dice系数 | Hausdorff距离(HD)/mm | |||||
---|---|---|---|---|---|---|---|---|---|---|
左心室 (LV) | 右心室 (RV) | 心肌 (Myo) | 左心室 (LV) | 右心室 (RV) | 心肌 (Myo) | |||||
Multi-domain regularized[ | 2016 | Caffe | 42894张图像 | 0.890 | - | - | - | - | - | |
CNN[ | 2016 | MATLAB | 51例临床数据 | 0.945 | - | - | 1.2648 | - | - | |
Deep generative models[ | 2017 | TensorFlow | 566例临床数据 | 0.936 | ||||||
Anatomically CNN[ | 2017 | - | UK Digital Heart、 CETUS、ACDC | 0.939 | - | 0.811 | 7.89 | - | 7.12 | |
Shape-guided deformable model driven by FCN[ | 2018 | Keras | 69例临床数据 | 0.86 | - | - | - | - | - | |
Recurrent FCN and optical flow[ | 2018 | TensorFlow | 556例临床病例 | 0.927 | - | - | - | - | - | |
Multi-structure segmentation[ | 2018 | - | 500例临床数据 | 0.868 | - | - | 14.3 | - | - | |
VoxelAtlasGAN[ | 2018 | PyTorch | 60例临床病例 | 0.953 | - | - | 7.26 | - | - | |
Automatic biplane[ | 2019 | TensorFlow | 427例临床病例 | 0.92 | - | - | - | - | - | |
CNN with the active shape model[ | 2019 | MATLAB | 30例临床数据 | 0.919 | - | - | 6.38 | - | - | |
Time-series information[ | 2020 | TensorFlow | 211例临床病例 | 0.695 | - | - | - | - | - | |
Beat-to-beat assessment[ | 2020 | PyTorch | EchoNet-Dynamic | 0.92 | - | - | - | - | - | |
DPS-Net[ | 2020 | PyTorch | 10858例临床数据 | 0.935 | - | - | 5.51 | - | - | |
Deep pyramid local[ | 2021 | PyTorch | CAMUS | 0.962 | - | - | 4.6 | - | - | |
3D ultrasound evaluation[ | 2022 | TensorFlow | 26例临床数据 | 0.82 | - | - | 6.78 | - | - | |
Contrastive pretraining[ | 2022 | - | CAMUS | 0.9252 | - | - | - | - | - | |
Label-free segmentation[ | 2022 | TensorFlow、 Keras | 18873例 | 0.83 | - | - | - | - | - | |
Lightweight network[ | 2022 | MATLAB | 2262例 | 0.902 | - | - | - | - | - | |
GUDU[ | 2023 | - | CAMUS | 0.946 | - | - | 4.7 | - | - | |
Knowledge fusion[ | 2023 | - | EchoNet-Dynamic、 150例临床数据 | 0.908 | - | - | 6.56 | - | - |
表5
心脏图像公开数据集
数据集 | 时间 | 模态 | 病例 | 网址 |
---|---|---|---|---|
Sunnybrook 2009 | 2009 | CMRI | 45 | https://www.cardiacatlas.org/sunnybrook-cardiac-data/ |
MESA | 2011 | CMRI | 2450 | https://www.cardiacatlas.org/mesa/ |
DETERMINE | 2011 | CMRI | 30 | https://www.cardiacatlas.org/determine/ |
MITEA | 2012 | CMRI | 134 | https://www.cardiacatlas.org/mitea/ |
CDEMRIS | 2012 | CMRI | 60 | https://www.imperial.ac.uk/collegedirectory/ |
LVIC | 2012 | CMRI | 30 | https://www.doc.ic.ac.uk/~rkarim/la_lv_framework/ |
SADACB | 2015 | CMRI | 1000 | https://www.kaggle.com/competitions/second-annual-data-science-bowl/overview |
HVSMR | 2016 | CMRI | 30 | http://segchd.csail.mit.edu/ |
ACDC | 2017 | CMRI | 150 | https://acdc.creatis.insa-lyon.fr/ |
LASC’18 | 2018 | CMRI | 154 | https://www.cardiacatlas.org/atriaseg2018-challenge/atria-seg-data/ |
M&Ms | 2020 | CMRI | 375 | https://www.ub.edu/mnms/ |
CMRxMotion | 2022 | CMRI | 360 | https://www.synapse.org/#!Synapse:syn28503327/files/ |
LAScarQS | 2022 | CMRI | 194 | https://zmiclab.github.io/projects/lascarqs22/ |
CMRxRecon | 2023 | CMRI | 300 | https://cmrxrecon.github.io/ |
CAT08 | 2008 | CTA | 32 | https://disk.yandex.ru/d/LR-C42NwDC7RRA |
MM-WHS | 2017 | CT/CMRI | 60/60 | https://mega.nz/folder/UNMF2YYI#1cqJVzo4p_wESv9P_pc8uA |
ASOCA | 2020 | CT | 40 | https://asoca.grand-challenge.org/access/ |
CETUS | 2014 | UCG | 45 | https://www.creatis.insa-lyon.fr/Challenge/CETUS/databases.html |
CAMUS | 2019 | UCG | 500 | https://www.creatis.insa-lyon.fr/Challenge/camus |
EchoNet-Dynamic | 2020 | UCG | 10030 | https://echonet.github.io/dynamic/index.html |
MITEA | 2023 | UCG | 536 | https://www.frontiersin.org/articles/10.3389/fcvm.2022.1016703/full |
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