Gastro-intestinal lesion segmentation using deep learning: organ-based versus whole-body training

F-Fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging is a valuable diagnostic tool in oncology with a wide range of clinical applications for cancer diagnosis, staging, and monitoring treatment response. Accurate tumor segmentation from these images is vital for understanding the biochemical and physiological alterations within the tumors. End-to-end deep learning approaches enable rapid and reproducible tumor identification and extraction, surpassing manual and semi-automatic methods. Compared to other organs, intestinal tumor segmentation poses a significant challenge due to its complex anatomical shape and acute non-malignant findings. This study aims to investigate the impact of training data homogeneity on the segmentation results of intestinal tumors using Convolutional Neural Networks (CNNs). To achieve this, we propose an organ-based approach where the training data is limited to the small intestine region. We will compare the results obtained by the organ-based approach with those from a model trained on the whole-body PET/CT data. In the whole-body approach, tumor segmentation predictions for the intestine are extracted from the results obtained by training on the whole-body data. Quantitative results show that the organ-based approach outperform the whole-body method in segmentation of intestinal tumors. Whole-body and organ-based approaches generated a dice score (mean±std) of 0.63±0.30 and 0.78±0.21 for the whole-body and organ-based approaches respectively with p-value less than 0.0001. The lesion level analysis yielded F1 scores of 0.79 for the whole-body approach and 0.86 for the organ-based approach.