A mirror-Unet architecture for PET/CT lesion segmentation
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 9 references that are in the map. First posted on arXiv 23 Sep 2023.
Builds on
6 of 9 references.
References · 6
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
- 2018Averaging Weights Leads to Wider Optima and Better Generalizationcited by 1,209
- 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 613
- 2020TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learningcited by 177
- 2023Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imagingcited by 14
Built on by
Not cited yet.