# PriorNet: lesion segmentation in PET-CT including prior tumor appearance information

arXiv: https://arxiv.org/abs/2210.02203 · HTML: https://mlsanity.com/paper/2210.02203/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 6 references that are in the map.
- First posted on arXiv 5 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 Challenge](https://mlsanity.com/paper/2110.02519/) (2021)
- [Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor Segmentation](https://mlsanity.com/paper/2004.08108/) (2020)

## Built on by (most cited first, 2 of 2)

- [Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imaging](https://mlsanity.com/paper/2303.07126/) (2023)
- [Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images](https://mlsanity.com/paper/2311.14482/) (2023)

## Abstract (arXiv)

Tumor segmentation in PET-CT images is challenging due to the dual nature of the acquired information: low metabolic information in CT and low spatial resolution in PET. U-Net architecture is the most common and widely recognized approach when developing a fully automatic image segmentation method in the medical field. We proposed a two-step approach, aiming to refine and improve the segmentation performances of tumoral lesions in PET-CT. The first step generates a prior tumor appearance map from the PET-CT volumes, regarded as prior tumor information. The second step, consisting of a standard U-Net, receives the prior tumor appearance map and PET-CT images to generate the lesion mask. We evaluated the method on the 1014 cases available for the AutoPET 2022 challenge, and the results showed an average Dice score of 0.701 on the positive cases.