  Automated segmentation of cancerous lesions in PET/CT images is a vital
initial task for quantitative analysis. However, it is often challenging to
train deep learning-based segmentation methods to high degree of accuracy due
to the diversity of lesions in terms of their shapes, sizes, and radiotracer
uptake levels. These lesions can be found in various parts of the body, often
close to healthy organs that also show significant uptake. Consequently,
developing a comprehensive PET/CT lesion segmentation model is a demanding
endeavor for routine quantitative image analysis. In this work, we train a 3D
Residual UNet using Generalized Dice Focal Loss function on the AutoPET
challenge 2023 training dataset. We develop our models in a 5-fold
cross-validation setting and ensemble the five models via average and
weighted-average ensembling. On the preliminary test phase, the average
ensemble achieved a Dice similarity coefficient (DSC), false-positive volume
(FPV) and false negative volume (FNV) of 0.5417, 0.8261 ml, and 0.2538 ml,
respectively, while the weighted-average ensemble achieved 0.5417, 0.8186 ml,
and 0.2538 ml, respectively. Our algorithm can be accessed via this link:
https://github.com/ahxmeds/autosegnet.
