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Coronary Ostia Localization Using Residual U-Net with Heatmap Matching and 3D DSNT

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Machine Learning in Medical Imaging (MLMI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13583))

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Abstract

Localization of coronary ostia landmarks in Computed Tomography Angiography (CTA) volumes is a crucial step in developing various automatic diagnostic procedures. In this study, we propose a one-step method of coronary ostia landmark localization that utilizes a residual U-Net with heatmap matching and 3D Differentiable Spatial to Numerical Transform (DSNT). We evaluate the method using two datasets: a Coronary Computed Tomography Angiography (CCTA) dataset containing 201 scans and a publicly available ImageTBAD dataset containing 77 CTA scans annotated with coronary ostia landmarks.

On the CCTA dataset we report median Euclidean distance error – 1.14 mm on the left coronary ostium and 0.98 mm on the right coronary ostium. On the ImageTBAD CTA dataset we report median Euclidean distance error – 3.48 mm on the left coronary ostium and 2.97 mm on the right coronary ostium. Our evaluation shows that the proposed method improves accuracy of coronary ostia landmark localization when compared to other known methods.

M. Gajowczyk and P. Rygiel—Equal contribution.

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Authors and Affiliations

  1. Hemolens Diagnostics Ltd., Wroclaw, Poland

    Milosz Gajowczyk, Patryk Rygiel, Piotr Grodek, Adrian Korbecki, Michal Sobanski, Przemyslaw Podgorski & Tomasz Konopczynski

  2. Wroclaw University of Science and Technology, Wroclaw, Poland

    Milosz Gajowczyk & Patryk Rygiel

  3. Wroclaw Medical University, Wroclaw, Poland

    Adrian Korbecki, Michal Sobanski & Przemyslaw Podgorski

Authors
  1. Milosz Gajowczyk
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  2. Patryk Rygiel
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  3. Piotr Grodek
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  4. Adrian Korbecki
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  5. Michal Sobanski
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  6. Przemyslaw Podgorski
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  7. Tomasz Konopczynski
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Correspondence to Tomasz Konopczynski .

Editor information

Editors and Affiliations

  1. Xi'an Jiaotong University, Xi'an, China

    Chunfeng Lian

  2. Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China

    Xiaohuan Cao

  3. Istanbul Technical University, Istanbul, Turkey

    Islem Rekik

  4. Rensselaer Polytechnic Institute, Troy, NY, USA

    Xuanang Xu

  5. ShanghaiTech University, Pudong, China

    Zhiming Cui

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Gajowczyk, M. et al. (2022). Coronary Ostia Localization Using Residual U-Net with Heatmap Matching and 3D DSNT. In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds) Machine Learning in Medical Imaging. MLMI 2022. Lecture Notes in Computer Science, vol 13583. Springer, Cham. https://doi.org/10.1007/978-3-031-21014-3_33

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  • DOI: https://doi.org/10.1007/978-3-031-21014-3_33

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  • Print ISBN: 978-3-031-21013-6

  • Online ISBN: 978-3-031-21014-3

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