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DeSPPNet: A Multiscale Deep Learning Model for Cardiac Segmentation.

Elizar Elizar^1,2, Rusdha Muharar², Mohd Asyraf Zulkifley¹

¹Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.

Diagnostics (Basel, Switzerland)

|January 8, 2025

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This study introduces DeSPPNet, a deep learning model for cardiac MRI segmentation. The model achieved high accuracy (0.859 Dice score) by effectively capturing multiscale features for precise heart structure delineation.

Area of Science:

Medical Imaging
Artificial Intelligence
Cardiology

Background:

Cardiac MRI is vital for monitoring heart disease and treatment response.
Deep learning segmentation precisely delineates cardiac structures like the myocardium and ventricles.
Accurate segmentation aids in diagnosing heart failure and assessing treatment efficacy.

Purpose of the Study:

Develop a multiscale deep learning model for cardiac MRI segmentation.
Address challenges in segmenting complex cardiac structures and motion artifacts.
Improve segmentation performance using multiscale approaches.

Main Methods:

Proposed DeSPPNet, a multiscale deep learning network with an encoder-decoder architecture.
Utilized Spatial Pyramid Pooling (SPP) and Pyramid Pooling Dense Module (PPDM) for feature extraction at multiple scales.

Keywords:

artificial intelligence magnetic resonance images medical image segmentation multiscale deep learning semantic segmentation

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Integrated PPDM into the encoder to capture local and global cardiac context.

Main Results:

The 3-path PPDM at encoder layer 5 achieved optimal segmentation.
Achieved a Dice score of 0.859, Intersection over Union (IoU) of 0.800, and accuracy of 0.993.
Demonstrated the effectiveness of multiscale feature processing for cardiac MRI.

Conclusions:

PPDM configuration impacts network performance; deeper layers offer more context but lower resolution.
Optimal PPDM placement balances feature richness and spatial detail for accurate segmentation.
The study highlights the importance of multiscale analysis in cardiac MRI segmentation.