A Domain-Shift Invariant CNN Framework for Cardiac MRI Segmentation Across Unseen Domains.
The emergence of various deep learning approaches in diagnostic medical image segmentation has made machines capable of accomplishing human-level accuracy. However, the generalizability of these architectures across patients from different countries, Magnetic Resonance Imaging (MRI) scans from disti...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 5; pp. 2148 - 2164 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2023
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| 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=171950877&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 171950877 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2023 vid: 36 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 171950877 164796896 171950877 171950877 10.1007/s10278-023-00873-2 171950877 ppf: 2148 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Domain-Shift Invariant CNN Framework for Cardiac MRI Segmentation Across Unseen Domains. aug: au: Patil, Sanjeet S. Ramteke, Manojkumar Verma, Mansi Seth, Sandeep Bhargava, Rohit Mittal, Shachi Rathore, Anurag S. affil: https://ror.org/049tgcd06 Department of Chemical Engineering, Indian Institute of Technology, Delhi, New Delhi, India sug: subj: Magnetic Resonance Imaging Deep Learning Conceptual Framework Neural Networks (Computer) Heart Human Ventricular Ejection Fraction Systole India Stroke Volume ab: The emergence of various deep learning approaches in diagnostic medical image segmentation has made machines capable of accomplishing human-level accuracy. However, the generalizability of these architectures across patients from different countries, Magnetic Resonance Imaging (MRI) scans from distinct vendors, and varying imaging conditions remains questionable. In this work, we propose a translatable deep learning framework for diagnostic segmentation of cine MRI scans. This study aims to render the available SOTA (state-of-the-art) architectures domain-shift invariant by utilizing the heterogeneity of multi-sequence cardiac MRI. To develop and test our approach, we curated a diverse group of public datasets and a dataset obtained from private source. We evaluated 3 SOTA CNN (Convolution neural network) architectures i.e., U-Net, Attention-U-Net, and Attention-Res-U-Net. These architectures were first trained on a combination of three different cardiac MRI sequences. Next, we examined the M&M (multi-center & mutli-vendor) challenge dataset to investigate the effect of different training sets on translatability. The U-Net architecture, trained on the multi-sequence dataset, proved to be the most generalizable across multiple datasets during validation on unseen domains. This model attained mean dice scores of 0.81, 0.85, and 0.83 for myocardial wall segmentation after testing on unseen MyoPS (Myocardial Pathology Segmentation) 2020 dataset, AIIMS (All India Institute of Medical Sciences) dataset and M&M dataset, respectively. Our framework achieved Pearson's correlation values of 0.98, 0.99, and 0.95 between the observed and predicted parameters of end diastole volume, end systole volume, and ejection fraction, respectively, on the unseen Indian population dataset. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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