MCAT: Visual Query-Based Localization of Standard Anatomical Clips in Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer
This work addresses the challenge of efficient and accurate standard plane acquisition in fetal ultrasound for sonographers, particularly in low-resource settings, though it is incremental as it builds on existing video localization methods.
The paper tackles the problem of manually selecting standard anatomical frames in fetal ultrasound videos, which is time-consuming and variable, by introducing MCAT, a visual query-based method that localizes video clips containing standard frames for a given anatomy, achieving 10-13% mIoU improvements on ultrasound datasets and 5.35% on a natural image dataset with 96% fewer tokens.
Accurate standard plane acquisition in fetal ultrasound (US) videos is crucial for fetal growth assessment, anomaly detection, and adherence to clinical guidelines. However, manually selecting standard frames is time-consuming and prone to intra- and inter-sonographer variability. Existing methods primarily rely on image-based approaches that capture standard frames and then classify the input frames across different anatomies. This ignores the dynamic nature of video acquisition and its interpretation. To address these challenges, we introduce Multi-Tier Class-Aware Token Transformer (MCAT), a visual query-based video clip localization (VQ-VCL) method, to assist sonographers by enabling them to capture a quick US sweep. By then providing a visual query of the anatomy they wish to analyze, MCAT returns the video clip containing the standard frames for that anatomy, facilitating thorough screening for potential anomalies. We evaluate MCAT on two ultrasound video datasets and a natural image VQ-VCL dataset based on Ego4D. Our model outperforms state-of-the-art methods by 10% and 13% mIoU on the ultrasound datasets and by 5.35% mIoU on the Ego4D dataset, using 96% fewer tokens. MCAT's efficiency and accuracy have significant potential implications for public health, especially in low- and middle-income countries (LMICs), where it may enhance prenatal care by streamlining standard plane acquisition, simplifying US-based screening, diagnosis and allowing sonographers to examine more patients.