# Improving Lesion Segmentation in FDG-18 Whole-Body PET/CT scans using Multilabel approach: AutoPET II challenge

arXiv: https://arxiv.org/abs/2311.01574 · HTML: https://mlsanity.com/paper/2311.01574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 14 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Built on by (most cited first, 3 of 3)

- [Autopet III challenge: Incorporating anatomical knowledge into nnUNet for lesion segmentation in PET/CT](https://mlsanity.com/paper/2409.12155/) (2024)
- [Segmentation of Prostate Tumour Volumes from PET Images is a Different Ball Game](https://mlsanity.com/paper/2407.10537/) (2024)
- [GRASPing Anatomy to Improve Pathology Segmentation](https://mlsanity.com/paper/2508.03374/) (2025)

## Abstract (arXiv)

Automatic segmentation of lesions in FDG-18 Whole Body (WB) PET/CT scans using deep learning models is instrumental for determining treatment response, optimizing dosimetry, and advancing theranostic applications in oncology. However, the presence of organs with elevated radiotracer uptake, such as the liver, spleen, brain, and bladder, often leads to challenges, as these regions are often misidentified as lesions by deep learning models. To address this issue, we propose a novel approach of segmenting both organs and lesions, aiming to enhance the performance of automatic lesion segmentation methods. In this study, we assessed the effectiveness of our proposed method using the AutoPET II challenge dataset, which comprises 1014 subjects. We evaluated the impact of inclusion of additional labels and data in the segmentation performance of the model. In addition to the expert-annotated lesion labels, we introduced eight additional labels for organs, including the liver, kidneys, urinary bladder, spleen, lung, brain, heart, and stomach. These labels were integrated into the dataset, and a 3D UNET model was trained within the nnUNet framework. Our results demonstrate that our method achieved the top ranking in the held-out test dataset, underscoring the potential of this approach to significantly improve lesion segmentation accuracy in FDG-18 Whole-Body PET/CT scans, ultimately benefiting cancer patients and advancing clinical practice.