AutoPET Challenge 2022: Step-by-Step Lesion Segmentation in Whole-body FDG-PET/CT
This addresses the need for accurate lesion segmentation in medical imaging for quantitative PET/CT analysis, though it appears incremental as it builds on existing segmentation methods.
The paper tackled the challenging problem of automatic tumor lesion segmentation in whole-body FDG-PET/CT scans, achieving a Dice score of 0.92, false positive volume of 0.89, and false negative volume of 0.53 on a preliminary test set.
Automatic segmentation of tumor lesions is a critical initial processing step for quantitative PET/CT analysis. However, numerous tumor lesions with different shapes, sizes, and uptake intensity may be distributed in different anatomical contexts throughout the body, and there is also significant uptake in healthy organs. Therefore, building a systemic PET/CT tumor lesion segmentation model is a challenging task. In this paper, we propose a novel step-by-step 3D segmentation method to address this problem. We achieved Dice score of 0.92, false positive volume of 0.89 and false negative volume of 0.53 on preliminary test set.The code of our work is available on the following link: https://github.com/rightl/autopet.