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20232026
most citedSegment Anything Model for Medical Images?

502 citations · 503 across the 4 of their papers we have counts for

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5 papers · 1 filter

eess.IV2025

Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound

Jian Wang, Qiongying Ni, Hongkui Yu +15

Accurate fetal birth weight (FBW) estimation is essential for optimizing delivery decisions and reducing perinatal mortality. However, clinical methods for FBW estimation are ineff…

eess.IV2025

UltraTwin: Towards Cardiac Anatomical Twin Generation from Multi-view 2D Ultrasound

Junxuan Yu, Yaofei Duan, Yuhao Huang +22

Echocardiography is routine for cardiac examination. However, 2D ultrasound (US) struggles with accurate metric calculation and direct observation of 3D cardiac structures. Moreove…

eess.IV2024★ 1 cited

Explainable and Controllable Motion Curve Guided Cardiac Ultrasound Video Generation

Junxuan Yu, Rusi Chen, Yongsong Zhou +7

Echocardiography video is a primary modality for diagnosing heart diseases, but the limited data poses challenges for both clinical teaching and machine learning training. Recently…

eess.IV2023

FFPN: Fourier Feature Pyramid Network for Ultrasound Image Segmentation

Chaoyu Chen, Xin Yang, Rusi Chen +7

Ultrasound (US) image segmentation is an active research area that requires real-time and highly accurate analysis in many scenarios. The detect-to-segment (DTS) frameworks have be…

eess.IV2023★ 502 cited

Segment Anything Model for Medical Images?

Yuhao Huang, Xin Yang, Lian Liu +16

The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It has achieved impressive results on various natural image segmentation tasks. Howev…