Robust Body Composition Analysis by Generating 3D CT Volumes from Limited 2D Slices
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909463923916800 |
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| author | Zuo, Lianrui Yu, Xin Su, Dingjie Xu, Kaiwen Krishnan, Aravind R. Liu, Yihao Bao, Shunxing Maldonado, Fabien Ferrucci, Luigi Landman, Bennett A. |
| author_facet | Zuo, Lianrui Yu, Xin Su, Dingjie Xu, Kaiwen Krishnan, Aravind R. Liu, Yihao Bao, Shunxing Maldonado, Fabien Ferrucci, Luigi Landman, Bennett A. |
| contents | Body composition analysis provides valuable insights into aging, disease progression, and overall health conditions. Due to concerns of radiation exposure, two-dimensional (2D) single-slice computed tomography (CT) imaging has been used repeatedly for body composition analysis. However, this approach introduces significant spatial variability that can impact the accuracy and robustness of the analysis. To mitigate this issue and facilitate body composition analysis, this paper presents a novel method to generate 3D CT volumes from limited number of 2D slices using a latent diffusion model (LDM). Our approach first maps 2D slices into a latent representation space using a variational autoencoder. An LDM is then trained to capture the 3D context of a stack of these latent representations. To accurately interpolate intermediateslices and construct a full 3D volume, we utilize body part regression to determine the spatial location and distance between the acquired slices. Experiments on both in-house and public 3D abdominal CT datasets demonstrate that the proposed method significantly enhances body composition analysis compared to traditional 2D-based analysis, with a reduced error rate from 23.3% to 15.2%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13071 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Body Composition Analysis by Generating 3D CT Volumes from Limited 2D Slices Zuo, Lianrui Yu, Xin Su, Dingjie Xu, Kaiwen Krishnan, Aravind R. Liu, Yihao Bao, Shunxing Maldonado, Fabien Ferrucci, Luigi Landman, Bennett A. Computer Vision and Pattern Recognition Image and Video Processing Body composition analysis provides valuable insights into aging, disease progression, and overall health conditions. Due to concerns of radiation exposure, two-dimensional (2D) single-slice computed tomography (CT) imaging has been used repeatedly for body composition analysis. However, this approach introduces significant spatial variability that can impact the accuracy and robustness of the analysis. To mitigate this issue and facilitate body composition analysis, this paper presents a novel method to generate 3D CT volumes from limited number of 2D slices using a latent diffusion model (LDM). Our approach first maps 2D slices into a latent representation space using a variational autoencoder. An LDM is then trained to capture the 3D context of a stack of these latent representations. To accurately interpolate intermediateslices and construct a full 3D volume, we utilize body part regression to determine the spatial location and distance between the acquired slices. Experiments on both in-house and public 3D abdominal CT datasets demonstrate that the proposed method significantly enhances body composition analysis compared to traditional 2D-based analysis, with a reduced error rate from 23.3% to 15.2%. |
| title | Robust Body Composition Analysis by Generating 3D CT Volumes from Limited 2D Slices |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2501.13071 |