An Organ-Guided Lightweight Multi-frame Integration Network for Real-Time Abdominal Lesion Detection in Ultrasound Video Streams.
New real-time AI framework improved lesion recall by 10.7% over baseline in abdominal ultrasound video.
An Organ-Guided Lightweight Multi-frame Integration Network for Real-Time Abdominal Lesion Detection in Ultrasound Video Streams.
Abdominal ultrasound is widely used for the routine screening of hepatobiliary and renal diseases because it is safe, inexpensive and broadly accessible.
We propose the Organ-Guided Lightweight Multi-frame Integration (Organ-Guided Lightweight Multi-frame Integration) framework based on YOLOv11 for unified lesion detection across the liver, gallbladder and kidney in ultrasound videos.
On the test set of 205 clinical videos (12,964 annotated frames), Organ-Guided Lightweight Multi-frame Integration 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.
Among the evaluated methods, Organ-Guided Lightweight Multi-frame Integration 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, Organ-Guided Lightweight Multi-frame Integration 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.