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  • SPS
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    Length: 0:09:58
29 Jun 2022

Accurate and automated localization of the left ventricle (LV) of heart and identification of its' orientation are crucial for effective diagnosis of various heart diseases. This work presents a fully automated deep learning based pipeline for generating a short-axis transformation of transversal 3D CT scans. The proposed novel solution consists of three independent convolutional neural networks (CNN) in cascaded sequential phases. In phase one, a binary classification network is used to determine a suitable 2D slice that clearly shows the left ventricle from the 3D image stack. In phase two, a different CNN is used to predict the location of 3 landmarks on the suitable 2D slice. From these landmarks a general orientation of the left ventricle is computed in the xy plane. In the last phase, a third CNN is used to predict the location of 3 landmarks on the orthogonal image. With all 6 points, a 3D orientation of the heart can be calculated. The proposed method performs as good as human accuracy, considering inter-observer variability, and performed equally well in terms of End-Diastolic Volume (EDV) and Left Ventricular Mass (LVM) calculation.

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  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00
  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00