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Automatic Segmentation of the Left Atrium from Computed Tomography Angiography Images

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Abstract

The left atrial appendage (LAA) causes 91% of thrombi in atrial fibrillation patients, a potential harbinger of stroke. Leveraging computed tomography angiography (CTA) images, radiologists interpret the left atrium (LA) and LAA geometries to stratify stroke risk. Nevertheless, accurate LA segmentation remains a time-consuming task with high inter-observer variability. Binary masks of the LA and their corresponding CTA images were used to train and test a 3D U-Net to automate LA segmentation. One model was trained using the entire unified-image-volume while a second model was trained on regional patch-volumes which were run for inference and then assimilated back into the full volume. The unified-image-volume U-Net achieved median DSCs of 0.92 and 0.88 for the train and test sets, respectively; the patch-volume U-Net achieved median DSCs of 0.90 and 0.89 for the train and test sets, respectively. This indicates that the unified-image-volume and patch-volume U-Net models captured up to 88 and 89% of the LA/LAA boundary’s regional complexity, respectively. Additionally, the results indicate that the LA/LAA were fully captured in most of the predicted segmentations. By automating the segmentation process, our deep learning model can expedite LA/LAA shape, informing stratification of stroke risk.

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Authors and Affiliations

  1. University of Pittsburgh, 302 Benedum Hall, 3700 O’Hara Street, Pittsburgh, PA, 15213, USA

    Amaan Kazi, Anish Salvi & Prahlad G. Menon

  2. Statistics & Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA

    Sage Betko

  3. ImageRx, Pittsburgh, PA, USA

    Amaan Kazi, Sage Betko, Anish Salvi & Prahlad G. Menon

Authors
  1. Amaan Kazi
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  2. Sage Betko
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  3. Anish Salvi
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  4. Prahlad G. Menon
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Correspondence to Prahlad G. Menon.

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Associate Editor Umberto Morbiducci oversaw the review of this article.

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Kazi, A., Betko, S., Salvi, A. et al. Automatic Segmentation of the Left Atrium from Computed Tomography Angiography Images. Ann Biomed Eng 51, 1713–1722 (2023). https://doi.org/10.1007/s10439-023-03170-9

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  • DOI: https://doi.org/10.1007/s10439-023-03170-9

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