Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT Images

• URL: http://arxiv.org/abs/2209.07705v1
• Date: Fri, 16 Sep 2022 04:01:14 GMT
• Title: Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT Images
• Authors: Yige Peng, Jinman Kim, Dagan Feng, Lei Bi
• Abstract summary: In PET/CT image assessment, automatic tumor segmentation is an important step. Existing methods tend to over-segment the tumor regions and include regions such as the normal high organs, inflammation, and other infections. We introduce a false positive reduction network to overcome this limitation.
• Score: 12.885308856495353
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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