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Commensal correlation network between segmentation and direct area estimation for bi-ventricle quantification.

Gongning Luo¹, Suyu Dong¹, Wei Wang¹

¹Biocomputing Research Center, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.

Medical Image Analysis

|November 10, 2019

Summary

This study introduces a unified framework for cardiac magnetic resonance (CMR) image analysis, improving bi-ventricle quantification accuracy. The novel deep commensal network (DCN) integrates segmentation and function estimation for better cardiac disease diagnosis.

Keywords:

Area commensal correlation Area estimation Bi-ventricle quantification Deep learning Segmentation

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3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

Published on: May 19, 2015

Area of Science:

Medical Imaging Analysis
Cardiovascular Disease Diagnosis
Deep Learning in Healthcare

Background:

Accurate cardiac bi-ventricle quantification using cardiac magnetic resonance (CMR) is vital for diagnosing heart conditions.
Traditional methods independently address bi-ventricle segmentation and function estimation, facing challenges due to variable CMR imaging conditions.
Existing approaches struggle with the inherent difficulties of precise ventricle quantification.

Purpose of the Study:

To develop a unified framework for cardiac bi-ventricle quantification by leveraging the correlation between segmentation and area estimation.
To introduce a novel deep commensal network (DCN) for end-to-end optimization of these tasks.
To enable efficient uncertainty estimation through a single inference pass.

Main Methods:

Proposed a novel area commensal correlation between bi-ventricle segmentation and direct area estimation.
Designed a deep commensal network (DCN) integrating these tasks using a commensal correlation loss.
Developed a differentiable area operator for continuous differentiability and an uncertainty estimation method based on cross-task output variability.

Main Results:

The DCN achieved end-to-end optimization, fast convergence, and uncertainty estimation with one-time inference.
Experiments on four benchmark CMR datasets (Sunnybrook, STACOM 2011, RVSC, ACDC) demonstrated superior bi-ventricle quantification accuracy.
The proposed method outperformed existing approaches in accuracy and optimization performance.

Conclusions:

The unified framework effectively addresses cardiac bi-ventricle quantification challenges.
The DCN shows significant potential for clinical application and extension to other medical image analysis tasks.
This approach offers improved accuracy and efficiency in diagnosing cardiac diseases via CMR imaging.