Enhancing Lesion Segmentation in PET/CT Imaging with Deep Learning and Advanced Data Preprocessing Techniques

TL;DR

This paper presents a deep learning approach combined with advanced data preprocessing to improve lesion segmentation accuracy in PET/CT imaging, aiming to standardize methods and enhance cancer diagnosis.

Contribution

It introduces novel preprocessing and augmentation techniques, including non-zero normalization and RandGaussianSharpen, to improve model robustness and generalizability in PET/CT lesion segmentation.

Findings

Enhanced segmentation accuracy demonstrated on AutoPET datasets

Robustness of the model improved through advanced augmentation techniques

Open-source code available for reproducibility

Abstract

The escalating global cancer burden underscores the critical need for precise diagnostic tools in oncology. This research employs deep learning to enhance lesion segmentation in PET/CT imaging, utilizing a dataset of 900 whole-body FDG-PET/CT and 600 PSMA-PET/CT studies from the AutoPET challenge III. Our methodical approach includes robust preprocessing and data augmentation techniques to ensure model robustness and generalizability. We investigate the influence of non-zero normalization and modifications to the data augmentation pipeline, such as the introduction of RandGaussianSharpen and adjustments to the Gamma transform parameter. This study aims to contribute to the standardization of preprocessing and augmentation strategies in PET/CT imaging, potentially improving the diagnostic accuracy and the personalized management of cancer patients. Our code will be open-sourced and available at https://github.com/jiayiliu-pku/DC2024.