Lightweight preprocessing and template matching facilitate streamlined ischemic myocardial scar classification

Michael H. Udin; Sara Armstrong; Alice Kai; Scott T. Doyle; Ciprian N. Ionita; Saraswati Pokharel; Umesh C. Sharma

doi:10.1117/1.JMI.11.2.024503

21 March 2024 Lightweight preprocessing and template matching facilitate streamlined ischemic myocardial scar classification

Michael H. Udin, Sara Armstrong, Alice Kai, Scott T. Doyle, Ciprian N. Ionita, Saraswati Pokharel, Umesh C. Sharma

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 2, 024503 (March 2024). https://doi.org/10.1117/1.JMI.11.2.024503

Abstract

Purpose

Ischemic myocardial scarring (IMS) is a common outcome of coronary artery disease that potentially leads to lethal arrythmias and heart failure. Late-gadolinium-enhanced cardiac magnetic resonance (CMR) imaging scans have served as the diagnostic bedrock for IMS, with recent advancements in machine learning enabling enhanced scar classification. However, the trade-off for these improvements is intensive computational and time demands. As a solution, we propose a combination of lightweight preprocessing (LWP) and template matching (TM) to streamline IMS classification.

Approach

CMR images from 279 patients (151 IMS, 128 control) were classified for IMS presence using two convolutional neural networks (CNNs) and TM, both with and without LWP. Evaluation metrics included accuracy, sensitivity, specificity, F1-score, area under the receiver operating characteristic curve (AUROC), and processing time. External testing dataset analysis encompassed patient-level classifications (PLCs) and a CNN versus TM classification comparison (CVTCC).

Results

LWP enhanced the speed of both CNNs (4.9x) and TM (21.9x). Furthermore, in the absence of LWP, TM outpaced CNNs by over 10x, while with LWP, TM was more than 100x faster. Additionally, TM performed similarly to the CNNs in accuracy, sensitivity, specificity, F1-score, and AUROC, with PLCs demonstrating improvements across all five metrics. Moreover, the CVTCC revealed a substantial 90.9% agreement.

Conclusions

Our results highlight the effectiveness of LWP and TM in streamlining IMS classification. Anticipated enhancements to LWP’s region of interest (ROI) isolation and TM’s ROI targeting are expected to boost accuracy, positioning them as a potential alternative to CNNs for IMS classification, supporting the need for further research.

Citation Download Citation

Michael H. Udin, Sara Armstrong, Alice Kai, Scott T. Doyle, Ciprian N. Ionita, Saraswati Pokharel, and Umesh C. Sharma "Lightweight preprocessing and template matching facilitate streamlined ischemic myocardial scar classification," Journal of Medical Imaging 11(2), 024503 (21 March 2024). https://doi.org/10.1117/1.JMI.11.2.024503

Received: 4 July 2023; Accepted: 7 March 2024; Published: 21 March 2024

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $24.00

Non-members: $28.00 ADD TO CART

JOURNAL ARTICLE
18 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image classification

Cardiovascular magnetic resonance imaging

Visualization

Image processing

Education and training

Photonic integrated circuits

Machine learning

Purpose

Approach

Results

Conclusions

Show All Keywords

Keywords/Phrases

Search In:

Publication Years