Pathological Cluster Identification by Unsupervised Analysis in 3,822 UK Biobank Cardiac MRIs

We perform unsupervised analysis of image-derived shape and motion features extracted from 3,822 cardiac Magnetic resonance imaging (MRIs) of the UK Biobank. First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion. Second, a feature selection is performed to remove highly correlated feature pairs. Third, clustering is carried out using a Gaussian mixture model on the selected features. After analysis, we identify 2 small clusters that probably correspond to 2 pathological categories. Further confirmation using a trained classification model and dimensionality reduction tools is carried out to support this finding. Moreover, we examine the differences between the other large clusters and compare our measures with the ground truth.

[1]  Max A. Viergever,et al.  Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images , 2017, STACOM@MICCAI.

[2]  Dorin Comaniciu,et al.  Cardiac Anchoring in MRI through Context Modeling , 2010, MICCAI.

[3]  M. Friedberg,et al.  Interpretation of Left Ventricular Diastolic Dysfunction in Children With Cardiomyopathy by Echocardiography: Problems and Limitations , 2013, Circulation. Cardiovascular imaging.

[4]  Simona Moldovanu,et al.  Threshold selection for classification of MR brain images by clustering method , 2015 .

[5]  Klaus H. Maier-Hein,et al.  Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges , 2017, Lecture Notes in Computer Science.

[6]  Arthur W. Toga,et al.  The Alzheimer's Disease Neuroimaging Initiative informatics core: A decade in review , 2015, Alzheimer's & Dementia.

[7]  Xiaoguang Lu,et al.  Automatic Segmentation of the Myocardium in Cine MR Images Using Deformable Registration , 2011, STACOM.

[8]  D. Holdstock.  Past, present--and future? , 2005, Medicine, conflict, and survival.

[9]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[10]  Hervé Delingette,et al.  Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow , 2018, Medical Image Anal..

[11]  Marleen de Bruijne,et al.  Machine learning approaches in medical image analysis: From detection to diagnosis , 2016, Medical Image Anal..

[12]  Nicholas Ayache,et al.  3-D Consistent and Robust Segmentation of Cardiac Images by Deep Learning With Spatial Propagation , 2018, IEEE Transactions on Medical Imaging.

[13]  Olivier Commowick,et al.  Imaging biomarkers in multiple Sclerosis: From image analysis to population imaging , 2016, Medical Image Anal..

[14]  J. Tait,et al.  Challenges and opportunities. , 1996, Journal of psychiatric and mental health nursing.

[15]  Ben Glocker,et al.  Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease , 2018, Medical Image Anal..

[16]  Elias Provencio-Vasquez,et al.  A decade in review , 2010 .

[17]  Carmel Hayes,et al.  Fully-automated left ventricular mass and volume MRI analysis in the UK Biobank population cohort: evaluation of initial results , 2017, The International Journal of Cardiovascular Imaging.

[18]  Holger Roth,et al.  Unsupervised segmentation of 3D medical images based on clustering and deep representation learning , 2018, Medical Imaging.

[19]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[20]  Alfonso Arellano,et al.  Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering , 2017, Journal of healthcare engineering.

[21]  Hervé Delingette,et al.  3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation , 2018, ArXiv.

[22]  Milind E. Rane,et al.  Clustering Techniques for Brain Tumor Detection , 2014 .

[23]  Jürgen Weese,et al.  Four challenges in medical image analysis from an industrial perspective , 2016, Medical Image Anal..

[24]  Ganapathy Krishnamurthi,et al.  Fully convolutional multi‐scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers , 2018, Medical Image Anal..

[25]  Ronald M. Summers,et al.  Medical Image Data and Datasets in the Era of Machine Learning—Whitepaper from the 2016 C-MIMI Meeting Dataset Session , 2017, Journal of Digital Imaging.

[26]  Qiao Zheng,et al.  Deep Learning for Robust Segmentation and Explainable Analysis of 3D and Dynamic Cardiac Images. (Apprentissage Profond pour la Segmentation Robuste et l'Analyse Explicable des Images Cardiaques Volumiques et Dynamiques) , 2019 .

[27]  Jan-Willem Romeijn,et al.  ‘All models are wrong...’: an introduction to model uncertainty , 2012 .

[28]  Khalid Raza,et al.  A Tour of Unsupervised Deep Learning for Medical Image Analysis , 2018, Current medical imaging.

[29]  Ganapathy Krishnamurthi,et al.  Densely Connected Fully Convolutional Network for Short-Axis Cardiac Cine MR Image Segmentation and Heart Diagnosis Using Random Forest , 2017, STACOM@MICCAI.

[30]  Alistair A. Young,et al.  Cardiac image modelling: Breadth and depth in heart disease , 2016, Medical Image Anal..

[31]  C. Sudlow,et al.  Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population , 2017, American journal of epidemiology.

[32]  Dinggang Shen,et al.  Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis , 2018, IEEE Journal of Biomedical and Health Informatics.

[33]  Stefan K. Piechnik,et al.  Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort , 2017, Journal of Cardiovascular Magnetic Resonance.

[34]  P. Matthews,et al.  UK Biobank’s cardiovascular magnetic resonance protocol , 2015, Journal of Cardiovascular Magnetic Resonance.

[35]  Michael Mitzenmacher,et al.  Detecting Novel Associations in Large Data Sets , 2011, Science.

[36]  Daisuke Komura,et al.  Machine Learning Methods for Histopathological Image Analysis , 2017, Computational and structural biotechnology journal.

[37]  Ben Glocker,et al.  Learning clinically useful information from images: Past, present and future , 2016, Medical Image Anal..

[38]  George Lee,et al.  Image analysis and machine learning in digital pathology: Challenges and opportunities , 2016, Medical Image Anal..

[39]  Alejandro F. Frangi,et al.  High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort , 2018, STACOM@MICCAI.

[40]  Dimitris N. Metaxas,et al.  Large-Scale medical image analytics: Recent methodologies, applications and Future directions , 2016, Medical Image Anal..

[41]  Christopher. Simons,et al.  Machine learning with Python , 2017 .

[42]  Anil K. Jain,et al.  Encyclopedia of Biometrics , 2015, Springer US.

[43]  Douglas A. Reynolds,et al.  Gaussian Mixture Models , 2018, Encyclopedia of Biometrics.

[44]  Oscar Camara,et al.  A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI , 2017, STACOM@MICCAI.