AutoPET Challenge 2023: Sliding Window-based Optimization of U-Net

TL;DR

This paper introduces a novel sliding window-based optimization method for U-Net to improve tumor segmentation accuracy in FDG-PET/CT scans, addressing challenges in distinguishing tumors from physiological uptake.

Contribution

It presents a new optimization approach for U-Net tailored for FDG-PET/CT tumor segmentation, advancing the state-of-the-art in medical image analysis.

Findings

Enhanced segmentation accuracy demonstrated on AutoPET dataset

Improved differentiation between tumor and normal tissue uptake

Method outperforms existing segmentation techniques

Abstract

Tumor segmentation in medical imaging is crucial and relies on precise delineation. Fluorodeoxyglucose Positron-Emission Tomography (FDG-PET) is widely used in clinical practice to detect metabolically active tumors. However, FDG-PET scans may misinterpret irregular glucose consumption in healthy or benign tissues as cancer. Combining PET with Computed Tomography (CT) can enhance tumor segmentation by integrating metabolic and anatomic information. FDG-PET/CT scans are pivotal for cancer staging and reassessment, utilizing radiolabeled fluorodeoxyglucose to highlight metabolically active regions. Accurately distinguishing tumor-specific uptake from physiological uptake in normal tissues is a challenging aspect of precise tumor segmentation. The AutoPET challenge addresses this by providing a dataset of 1014 FDG-PET/CT studies, encouraging advancements in accurate tumor segmentation and analysis within the FDG-PET/CT domain. Code: https://github.com/matt3o/AutoPET2-Submission/