Comparative analysis of intestinal tumor segmentation in PET CT scans using organ based and whole body deep learning.

ABSTRACT:

Background

18-Fluoro-deoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) is a valuable imaging tool widely used in the management of cancer patients. Deep learning models excel at segmenting highly metabolic tumors but face challenges in regions with complex anatomy and normal cell uptake, such as the gastro-intestinal tract. Despite these challenges, it remains important to achieve accurate segmentation of gastro-intestinal tumors.

Methods

Here, we present an international multicenter comparative study between a novel organ-focused approach and a whole-body training method to evaluate the effectiveness of training data homogeneity in accurately identifying gastro-intestinal tumors. In the organ-focused method, the training data is limited to cases wit

SUBMITTER: Torkaman M 

PROVIDER: S-EPMC11834234 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature