Automated hepatocellular carcinoma detection and segmentation in abbreviated MRI using vision transformers

Hepatocellular carcinoma (HCC) surveillance primarily relies on ultrasound (U/S), which often exhibits decreased sensitivity in high-risk populations, such as individuals with cirrhosis or obesity. Abbreviated magnetic resonance imaging (AMRI) offers a potential alternative by employing targeted MRI sequences to enhance HCC detection. AMRI encompasses three primary strategies: non-contrast, dynamic contrast-enhanced, and hepatobiliary phase imaging, showing potential for overcoming U/S limitations in these populations. This study investigates the application of deep learning (DL) techniques to automate HCC tumor detection and segmentation within dynamic contrast-enhanced (Dyn-AMRI) protocols. Specifically, we leverage the capabilities of Vision Transformers (ViTs) to analyze complex image data and extract relevant features. Additionally, a novel heuristic is introduced to enhance the segmentation performance of the MedNeXt architecture. Our aim is to develop a robust DL pipeline for accurate HCC detection and segmentation on Dyn-AMRI, ultimately improving diagnostic outcomes.