  Multi-modality Fluorodeoxyglucose (FDG) positron emission tomography /
computed tomography (PET/CT) has been routinely used in the assessment of
common cancers, such as lung cancer, lymphoma, and melanoma. This is mainly
attributed to the fact that PET/CT combines the high sensitivity for tumor
detection of PET and anatomical information from CT. In PET/CT image
assessment, automatic tumor segmentation is an important step, and in recent
years, deep learning based methods have become the state-of-the-art.
Unfortunately, existing methods tend to over-segment the tumor regions and
include regions such as the normal high uptake organs, inflammation, and other
infections. In this study, we introduce a false positive reduction network to
overcome this limitation. We firstly introduced a self-supervised pre-trained
global segmentation module to coarsely delineate the candidate tumor regions
using a self-supervised pre-trained encoder. The candidate tumor regions were
then refined by removing false positives via a local refinement module. Our
experiments with the MICCAI 2022 Automated Lesion Segmentation in Whole-Body
FDG-PET/CT (AutoPET) challenge dataset showed that our method achieved a dice
score of 0.9324 with the preliminary testing data and was ranked 1st place in
dice on the leaderboard. Our method was also ranked in the top 7 methods on the
final testing data, the final ranking will be announced during the 2022 MICCAI
AutoPET workshop. Our code is available at:
https://github.com/YigePeng/AutoPET_False_Positive_Reduction.
