2023/09/23 by Yamila Rotstein Habarnau, Habarnau, Yamila Rotstein, Mauro Namías +1 · 1 citation
Medicine · #Artificial intelligence #Bottleneck #Code (set theory) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Deep learning #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Image segmentation #Lesion #Lung Cancer Diagnosis and Treatment #Medical Imaging Techniques and Applications #Medicine #Pathology #Pattern recognition (psychology) #Radiomics and Machine Learning in Medical Imaging #Segmentation #Task (project management) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2309.13398
openalex publication_date 2023/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Automatic lesion detection and segmentation from [18F]FDG PET/CT scans is a challenging task, due to the diversity of shapes, sizes, FDG uptake and location they may present, besides the fact that physiological uptake is also present on healthy tissues. In this work, we propose a deep learning method aimed at the segmentation of oncologic lesions, based on a combination of two UNet-3D branches. First, one of the network's branches is trained to segment a group of tissues from CT images. The other branch is trained to segment the lesions from PET images, combining on the bottleneck the embedded information of CT branch, already trained. We trained and validated our networks on the AutoPET MICCAI 2023 Challenge dataset. Our code is available at: https://github.com/yrotstein/AutoPET2023Mv1.