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Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation

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Computational Advances in Bio and Medical Sciences (ICCABS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13254))

Abstract

Coronary X-ray angiography is a crucial clinical procedure for the diagnosis and treatment of coronary artery disease, which accounts for roughly 16% of global deaths every year. However, the images acquired in these procedures have low resolution and poor contrast, making lesion detection and assessment challenging. Accurate coronary artery segmentation not only helps mitigate these problems, but also allows the extraction of relevant anatomical features for further analysis by quantitative methods. Although automated segmentation of coronary arteries has been proposed before, previous approaches have used non-optimal segmentation criteria, leading to less useful results. Most methods either segment only the major vessel, discarding important information from the remaining ones, or segment the whole coronary tree, based mostly on contrast information, producing a noisy output that includes vessels that are not relevant for diagnosis. We adopt a better-suited clinical criterion and segment vessels according to their clinical relevance. Additionally, we simultaneously perform catheter segmentation, which may be useful for diagnosis due to the scale factor provided by the catheter’s known diameter, and is a task that has not yet been performed with good results. To derive the optimal approach, we conducted an extensive comparative study of encoder-decoder architectures trained on a combination of focal loss and a variant of generalized dice loss. Based on the EfficientNet and the UNet++ architectures, we propose a line of efficient and high-performance segmentation models using a new decoder architecture, the EfficientUNet++, whose best-performing version achieves a generalized dice score of 0.9202 ± 0.0356, and artery and catheter class dice scores of 0.8858 ± 0.0461 and 0.7627 ± 0.1812.

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Acknowledgements

This work was supported by national funds through Fundação para a Ciência e Tecnologia (FCT), under the project with reference UIDB/50021/2020.

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Authors and Affiliations

  1. INESC-ID/Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal

    João Lourenço-Silva & Arlindo L. Oliveira

  2. Cardiology Department, CAML, CCUL, Lisbon School of Medicine, Lisbon, Portugal

    Miguel Nobre Menezes, Tiago Rodrigues, Beatriz Silva & Fausto J. Pinto

Authors
  1. João Lourenço-Silva
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  2. Miguel Nobre Menezes
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  3. Tiago Rodrigues
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  4. Beatriz Silva
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  5. Fausto J. Pinto
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  6. Arlindo L. Oliveira
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Corresponding author

Correspondence to João Lourenço-Silva .

Editor information

Editors and Affiliations

  1. University of Connecticut, Storrs, CT, USA

    Mukul S. Bansal

  2. University of Connecticut, Storrs, CT, USA

    Ion Măndoiu

  3. University of Connecticut Health Center, Farmington, CT, USA

    Marmar Moussa

  4. Georgia State University, Atlanta, GA, USA

    Murray Patterson

  5. University of Connecticut, Storrs, CT, USA

    Sanguthevar Rajasekaran

  6. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  7. Georgia State University, Atlanta, GA, USA

    Alexander Zelikovsky

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Lourenço-Silva, J., Menezes, M.N., Rodrigues, T., Silva, B., Pinto, F.J., Oliveira, A.L. (2022). Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation. In: Bansal, M.S., et al. Computational Advances in Bio and Medical Sciences. ICCABS 2021. Lecture Notes in Computer Science(), vol 13254. Springer, Cham. https://doi.org/10.1007/978-3-031-17531-2_6

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