Shape Completion in the Dark: Completing Vertebrae Morphology from 3D Ultrasound

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Auteurs principaux: Gafencu, Miruna-Alexandra, Velikova, Yordanka, Saleh, Mahdi, Ungi, Tamas, Navab, Nassir, Wendler, Thomas, Azampour, Mohammad Farid
Format: Preprint
Publié: 2024
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author Gafencu, Miruna-Alexandra
Velikova, Yordanka
Saleh, Mahdi
Ungi, Tamas
Navab, Nassir
Wendler, Thomas
Azampour, Mohammad Farid
author_facet Gafencu, Miruna-Alexandra
Velikova, Yordanka
Saleh, Mahdi
Ungi, Tamas
Navab, Nassir
Wendler, Thomas
Azampour, Mohammad Farid
contents Purpose: Ultrasound (US) imaging, while advantageous for its radiation-free nature, is challenging to interpret due to only partially visible organs and a lack of complete 3D information. While performing US-based diagnosis or investigation, medical professionals therefore create a mental map of the 3D anatomy. In this work, we aim to replicate this process and enhance the visual representation of anatomical structures. Methods: We introduce a point-cloud-based probabilistic DL method to complete occluded anatomical structures through 3D shape completion and choose US-based spine examinations as our application. To enable training, we generate synthetic 3D representations of partially occluded spinal views by mimicking US physics and accounting for inherent artifacts. Results: The proposed model performs consistently on synthetic and patient data, with mean and median differences of 2.02 and 0.03 in CD, respectively. Our ablation study demonstrates the importance of US physics-based data generation, reflected in the large mean and median difference of 11.8 CD and 9.55 CD, respectively. Additionally, we demonstrate that anatomic landmarks, such as the spinous process (with reconstruction CD of 4.73) and the facet joints (mean distance to GT of 4.96mm) are preserved in the 3D completion. Conclusion: Our work establishes the feasibility of 3D shape completion for lumbar vertebrae, ensuring the preservation of level-wise characteristics and successful generalization from synthetic to real data. The incorporation of US physics contributes to more accurate patient data completions. Notably, our method preserves essential anatomic landmarks and reconstructs crucial injections sites at their correct locations. The generated data and source code will be made publicly available (https://github.com/miruna20/Shape-Completion-in-the-Dark).
format Preprint
id arxiv_https___arxiv_org_abs_2404_07668
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Shape Completion in the Dark: Completing Vertebrae Morphology from 3D Ultrasound
Gafencu, Miruna-Alexandra
Velikova, Yordanka
Saleh, Mahdi
Ungi, Tamas
Navab, Nassir
Wendler, Thomas
Azampour, Mohammad Farid
Image and Video Processing
Computer Vision and Pattern Recognition
Purpose: Ultrasound (US) imaging, while advantageous for its radiation-free nature, is challenging to interpret due to only partially visible organs and a lack of complete 3D information. While performing US-based diagnosis or investigation, medical professionals therefore create a mental map of the 3D anatomy. In this work, we aim to replicate this process and enhance the visual representation of anatomical structures. Methods: We introduce a point-cloud-based probabilistic DL method to complete occluded anatomical structures through 3D shape completion and choose US-based spine examinations as our application. To enable training, we generate synthetic 3D representations of partially occluded spinal views by mimicking US physics and accounting for inherent artifacts. Results: The proposed model performs consistently on synthetic and patient data, with mean and median differences of 2.02 and 0.03 in CD, respectively. Our ablation study demonstrates the importance of US physics-based data generation, reflected in the large mean and median difference of 11.8 CD and 9.55 CD, respectively. Additionally, we demonstrate that anatomic landmarks, such as the spinous process (with reconstruction CD of 4.73) and the facet joints (mean distance to GT of 4.96mm) are preserved in the 3D completion. Conclusion: Our work establishes the feasibility of 3D shape completion for lumbar vertebrae, ensuring the preservation of level-wise characteristics and successful generalization from synthetic to real data. The incorporation of US physics contributes to more accurate patient data completions. Notably, our method preserves essential anatomic landmarks and reconstructs crucial injections sites at their correct locations. The generated data and source code will be made publicly available (https://github.com/miruna20/Shape-Completion-in-the-Dark).
title Shape Completion in the Dark: Completing Vertebrae Morphology from 3D Ultrasound
topic Image and Video Processing
Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2404.07668