1 research outputs found
Autopet Challenge 2023: nnUNet-based whole-body 3D PET-CT Tumour Segmentation
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\_nnune