  Fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with
computed tomography (CT) is considered the primary solution for detecting some
cancers, such as lung cancer and melanoma. Automatic segmentation of tumors in
PET/CT images can help reduce doctors' workload, thereby improving diagnostic
quality. However, precise tumor segmentation is challenging due to the small
size of many tumors and the similarity of high-uptake normal areas to the tumor
regions. To address these issues, this paper proposes a
localization-to-segmentation framework (L2SNet) for precise tumor segmentation.
L2SNet first localizes the possible lesions in the lesion localization phase
and then uses the location cues to shape the segmentation results in the lesion
segmentation phase. To further improve the segmentation performance of L2SNet,
we design an adaptive threshold scheme that takes the segmentation results of
the two phases into consideration. The experiments with the MICCAI 2023
Automated Lesion Segmentation in Whole-Body FDG-PET/CT challenge dataset show
that our method achieved a competitive result and was ranked in the top 7
methods on the preliminary test set. Our work is available at:
https://github.com/MedCAI/L2SNet.
