Automated Cardiovascular Pathology Assessment Using Semantic Segmentation and Ensemble Learning.
Cardiac magnetic resonance imaging provides high spatial resolution, enabling improved extraction of important functional and morphological features for cardiovascular disease staging. Segmentation of ventricular cavities and myocardium in cardiac cine sequencing provides a basis to quantify cardiac...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 3; pp. 607 - 613 |
|---|---|
| Autores principales: | , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2020
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143476521&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143476521 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2020 vid: 33 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143476521 143476521 143476521 10.1007/s10278-019-00197-0 143476521 ppf: 607 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated Cardiovascular Pathology Assessment Using Semantic Segmentation and Ensemble Learning. aug: au: Lindsey, Tony Lee, Jin-Ju affil: Intelligent Systems, NASA Ames Research Center, Room 250, M/S N269-2, 94035, Mountain View, CA, USA sug: subj: Cardiovascular Diseases Diagnosis Automation Magnetic Resonance Imaging Methods Image Processing, Computer Assisted Methods Machine Learning Human Neural Networks (Computer) Myocardium Pathology Heart Ventricle Pathology Cardiovascular Diseases Classification Academic Medical Centers France ab: Cardiac magnetic resonance imaging provides high spatial resolution, enabling improved extraction of important functional and morphological features for cardiovascular disease staging. Segmentation of ventricular cavities and myocardium in cardiac cine sequencing provides a basis to quantify cardiac measures such as ejection fraction. A method is presented that curtails the expense and observer bias of manual cardiac evaluation by combining semantic segmentation and disease classification into a fully automatic processing pipeline. The initial processing element consists of a robust dilated convolutional neural network architecture for voxel-wise segmentation of the myocardium and ventricular cavities. The resulting comprehensive volumetric feature matrix captures diagnostic clinical procedure data and is utilized by the final processing element to model a cardiac pathology classifier. Our approach evaluated anonymized cardiac images from a training data set of 100 patients (4 pathology groups, 1 healthy group, 20 patients per group) examined at the University Hospital of Dijon. The top average Dice index scores achieved were 0.940, 0.886, and 0.849 for structure segmentation of the left ventricle (LV), myocardium, and right ventricle (RV), respectively. A 5-ary pathology classification accuracy of 90% was recorded on an independent test set using the trained model. Performance results demonstrate the potential for advanced machine learning methods to deliver accurate, efficient, and reproducible cardiac pathological assessment. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|