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Full left ventricle quantification via deep multitask relationships learning.

Wufeng Xue1, Gary Brahm1, Sachin Pandey1

  • 1Department of Medical Imaging, Western University, London, ON, Canada; Digital Image Group (DIG), London, ON, Canada.

Medical Image Analysis
|October 9, 2017
PubMed
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This study introduces a deep multitask relationship learning network (DMTRL) for comprehensive cardiac left ventricle (LV) quantification. The novel method accurately assesses cardiac function by simultaneously analyzing multiple LV indices and cardiac phase from MRI sequences.

Area of Science:

  • Cardiovascular Imaging and Diagnostics
  • Artificial Intelligence in Medicine
  • Medical Image Analysis

Background:

  • Accurate cardiac left ventricle (LV) quantification is crucial for diagnosing cardiac diseases.
  • Existing methods face challenges due to high inter-subject variability and complex temporal dynamics in cardiac sequences.
  • Simultaneously quantifying all LV indices (areas, wall thickness, dimensions, phase) is particularly difficult due to inter-index correlations.

Purpose of the Study:

  • To develop a deep multitask relationship learning network (DMTRL) for comprehensive LV quantification.
  • To address the challenge of simultaneously estimating multiple LV indices and cardiac phase.
  • To improve the accuracy and generalization of cardiac function assessment.

Main Methods:

Keywords:
Bayesian frameworkLeft ventricle quantificationMultitask learningMultitask relationship

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  • Utilized a deep convolution neural network (CNN) for robust cardiac representation learning.
  • Employed parallel recurrent neural network (RNN) modules to model temporal dynamics of cardiac sequences.
  • Integrated a Bayesian framework for automatic multitask relationship learning and a softmax classifier for phase estimation, enabling end-to-end training.

    • Main Results:

    • Achieved high accuracy in predicting LV indices: average mean absolute error of 180 mm² for areas, 1.39 mm for regional wall thickness (RWT), and 2.51 mm for dimensions.
    • Demonstrated accurate cardiac phase classification with an error rate of 8.2%.
    • The learned task covariance matrix effectively captured correlations among indices, leading to precise estimations.

    Conclusions:

    • The proposed DMTRL network offers an effective and integrated approach for end-to-end full LV quantification.
    • The method shows significant potential for comprehensive clinical assessment of global, regional, and dynamic cardiac function.
    • Accurate quantification of LV indices and cardiac phase can aid in earlier and more precise cardiac disease diagnosis.