计算机科学 ›› 2020, Vol. 47 ›› Issue (8): 213-220.doi: 10.11896/jsjkx.190600026
所属专题: 医学图像
高强1, 高敬阳1, 赵地2
GAO Qiang1, GAO Jing-yang1, ZHAO Di2
摘要: 心血管疾病已成为威胁人类健康的头号杀手。目前, 医生们通过左心室MRI成像技术对左心室轮廓进行手工标注来计算心脏的各项功能参数, 以监测和预防心血管疾病, 但此方法的标注工作量大、耗时且繁琐。目前, 深度学习在许多医疗影像分割领域取得了显著的成功, 但在左心室轮廓分割领域仍有提升的空间。文中提出了一种基于组归一化与最近邻插值的MRI左心室轮廓精确分割网络——GNNI U-net(U-net with Group Normalization and Nearest Interpolation), 该网络利用组归一化方法构建了能够快速、准确提取特征信息的卷积模块, 基于最近邻插值法构建了用于特征信息还原的上采样模块。在Sunnybrook与LVSC两个左心室分割数据集上采用了中心裁减ROI提取的预处理方法, 并对GNNI U-net进行了充分的对比实验。所提网络在Sunnybrook数据集上获得了Dice系数为0.937以及Jaccard系数为0.893的精度。在LVSC数据集上获得了Dice系数为0.957以及Jaccard系数为0.921的精度。GNNI U-net在左心室轮廓分割领域取得了比现有卷积网络分割方法更高的Dice系数精度。最后, 进一步讨论并验证了组归一化操作卷积模块能够加速网络的收敛并提高分割精度;采用最近邻插值法的上采样模块对左心室轮廓这类较小目标的分割效果更好, 能够在一定程度上加速网络的收敛。
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[1]MOZAFFARIAN D, BENJAMIN E J, GO A S, et al.Heart Di-sease and Stroke Statistics-2015 Update:A Report From the American Heart Association[J].Circulation, 2015, 131(4):e29-e322. [2]LOW W Y, LEE Y K, SAMY A L.Non-communicable diseases in the Asia-Pacific region:Prevalence, risk factors and community-based prevention[J].International Journal of Occupational Medicine & Environmental Health, 2014, 28(1):1-7. [3]LISET VZQUEZ R, MARLY G F C, FRANCISCO P R, et al.Left ventricle segmentation in cardiac MRI images using fully convolutional neural networks[C]∥Medical Imaging 2017:Computer-Aided Diagnosis.International Society for Optics and Photonics, 2017:324-336. [4]TRAN P V.A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI[J].arXiv:1604.00494 . [5]TAN L K, LIEW Y M, LIM E, et al.Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences[J].Medical Image Analysis, 2017, 39:78-86. [6]PETITJEAN C, DACHER J N.A review of segmentation me-thods in short axis cardiac MR images[J].Medical Image Analysis, 2011, 15(2):169-184. [7]PENG P, LEKADIR K, GOOYA A, et al.A review of heartchamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging[J].Magma, 2016, 29(2):155-195. [8]TIAN J X, LIU G C, GU S S, et al.Deep Learning in Medical Image Analysis and Its Challenges[J].Acta Automatica Sinica, 2018, 44(3):401-424. [9]FENG C L.Research on Key Algorithms for Segmentation and Visualization of Cardiac Tissue[D].Shenyang:Northeastern University, 2014. [10]LONG J, SHELHAMER E, DARRELL T.Fully Convolutional Networks for Semantic Segmentation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 39(4):640-651. [11]RONNEBERGER O, FISCHER P, BROXT.U-Net:Convolu-tional Networks for Biomedical Image Segmentation[C]∥International Conference on Medical ImageComputing and Compu-ter-Assisted Intervention.Springer International Publishing, 2015:234-241. [12]LITJENS G, KOOI T, BEJNORDI B E, et al.A survey on deep learning in medical image analysis[J].Medical Image Analysis, 2017, 42:60-88. [13]ZHOU Z, SIDDIQUEE M R, TAJBAKHSH N, et al.UNet++:A Nested U-Net Architecture for Medical Image Segmentation[J].arXiv:1807.10165v1. [14]ZENG G, YANG X, LI J, et al.3D U-net with Multi-level Deep Supervision:Fully Automatic Segmentation of Proximal Femur in 3D MR Images[C]∥International Workshop on Machine Learning in Medical Imaging.Springer, Cham, 2017:274-282. [15]GARCIA-GARCIA A, ORTS-ESCOLANO S, OPREA S, Et al.A Review on Deep Learning Techniques Applied to Semantic Segmentation[J].arXiv:1704.06857v1. [16]ZHANG Q L, ZHAO D, CHI X B, et al.Review for Deep Learning Based on Medical Imaging Diagnosis[J].Computer Science, 2017, 44(S2):1-7. [17]WU Y, HE K.Group Normalization[J].arXiv:1803.08494v3. [18]IOFFE S, SZEGEDY C.Batch Normalization:Accelerating Deep Network Training by Reducing Internal Covariate Shift[J].ar-Xiv:1502.03167v3. [19]NOH H, HONG S, HAN B.Learning Deconvolution Network for Semantic Segmentation[J].arXiv:1505.04366v1. [20]BADRINARAYANAN V, KENDALL A, CIPOLLA R.Seg-Net:A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017:39(12):2481-2495. [21]LIN T Y, DOLLR, PIOTR, et al.Feature Pyramid Networks for Object Detection[J].arXiv:1612.03144v2. [22]FONSECA C G, BACKHAUS M, BLUEMKE D A, et al.The Cardiac Atlas Project--an imaging database for computational modeling and statistical atlases of the heart[J].Bioinformatics, 2011, 27(16):2288-2295. |
[1] | 康牧,李永亮. 一种基于弹性模型的图像放大算法 Image Zoom Algorithm Based on the Spring Model 计算机科学, 2009, 36(10): 292-295. |
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