Autopet Challenge 2023: nnUNet-based whole-body 3D PET-CT Tumour Segmentation

TL;DR

This paper applies nnUNet to automate whole-body PET-CT tumour segmentation, achieving a Dice score of 69% and addressing challenges like false positives and negatives in complex oncological imaging.

Contribution

It demonstrates the effectiveness of nnUNet for whole-body tumour segmentation in PET-CT scans and explores optimal training and post-processing strategies.

Findings

Achieved a 69% Dice score on internal test set.

Reduced false negative volume to 6.27 mL.

Reduced false positive volume to 5.78 mL.

Abstract

Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) combined with Computed Tomography (CT) scans are critical in oncology to the identification of solid tumours and the monitoring of their progression. However, precise and consistent lesion segmentation remains challenging, as manual segmentation is time-consuming and subject to intra- and inter-observer variability. Despite their promise, automated segmentation methods often struggle with false positive segmentation of regions of healthy metabolic activity, particularly when presented with such a complex range of tumours across the whole body. In this paper, we explore the application of the nnUNet to tumour segmentation of whole-body PET-CT scans and conduct different experiments on optimal training and post-processing strategies. Our best model obtains a Dice score of 69\% and a false negative and false positive volume of 6.27 and 5.78 mL respectively, on our internal test set. This model is submitted as part of the autoPET 2023 challenge. Our code is available at: https://github.com/anissa218/autopet\_nnunet