A mirror-Unet architecture for PET/CT lesion segmentation
Yamila Rotstein Habarnau Mauro Namías
Automatic lesion detection and segmentation from [${}^{18}$F]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/AutoPET2023_Mv1.