43 citations · 114 across the 25 of their papers we have counts for
10 papers · 1 filter
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations
Hong Liu, Dong Wei, Donghuan Lu +3
Layer segmentation is important to quantitative analysis of retinal optical coherence tomography (OCT). Recently, deep learning based methods have been developed to automate this t…
Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views
Dong Wei, Yawen Huang, Donghuan Lu +2
Background: View planning for the acquisition of cardiac magnetic resonance (CMR) imaging remains a demanding task in clinical practice. Purpose: Existing approaches to its automat…
M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities
Hong Liu, Dong Wei, Donghuan Lu +3
Multimodal magnetic resonance imaging (MRI) provides complementary information for sub-region analysis of brain tumors. Plenty of methods have been proposed for automatic brain tum…
Segmentation-guided Domain Adaptation and Data Harmonization of Multi-device Retinal Optical Coherence Tomography using Cycle-Consistent Generative Adversarial Networks
Shuo Chen, Da Ma, Sieun Lee +7
Optical Coherence Tomography(OCT) is a non-invasive technique capturing cross-sectional area of the retina in micro-meter resolutions. It has been widely used as a auxiliary imagin…
Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration
Jiashun Chen, Donghuan Lu, Yu Zhang +5
Recently, deep-learning-based approaches have been widely studied for deformable image registration task. However, most efforts directly map the composite image representation to s…
Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images
Hong Liu, Dong Wei, Donghuan Lu +4
Automated surface segmentation of retinal layer is important and challenging in analyzing optical coherence tomography (OCT). Recently, many deep learning based methods have been d…
Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease using Generative Adversarial Network
Da Ma, Donghuan Lu, Karteek Popuri +1
Frontotemporal dementia and Alzheimer's disease are two common forms of dementia and are easily misdiagnosed as each other due to their similar pattern of clinical symptoms. Differ…
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation
Zhe Xu, Yixin Wang, Donghuan Lu +6
Semi-supervised learning has substantially advanced medical image segmentation since it alleviates the heavy burden of acquiring the costly expert-examined annotations. Especially,…
Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation
Zhe Xu, Donghuan Lu, Yixin Wang +5
Manually segmenting the hepatic vessels from Computer Tomography (CT) is far more expertise-demanding and laborious than other structures due to the low-contrast and complex morpho…
Cascaded Deep Neural Networks for Retinal Layer Segmentation of Optical Coherence Tomography with Fluid Presence
Donghuan Lu, Morgan Heisler, Da Ma +5
Optical coherence tomography (OCT) is a non-invasive imaging technology which can provide micrometer-resolution cross-sectional images of the inner structures of the eye. It is wid…