Work

Sine Wave Normalization for Deep Learning-Based Tumor Segmentation in CT/PET Imaging

Year: 2024

Type: preprint

Abstract: This report presents a normalization block for automated tumor segmentation in CT/PET scans, developed for the autoPET III Challenge. The key innovation is the introduction of the SineNormal, which applies periodic sine transformations to PET data to enhance lesion detection. By highlighting intensity variations and producing concentric ring patterns in PET highlighted regions, the model aims to improve segmentation accuracy, particularly for challenging multitracer PET datasets. The code for this project is available on GitHub (https://github.com/BBQtime/Sine-Wave-Normalization-for-Deep-Learning-Based-Tumor-Segmentation-in-CT-PET).

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2409.13410

Authors: Ren, Jintao, Li, Muheng, Korreman, Stine Sofia

Institutions: Aarhus University, Aarhus University Hospital, Paul Scherrer Institute

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W4403758480

Cites:

Cited by: 1

Related to: 10