Publications (41)
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…
Vestibular schwannoma growth prediction from longitudinal MRI by time conditioned neural fields
Yunjie Chen, Jelmer M. Wolterink, Olaf M. Neve +5
Vestibular schwannomas (VS) are benign tumors that are generally managed by active surveillance with MRI examination. To further assist clinical decision-making and avoid overtreat…
CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation
Yunjie Chen, Marius Staring, Olaf M. Neve +5
Multi-sequence magnetic resonance imaging (MRI) has found wide applications in both modern clinical studies and deep learning research. However, in clinical practice, it frequently…
UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction
Donghang Lyu, Chinmay Rao, Marius Staring +4
Cardiac magnetic resonance imaging (CMR) is vital for diagnosing heart diseases, but long scan time remains a major drawback. To address this, accelerated imaging techniques have b…
Enhancing Implicit Neural Representations with Image Feature Embedding for Unsupervised Cardiac Cine MRI Reconstruction
Donghang Lyu, Marius Staring, Yiming Dong +3
Cardiac cine Magnetic Resonance Imaging (MRI) is a critical diagnostic tool that provides dynamic insights for radiologists. To accelerate acquisition, under-sampled k-space data i…
Deep Unrolled Networks in Representation Space Applied to MRI Reconstruction
Efe Ilıcak, Baris Imre, Chloé Najac +4
Deep unrolled networks (DUNs) integrate physical forward models with learned regularization in cascaded network architectures, achieving exceptional performance in inverse problems…