# Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension Perspective

arXiv: https://arxiv.org/abs/2207.06551 · HTML: https://mlsanity.com/paper/2207.06551/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 65 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 13 of 13)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Recurrent Feature Reasoning for Image Inpainting](https://mlsanity.com/paper/2008.03737/) (2020)
- [Boundless: Generative Adversarial Networks for Image Extension](https://mlsanity.com/paper/1908.07007/) (2019)
- [Domain-adversarial neural networks to address the appearance variability of histopathology images](https://mlsanity.com/paper/1707.06183/) (2017)
- [How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosis](https://mlsanity.com/paper/1710.09762/) (2017)
- [Multi-Domain Image Completion for Random Missing Input Data](https://mlsanity.com/paper/2007.05534/) (2020)
- [CT Field of View Extension Using Combined Channels Extension and Deep Learning Methods](https://mlsanity.com/paper/1908.09529/) (2019)
- [Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks](https://mlsanity.com/paper/1808.03844/) (2018)

## Built on by (most cited first, 7 of 7)

- [Zero-shot CT Field-of-view Completion with Unconditional Generative Diffusion Prior](https://mlsanity.com/paper/2304.03760/) (2023)
- [Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential](https://mlsanity.com/paper/2501.18834/) (2025)
- [Efficient Image-to-Image Schr"odinger Bridge for CT Field of View Extension](https://mlsanity.com/paper/2508.11211/) (2025)
- [Task-Specific Data Preparation for Deep Learning to Reconstruct Structures of Interest from Severely Truncated CBCT Data](https://mlsanity.com/paper/2409.08800/) (2024)
- [Beyond the Lungs: Extending the Field of View in Chest CT with Latent Diffusion Models](https://mlsanity.com/paper/2501.13068/) (2025)
- [Multipath cycleGAN for harmonization of paired and unpaired low-dose lung computed tomography reconstruction kernels](https://mlsanity.com/paper/2505.22568/) (2025)
- [Field-of-View Extension in Dental Cone-Beam CT via Implicit Neural Representations and Diffusion Model-Based Refinement](https://mlsanity.com/paper/2609.28110/) (2026)

## Abstract (arXiv)

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 a large-scale lung screening CT dataset, 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.