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Slice-Mask Based 3D Cardiac Shape Reconstruction from CT Volume

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13846))

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Abstract

An accurate 3D ventricular model is essential for diagnosing and analyzing cardiovascular disease. It is challenging to obtain accurate patient-specific models on scarce data via widely accepted deep-learning methods. To fully use the characteristics of medical volume-based images, we present a slice-mask representation to better regress the parameters of the 3D model. A data synthesis strategy is proposed to alleviate the lack of training data by sampling in the constructed statistical shape model space and obtaining the corresponding slice-masks. We train the end-to-end structure by combining the segmentation and parametric regression modules. Furthermore, we establish a larger left ventricular CT dataset than before, which fills the gap in relevant data of the healthy population. Our method is evaluated on both synthetic data and real cardiac scans. Experiments demonstrate that our method can achieve advanced results in shape reconstruction and segmentation tasks. Code is publicly available at https://github.com/yuan-xiaohan/Slice-mask-based-3D-Cardiac-Shape-Reconstruction.

All the authors from Southeast University are affiliated with the Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, China.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of Jiangsu Province (No. BK20220127), the National Natural Science Foundation of China (No. 62076061), the “Young Elite Scientists Sponsorship Program by CAST” (No. YES20200025), and the “Zhishan Young Scholar” Program of Southeast University (No. 2242021R41083).

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Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, China

    Xiaohan Yuan, Cong Liu, Fu Feng & Yangang Wang

  2. Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China

    Yinsu Zhu

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  1. Xiaohan Yuan
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  2. Cong Liu
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  3. Fu Feng
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  4. Yinsu Zhu
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  5. Yangang Wang
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Corresponding author

Correspondence to Yangang Wang .

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Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Yuan, X., Liu, C., Feng, F., Zhu, Y., Wang, Y. (2023). Slice-Mask Based 3D Cardiac Shape Reconstruction from CT Volume. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13846. Springer, Cham. https://doi.org/10.1007/978-3-031-26351-4_5

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  • DOI: https://doi.org/10.1007/978-3-031-26351-4_5

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