An Organ-Guided Lightweight Multi-frame Integration Network for Real-Time Abdominal Lesion Detection in Ultrasound Video Streams.

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Abstract

Abdominal ultrasound is widely used for the routine screening of hepatobiliary and renal diseases because it is safe, inexpensive and broadly accessible. In daily clinical practice, however, lesion detection remains highly operator-dependent, particularly when sonographers must identify subtle abnormalities across different abdominal organs under low-contrast, noisy and continuously changing views. Missed or delayed recognition may affect subsequent diagnostic assessment and patient management. Although deep learning has shown promise in ultrasound analysis, most existing methods focus on single-organ or static-image settings, which limits their applicability to real-time abdominal screening. These challenges motivate the development of a unified real-time detection framework that can better support lesion identification in multi-organ ultrasound videos. We propose the Organ-Guided Lightweight Multi-frame Integration (OGLMFI) framework based on YOLOv11 for unified lesion detection across the liver, gallbladder and kidney in ultrasound videos. The framework incorporates an Organ-Guided Feature Filtering module that uses organ segmentation priors to suppress background interference and enhance lesion discrimination. It also includes a Lightweight Multi-frame Integration module, which adopts a dual-branch fusion strategy to efficiently integrate temporal information from consecutive frames using only historical context, thereby preserving causal real-time inference. On the test set of 205 clinical videos (12,964 annotated frames), OGLMFI achieved a Recall of 0.691, a mean average precision at an intersection-over-union threshold of 0.5 (mAP50) of 0.703, an mAP50-95 of 0.510 and an inference speed of 52.3 frames per second. Compared with the YOLOv11-L baseline, OGLMFI improved Recall by 10.7%, mAP50 by 3.2% and mAP50-95 by 8.1%, while maintaining real-time performance. Among the evaluated methods, OGLMFI achieved the highest Recall, mAP50 and mAP50-95. Category-wise analysis further showed the highest average precision at an intersection-over-union threshold of 0.5 across all seven lesion categories, including 66.8% for gallbladder stone and 53.7% for gallbladder polyp. By integrating organ-guided feature filtering and causal multi-frame feature fusion, OGLMFI improves real-time lesion detection in abdominal ultrasound videos. The proposed framework provides a practical unified solution for lesion detection across three abdominal organs and may serve as a useful computer-aided tool for routine abdominal ultrasound screening.

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