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Automated segmentation of cancerous lesions in PET/CT images is a vitalinitial task for quantitative analysis. However, it is often challenging totrain deep learning-based segmentation methods to high degree of accuracy dueto the diversity of lesions in terms of their shapes, sizes, and radiotraceruptake levels. These lesions can be found in various parts of the body, oftenclose to healthy organs that also show significant uptake. Consequently,developing a comprehensive PET/CT lesion segmentation model is a demandingendeavor for routine quantitative image analysis. In this work, we train a 3DResidual UNet using Generalized Dice Focal Loss function on the AutoPETchallenge 2023 training dataset. We develop our models in a 5-foldcross-validation setting and ensemble the five models via average andweighted-average ensembling. On the preliminary test phase, the averageensemble 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.

Generalized Dice Focal Loss trained 3D Residual UNet for Automated Lesion Segmentation in Whole-Body FDG PET/CT Images