# Automated Lesion Segmentation in Whole-Body FDG-PET/CT with Multi-modality Deep Neural Networks

arXiv: https://arxiv.org/abs/2302.12774 · HTML: https://mlsanity.com/paper/2302.12774/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2023.

## Built on by (most cited first, 1 of 1)

- [State of Abdominal CT Datasets: A Critical Review of Bias, Clinical Relevance, and Real-world Applicability](https://mlsanity.com/paper/2508.13626/) (2025)

## Abstract (arXiv)

Recent progress in automated PET/CT lesion segmentation using deep learning methods has demonstrated the feasibility of this task. However, tumor lesion detection and segmentation in whole-body PET/CT is still a chal-lenging task. To promote research on machine learning-based automated tumor lesion segmentation on whole-body FDG-PET/CT data, Automated Lesion Segmentation in Whole-Body FDG-PET/CT (autoPET) challenge is held, and a large, publicly available training dataset is provided. In this report, we present our solution to the autoPET challenge. We employ multi-modal residual U-Net with deep super vision. The experimental results for five preliminary test cases show that Dice score is 0.79 +/- 0.21.