ArticleMedical image analysis2023
Body composition assessment with limited field-of-view computed tomography: A semantic image extension perspective.
Article in Medical image analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- AutoLMISeg: A Fully Automated Pipeline for 3D Lumbar Muscle Volumetry and Densitometry on CT - Technical Validation Against an Operator-Driven Workflow.Journal of imaging informatics in medicine · 2026Article
- CT-based body composition and its change through time in relation to outcomes in participants screened for lung cancer.EBioMedicine · 2026Article
- Repeatability of automated body composition measurement on low dose chest CT in male subjects.PloS one · 2026Article
- Harmonizing the National Lung Screening Trial: Kernel resolution and field of view compensation.PloS one · 2026Article
- Enhancing Newborn Health Assessment: Ultrasound-based Body Composition Prediction Using Deep Learning Techniques.Ultrasound in medicine & biology · 2026Article
- Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential.Computers in biology and medicine · 2025Article
- Early adipose tissue wasting in a preclinical model of human lung cancer cachexia.Cell reports · 2025Article
- Cancer-Associated Cachexia: Bridging Clinical Findings with Mechanistic Insights in Human Studies.Cancer discovery · 2025Review
- Latent space reconstruction for missing data problems in CT.Medical physics · 2025Article
- Beyond the lungs: extending the field-of-view in chest CT with latent diffusion models.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Developing a Deep Learning Approach for Automated Body Composition Prediction in Newborns Using Ultrasound Images.IEEE access : practical innovations, open solutions · 2025Article
- The role of body composition in left ventricular remodeling, reverse remodeling, and clinical outcomes for heart failure with mildly reduced ejection fraction: more knowledge to the "obesity paradox".Cardiovascular diabetology · 2024Article
- Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks.Medical physics · 2024Article
- AI's keen diagnostic eye.Nature · 2024Article
- The relationship of fat and muscle measurements with emphysema and bronchial wall thickening in smokers.ERJ open research · 2024Article
- Generating missing patient anatomy from partially acquired cone-beam computed tomography images using deep learning: a proof of concept.Physical and engineering sciences in medicine · 2023Article
- AI Body Composition in Lung Cancer Screening: Added Value Beyond Lung Cancer Detection.Radiology · 2023Article
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9 authors.
Funding
Abstract
Field-of-view (FOV) tissue truncation beyond the lungs is common in routine lung screening computed tomography (CT). This poses limitations for opportunistic CT-based body composition (BC) assessment as key anatomical structures are missing. Traditionally, extending the FOV of CT is considered as a CT reconstruction problem using limited data. However, this approach relies on the projection domain data which might not be available in application. In this work, we formulate the problem from the semantic image extension perspective which only requires image data as inputs. The proposed two-stage method identifies a new FOV border based on the estimated extent of the complete body and imputes missing tissues in the truncated region. The training samples are simulated using CT slices with complete body in FOV, making the model development self-supervised. We evaluate the validity of the proposed method in automatic BC assessment using lung screening CT with limited FOV. The proposed method effectively restores the missing tissues and reduces BC assessment error introduced by FOV tissue truncation. In the BC assessment for large-scale lung screening CT datasets, this correction improves both the intra-subject consistency and the correlation with anthropometric approximations. The developed method is available at https://github.com/MASILab/S-EFOV.
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