Cardiac fat segmentation using computed tomography and an image-to-image conditional generative adversarial neural network

Problem
This preprint addresses the gap in automated segmentation of cardiac fat deposits, specifically epicardial and mediastinal fats, which are critical for assessing cardiovascular disease risk. Manual segmentation is labor-intensive and not widely adopted in clinical settings, necessitating efficient computational methods for accurate and timely analysis.

Method
The authors propose a novel application of the pix2pix architecture, a conditional generative adversarial network (cGAN) designed for image-to-image translation tasks. The model is trained on a dataset of computed tomography (CT) images to autonomously segment the two types of cardiac fat. The training process involves optimizing a loss function that balances adversarial loss and pixel-wise loss, although specific details on the dataset size, training compute, and hyperparameters are not disclosed. The architecture’s ability to generalize to the segmentation task, despite not being originally designed for it, is a key technical contribution.

Results
The proposed methodology achieved an average accuracy of 99.08% and an F1-score of 98.73 for epicardial fat segmentation, while mediastinal fat segmentation yielded an accuracy of 97.90% and an F1-score of 98.40%. These results indicate a high level of precision and overlap agreement, outperforming existing segmentation methods in both accuracy and runtime efficiency. The authors claim that their approach enables real-time segmentation, which is a significant advancement over prior techniques.

Limitations
The authors acknowledge that their study is limited by the dataset used for training and validation, which may not encompass the full variability of cardiac fat appearances across different populations. Additionally, the reliance on a specific architecture (pix2pix) may limit the generalizability of the findings to other segmentation tasks. The paper does not discuss potential biases in the dataset or the implications of model interpretability in clinical settings.

Why it matters
This work has significant implications for the field of medical imaging and cardiovascular health. By providing a robust, automated solution for cardiac fat segmentation, it can facilitate more widespread adoption of quantitative imaging analyses in clinical practice, potentially leading to improved patient outcomes through earlier and more accurate risk assessments for cardiovascular diseases. The methodology could also serve as a foundation for future research in automated segmentation tasks across various medical imaging modalities.

Authors: Guilherme Santos da Silva, Dalcimar Casanova, Jefferson Tales Oliva, Erick Oliveira Rodrigues
Source: arXiv:2605.20064
URL: https://arxiv.org/abs/2605.20064v1