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3D shallow deep neural network for fast and precise segmentation of left atrium

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Abstract

Knowledge of the underlying anatomy of the left atrium can promote improved diagnostic protocols and clinical interventions. Hence, an automatic segmentation of the left atrium on magnetic resonance imaging (MRI) can support diagnosis, treatment and surgery planning of heart. However, due to the small size of left atrium with respect to the whole MRI volume, accurate segmentation of left atrium is challenging. Most of the existing deep learning approaches are based on cropping or cascading networks. In this work, we present a novel deep learning architecture for the segmentation of left atrium from MRI volume which incorporates the residual learning based encoder-decoder network. We introduce a loss function and parameter adjustments to deal with the issue of class imbalance and unavailability of large medical imaging dataset. To facilitate the high quality segmentation, we present a three-dimensional multi-scale residual learning based architecture that maintains coarse and fine level features throughout the network. Experimental results have shown a considerable improvement in segmentation performance by surpassing the current benchmarks (especially the winner of Left Atrial Segmentation Challenge-2018) with fewer parameters compared to the state-of-the-art approaches, thus potentially supporting cardiac diagnosis and surgery without adding any extensive pre-processing of input volumes or any post-processing on the base network’s output.

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Authors and Affiliations

  1. MJIIT, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Asma Kausar & Mohammad Ibrahim Shapiai

  2. Deakin University, Geelong, Australia

    Imran Razzak

  3. Macquire University, Sydney, Australia

    Amin Beheshti

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  1. Asma Kausar
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  2. Imran Razzak
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Correspondence to Asma Kausar.

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Kausar, A., Razzak, I., Shapiai, M.I. et al. 3D shallow deep neural network for fast and precise segmentation of left atrium. Multimedia Systems 29, 1739–1749 (2023). https://doi.org/10.1007/s00530-021-00776-8

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