7 citations · 7 across the 5 of their papers we have counts for
6 papers
ReG-SAM: Reference Graph-Driven SAM for 2D Foundational Vessel Segmentation
Donghang Lyu, Zichen Zhang, Oleh Dzyubachyk +1
Vessel segmentation in medical images is essential for many clinical tasks, ranging from diagnosis to treatment planning. However, it remains challenging due to complex vascular mo…
Physics-Guided Dual-Domain Network with Attention-Based Fusion for Portable MRI Reconstruction
Efe Ilıcak, Baris Imre, Chloé Najac +4
Portable low-field magnetic resonance imaging (MRI) systems have gained renewed interest owing to their cost effectiveness and point-of-care imaging capabilities. Yet, portable MRI…
Efficient Large-Deformation Medical Image Registration via Recurrent Dynamic Correlation
Tianran Li, Marius Staring, Yuchuan Qiao
Deformable image registration estimates voxel-wise correspondences between images through spatial transformations, and plays a key role in medical imaging. While deep learning meth…
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Jun Ma, Feifei Li, Sumin Kim +79
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive co…
MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day
Donghang Lyu, Ruochen Gao, Marius Staring
Medical image segmentation involves partitioning medical images into meaningful regions, with a focus on identifying anatomical structures and lesions. It has broad applications in…
Swin-LiteMedSAM: A Lightweight Box-Based Segment Anything Model for Large-Scale Medical Image Datasets
Ruochen Gao, Donghang Lyu, Marius Staring
Medical imaging is essential for the diagnosis and treatment of diseases, with medical image segmentation as a subtask receiving high attention. However, automatic medical image se…