July 15, 2024Computers in Biology and MedicineOpen Access

Self-adaptive deep learning-based segmentation for universal and functional clinical and preclinical CT image analysis

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Authors

AZAnne-Wietje ZwijnenErasmus MC LWLeon Watzema YRYanto RidwanErasmus MC

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Implication

Deep learning evaluation demonstrates automated cardiac segmentation in clinical and preclinical CT imaging, highlighting potential to accelerate heart failure drug discovery.

Key Points

Automated extraction of quantitative cardiac functioning data succeeds across both clinical computed tomography and preclinical micro ct images, bypassing manual analysis limits.
Deep learning image analysis automated the extraction of functional metrics from cardiac substructures, bridging the methodological gap between clinical and micro ct scans.
Accelerates translational heart failure research by standardizing non-invasive cardiac evaluation; preclinical model translation requires further functional validation.

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Zwijnen et al. (2024) studied this question.

synapsesocial.com/papers/68e60461b6db643587597695 https://doi.org/10.1016/j.compbiomed.2024.108853

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Implication

Deep learning evaluation demonstrates automated cardiac segmentation in clinical and preclinical CT imaging, highlighting potential to accelerate heart failure drug discovery.

Self-adaptive deep learning-based segmentation for universal and functional clinical and preclinical CT image analysis

Discussion

Member takes

Implication

Key Points

Cite This Study

Self-adaptive deep learning-based segmentation for universal and functional clinical and preclinical CT image analysis

Discussion

Member takes

Implication

Key Points

Cite This Study

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