Three-Dimensional Embedded Attentive RNN (3D-EAR) Segmentor for Left Ventricle Delineation from Myocardial Velocity Mapping

• URL: http://arxiv.org/abs/2104.13214v1
• Date: Mon, 26 Apr 2021 11:04:43 GMT
• Title: Three-Dimensional Embedded Attentive RNN (3D-EAR) Segmentor for Left Ventricle Delineation from Myocardial Velocity Mapping
• Authors: Mengmeng Kuang, Yinzhe Wu, Diego Alonso-\'Alvarez, David Firmin, Jennifer Keegan, Peter Gatehouse, Guang Yang
• Abstract summary: We propose a novel fully automated framework incorporating a 3D-UNet backbone architecture with Embedded multichannel Attention mechanism and LSTM based Recurrent neural networks (RNN) for the MVM-CMR datasets. By comparing the baseline model of 3D-UNet and ablation studies with and without embedded attentive LSTM modules and various loss functions, we can demonstrate that the proposed model has outperformed the state-of-the-art baseline models with significant improvement.
• Score: 1.8653386811342048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Myocardial Velocity Mapping Cardiac MR (MVM-CMR) can be used to measure global and regional myocardial velocities with proved reproducibility. Accurate left ventricle delineation is a prerequisite for robust and reproducible myocardial velocity estimation. Conventional manual segmentation on this dataset can be time-consuming and subjective, and an effective fully automated delineation method is highly in demand. By leveraging recently proposed deep learning-based semantic segmentation approaches, in this study, we propose a novel fully automated framework incorporating a 3D-UNet backbone architecture with Embedded multichannel Attention mechanism and LSTM based Recurrent neural networks (RNN) for the MVM-CMR datasets (dubbed 3D-EAR segmentor). The proposed method also utilises the amalgamation of magnitude and phase images as input to realise an information fusion of this multichannel dataset and exploring the correlations of temporal frames via the embedded RNN. By comparing the baseline model of 3D-UNet and ablation studies with and without embedded attentive LSTM modules and various loss functions, we can demonstrate that the proposed model has outperformed the state-of-the-art baseline models with significant improvement.

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