LUS-DET: Liver Ultrasound Open-Vocabulary Object Detection

In liver ultrasound, the acquisition of standard scanning planes serves as a prerequisite for reliable lesion assessment. In clinical practice, physicians make diagnostic decisions by jointly interpreting the spatial configuration of key anatomical structures within standard planes and local lesion features. However, existing studies commonly treat standard plane recognition and lesion detection as two separate tasks, lacking a unified modeling approach that reflects their semantic continuity and clinical interdependence. Inspired by the diagnostic workflow of liver ultrasound, we propose LUS-DET, an open-vocabulary object detection framework designed to semantically bridge liver ultrasound standard plane analysis (LUSP) and liver ultrasound disease diagnosis (LUDD) through text-guided modeling. Specifically, we curate a retrospective LUSP dataset and develop a region-text alignment mechanism linking 44,669 region-caption pairs across 12 anatomical categories to enable in-domain open-vocabulary pretraining. Building upon this alignment, we introduce object prompts to guide zero-shot lesion detection in an open-source LUDD task without using any lesion-specific annotations. Experimental results demonstrate that LUS-DET not only achieves competitive zero-shot performance, but also exhibits superior accuracy and robustness during end-to-end fine-tuning compared to conventional detection baselines. To the best of our knowledge, this is the first study to propose a clinically coherent modelling paradigm that unifies standard plane localisation and lesion analysis in liver ultrasound, providing a new direction for structure-aware and workflow-aligned AI systems in medical imaging.