58 citations · 115 across the 14 of their papers we have counts for
14 papers
Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
Kang Wang, Chen Qin, Zhang Shi +46
Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these models is highly dependent on…
Towards Automatic Scoring of Spinal X-ray for Ankylosing Spondylitis
Yuanhan Mo, Yao Chen, Aimee Readie +3
Manually grading structural changes with the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) on spinal X-ray imaging is costly and time-consuming due to bone shape comp…
VertXNet: An Ensemble Method for Vertebrae Segmentation and Identification of Spinal X-Ray
Yao Chen, Yuanhan Mo, Aimee Readie +5
Reliable vertebrae annotations are key to perform analysis of spinal X-ray images. However, obtaining annotation of vertebrae from those images is usually carried out manually due…
The Extreme Cardiac MRI Analysis Challenge under Respiratory Motion (CMRxMotion)
Shuo Wang, Chen Qin, Chengyan Wang +12
The quality of cardiac magnetic resonance (CMR) imaging is susceptible to respiratory motion artifacts. The model robustness of automated segmentation techniques in face of real-wo…
VertXNet: Automatic Segmentation and Identification of Lumbar and Cervical Vertebrae from Spinal X-ray Images
Yao Chen, Yuanhan Mo, Aimee Readie +3
Manual annotation of vertebrae on spinal X-ray imaging is costly and time-consuming due to bone shape complexity and image quality variations. In this study, we address this challe…
Suggestive Annotation of Brain MR Images with Gradient-guided Sampling
Chengliang Dai, Shuo Wang, Yuanhan Mo +3
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. The success of m…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
Chengliang Dai, Shuo Wang, Yuanhan Mo +4
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. As a data-driven…
Deep Generative Model-based Quality Control for Cardiac MRI Segmentation
Shuo Wang, Giacomo Tarroni, Chen Qin +7
In recent years, convolutional neural networks have demonstrated promising performance in a variety of medical image segmentation tasks. However, when a trained segmentation model…
Efficient Deep Representation Learning by Adaptive Latent Space Sampling
Yuanhan Mo, Shuo Wang, Chengliang Dai +4
Supervised deep learning requires a large amount of training samples with annotations (e.g. label class for classification task, pixel- or voxel-wised label map for segmentation ta…
Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction
Shuo Wang, Chengliang Dai, Yuanhan Mo +3
Gliomas are the most common malignant brain tumourswith intrinsic heterogeneity. Accurate segmentation of gliomas and theirsub-regions on multi-parametric magnetic resonance images…
Transfer Learning from Partial Annotations for Whole Brain Segmentation
Chengliang Dai, Yuanhan Mo, Elsa Angelini +2
Brain MR image segmentation is a key task in neuroimaging studies. It is commonly conducted using standard computational tools, such as FSL, SPM, multi-atlas segmentation etc, whic…
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
Hao Dong, Guang Yang, Fangde Liu +2
A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has…
The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation
Yuanhan Mo, Fangde Liu, Douglas McIlwraith +4
Precise segmentation of the left ventricle (LV) within cardiac MRI images is a prerequisite for the quantitative measurement of heart function. However, this task is challenging du…