Mohamed Elbayumi

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Mohammed S.M. Elbaz

Northwestern University

Maurice Pradella

University of Basel

Ulaş Bağcı

Intel (United States)

Zachary Zilber

Northwestern University

Philip Greenland

Northwestern University

James Carr

Northwestern University

Melika Shafeghat

Tehran University of Medical Sciences

Mehri Mehrnia

Northwestern University

Cooperative Institutions

Northwestern University

University of Basel

University of Calgary

Northwestern University

University Hospital of Basel

Siemens Healthcare (Germany)

Hospital Base

Northwestern Medicine

Intel (United States)

University of Central Florida

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Relative Vortex Strength and Energetics Associate with Pulmonary Artery Pressure and Cardiac Function in Pulmonary Arterial Hypertension

Journal of Cardiovascular Magnetic Resonance (2024)

Melika Shafeghat Mohamed Elbayumi James Carr Mohammed S.M. Elbaz

Angiology

10.1016/j.jocmr.2024.100905

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Healthy-to-patients deep learning for automated left atrial segmentation and function analysis from short-axis cine CMR

Journal of Cardiovascular Magnetic Resonance (2025)

Mohamed Elbayumi Ulaş Bağcı Maurice Pradella Zachary Zilber Philip Greenland

Angiology

Short axis

Ventricular Function

10.1016/j.jocmr.2024.101495

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Healthy-to-Patients Domain-Adaptive Deep Learning for Time-Resolved Segmentation of Left Atrium in Short-Axis Cine MRI Images

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2024)

Mohamed Elbayumi Ulaş Bağcı Maurice Pradella Zachary Zilber Philip Greenland

Motivation: Addressing challenges with current deep learning (DL) techniques that struggle with domain shifts. Goal(s): To introduce a domain-adaptive technique that is able to segment the Left Atrium from MRI of patients employing model trained exclusively on healthy data. Approach: Our approach involves training exclusively on healthy data and incorporating stochastic encoding of temporal composite variations as augmentations to encode the underlying space of plausible anatomical changes and dynamics. We tested on three challenging unseen patient daatsets. Results: Our domain-adaptive approach showed significant improvement over the state-of-the-art LA segmentation model. Enabling LA segmentation of all time frames of the cardiac cycle. Impact: The proposed domain-adaptive deep learning approach addresses a fundamental challenge of training deep learning models only on healthy control datasets while maintaining high performance on unseen patients' populations. This could potentially lead to solve performance issues for limited patients cohorts.

Real-time MRI

Short axis

10.58530/2024/1202

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Assessing Foundational Medical 'Segment Anything' (Med-SAM1, Med-SAM2) Deep Learning Models for Left Atrial Segmentation in 3D LGE MRI

arXiv (Cornell University) (2024)

Mehri Mehrnia Mohamed Elbayumi Mohammed S.M. Elbaz

Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with heart failure and stroke. Accurate segmentation of the left atrium (LA) in 3D late gadolinium-enhanced (LGE) MRI is helpful for evaluating AF, as fibrotic remodeling in the LA myocardium contributes to arrhythmia and serves as a key determinant of therapeutic strategies. However, manual LA segmentation is labor-intensive and challenging. Recent foundational deep learning models, such as the Segment Anything Model (SAM), pre-trained on diverse datasets, have demonstrated promise in generic segmentation tasks. MedSAM, a fine-tuned version of SAM for medical applications, enables efficient, zero-shot segmentation without domain-specific training. Despite the potential of MedSAM model, it has not yet been evaluated for the complex task of LA segmentation in 3D LGE-MRI. This study aims to (1) evaluate the performance of MedSAM in automating LA segmentation, (2) compare the performance of the MedSAM2 model, which uses a single prompt with automated tracking, with the MedSAM1 model, which requires separate prompt for each slice, and (3) analyze the performance of MedSAM1 in terms of Dice score(i.e., segmentation accuracy) by varying the size and location of the box prompt.

10.48550/arxiv.2411.05963

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