Automated Lesion Segmentation in Whole-Body FDG-PET/CT with Multi-modality Deep Neural Networks

TL;DR

This paper presents a deep learning approach using a multi-modal residual U-Net with deep supervision for automated lesion segmentation in whole-body FDG-PET/CT scans, demonstrating promising results on a public challenge dataset.

Contribution

The paper introduces a novel multi-modal residual U-Net architecture with deep supervision for lesion segmentation in PET/CT images, advancing automated tumor detection methods.

Findings

Dice score of 0.79 on preliminary test cases

Demonstrates feasibility of deep learning for whole-body PET/CT segmentation

Provides a publicly available dataset for further research

Abstract

Recent progress in automated PET/CT lesion segmentation using deep learning methods has demonstrated the feasibility of this task. However, tumor lesion detection and segmentation in whole-body PET/CT is still a chal-lenging task. To promote research on machine learning-based automated tumor lesion segmentation on whole-body FDG-PET/CT data, Automated Lesion Segmentation in Whole-Body FDG-PET/CT (autoPET) challenge is held, and a large, publicly available training dataset is provided. In this report, we present our solution to the autoPET challenge. We employ multi-modal residual U-Net with deep super vision. The experimental results for five preliminary test cases show that Dice score is 0.79 +/- 0.21.