Anatomy-Aware Lymphoma Lesion Detection in Whole-Body PET/CT

TL;DR

This paper demonstrates that incorporating anatomical priors significantly enhances CNN-based lymphoma lesion detection in PET/CT images, while having minimal effect on transformer-based models, emphasizing the importance of anatomical context.

Contribution

The study introduces the integration of organ segmentation masks as priors into deep learning models for improved lesion detection in PET/CT scans, comparing CNN and transformer architectures.

Findings

Anatomical priors improve CNN-based detection performance.

Transformers show minimal performance change with anatomical priors.

CNN models outperform Swin Transformer in this task.

Abstract

Early cancer detection is crucial for improving patient outcomes, and 18F FDG PET/CT imaging plays a vital role by combining metabolic and anatomical information. Accurate lesion detection remains challenging due to the need to identify multiple lesions of varying sizes. In this study, we investigate the effect of adding anatomy prior information to deep learning-based lesion detection models. In particular, we add organ segmentation masks from the TotalSegmentator tool as auxiliary inputs to provide anatomical context to nnDetection, which is the state-of-the-art for lesion detection, and Swin Transformer. The latter is trained in two stages that combine self-supervised pre-training and supervised fine-tuning. The method is tested in the AutoPET and Karolinska lymphoma datasets. The results indicate that the inclusion of anatomical priors substantially improves the detection performance within the nnDetection framework, while it has almost no impact on the performance of the vision transformer. Moreover, we observe that Swin Transformer does not offer clear advantages over conventional convolutional neural network (CNN) encoders used in nnDetection. These findings highlight the critical role of the anatomical context in cancer lesion detection, especially in CNN-based models.