2022/08/31 by Shaonan Zhong, Zhong, Shaonan, Junyang Mo +3 · 2 citations
Computer Science · Engineering · Mathematics · Medicine · #Artificial Intelligence (cs.AI) #Artificial intelligence #Code (set theory) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Dice #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Lesion #Lung Cancer Diagnosis and Treatment #Mathematics #Medical Imaging Techniques and Applications #Medicine #Nuclear medicine #Pathology #Pattern recognition (psychology) #Radiology #Radiomics and Machine Learning in Medical Imaging #Segmentation #Set (abstract data type) #Volume (thermodynamics) #cs.AI #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2209.01212
published in arXiv (Cornell University) (Cornell University)
arxiv created 2022/08/31 · openalex publication_date 2022/08/31 · arxiv updated 2022/09/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Automatic segmentation of tumor lesions is a critical initial processing step for quantitative PET/CT analysis. However, numerous tumor lesion 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 training strategy to build deep learning models capable of systemic tumor segmentation. Our method is validated on the training set of the AutoPET 2022 Challenge. We achieved 0.7574 Dice score, 0.0299 false positive volume and 0.2538 false negative volume on preliminary test set.The code of our work is available on the following link: https://github.com/ZZZsn/MICCAI2022-autopet.