  There has been growing research interest in using deep learning based method
to achieve fully automated segmentation of lesion in Positron emission
tomography computed tomography(PET CT) scans for the prognosis of various
cancers. Recent advances in the medical image segmentation shows the nnUNET is
feasible for diverse tasks. However, lesion segmentation in the PET images is
not straightforward, because lesion and physiological uptake has similar
distribution patterns. The Distinction of them requires extra structural
information in the CT images. The present paper introduces a nnUNet based
method for the lesion segmentation task. The proposed model is designed on the
basis of the joint 2D and 3D nnUNET architecture to predict lesions across the
whole body. It allows for automated segmentation of potential lesions. We
evaluate the proposed method in the context of AutoPet Challenge, which
measures the lesion segmentation performance in the metrics of dice score,
false-positive volume and false-negative volume.
