b'Wenjia Bai'

Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling

Machine learning has been widely adopted for medical image analysis in r...
06/26/2020 ∙ by Chengliang Dai, et al. ∙ 0 ∙ share

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Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction

Gliomas are the most common malignant brain tumourswith intrinsic hetero...
11/19/2019 ∙ by Shuo Wang, et al. ∙ 0 ∙ share

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Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image...
11/09/2019 ∙ by Chen Chen, et al. ∙ 95 ∙ share

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Transfer Learning from Partial Annotations for Whole Brain Segmentation

Brain MR image segmentation is a key task in neuroimaging studies. It is...
08/28/2019 ∙ by Chengliang Dai, et al. ∙ 0 ∙ share

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Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image

Accelerating the acquisition of magnetic resonance imaging (MRI) is a ch...
08/20/2019 ∙ by Chen Qin, et al. ∙ 4 ∙ share

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Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation

In this work, we present a fully automatic method to segment cardiac str...
08/20/2019 ∙ by Chen Chen, et al. ∙ 0 ∙ share

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Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images

Cardiac MR image segmentation is essential for the morphological and fun...
07/23/2019 ∙ by Chen Chen, et al. ∙ 5 ∙ share

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VS-Net: Variable splitting network for accelerated parallel MRI reconstruction

In this work, we propose a deep learning approach for parallel magnetic ...
07/19/2019 ∙ by Jinming Duan, et al. ∙ 2 ∙ share

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Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction

In the recent years, convolutional neural networks have transformed the ...
07/05/2019 ∙ by Wenjia Bai, et al. ∙ 6 ∙ share

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Explainable Shape Analysis through Deep Hierarchical Generative Models: Application to Cardiac Remodeling

Quantification of anatomical shape changes still relies on scalar global...
06/28/2019 ∙ by Carlo Biffi, et al. ∙ 1 ∙ share

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Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study

Background: The trend towards large-scale studies including population i...
01/27/2019 ∙ by Robert Robinson, et al. ∙ 38 ∙ share

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Multi-Task Learning for Left Atrial Segmentation on GE-MRI

Segmentation of the left atrium (LA) is crucial for assessing its anatom...
10/31/2018 ∙ by Chen Chen, et al. ∙ 4 ∙ share

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A Comprehensive Approach for Learning-based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks

In the clinical routine, short axis (SA) cine cardiac MR (CMR) image sta...
10/03/2018 ∙ by Giacomo Tarroni, et al. ∙ 8 ∙ share

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Automatic 3D bi-ventricular segmentation of cardiac images by a shape-constrained multi-task deep learning approach

Deep learning approaches have achieved state-of-the-art performance in c...
08/26/2018 ∙ by Jinming Duan, et al. ∙ 0 ∙ share

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Recurrent neural networks for aortic image sequence segmentation with sparse annotations

Segmentation of image sequences is an important task in medical image an...
08/01/2018 ∙ by Wenjia Bai, et al. ∙ 6 ∙ share

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Deep nested level sets: Fully automated segmentation of cardiac MR images in patients with pulmonary hypertension

In this paper we introduce a novel and accurate optimisation method for ...
07/27/2018 ∙ by Jinming Duan, et al. ∙ 12 ∙ share

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Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling

Alterations in the geometry and function of the heart define well-establ...
07/18/2018 ∙ by Carlo Biffi, et al. ∙ 8 ∙ share

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Real-time Prediction of Segmentation Quality

Recent advances in deep learning based image segmentation methods have e...
06/16/2018 ∙ by Robert Robinson, et al. ∙ 14 ∙ share

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Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences

Cardiac motion estimation and segmentation play important roles in quant...
06/11/2018 ∙ by Chen Qin, et al. ∙ 0 ∙ share

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Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents

We propose a fully automatic method to find standardized view planes in ...
06/08/2018 ∙ by Amir Alansary, et al. ∙ 10 ∙ share

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Domain Adaptation for MRI Organ Segmentation using Reverse Classification Accuracy

The variations in multi-center data in medical imaging studies have brou...
06/01/2018 ∙ by Vanya V. Valindria, et al. ∙ 0 ∙ share

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Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks

Measurement of head biometrics from fetal ultrasonography images is of k...
04/24/2018 ∙ by Matthew Sinclair, et al. ∙ 6 ∙ share

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Learning-Based Quality Control for Cardiac MR Images

The effectiveness of a cardiovascular magnetic resonance (CMR) scan depe...
03/25/2018 ∙ by Giacomo Tarroni, et al. ∙ 0 ∙ share

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Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Deep learning approaches such as convolutional neural nets have consiste...
11/04/2017 ∙ by Konstantinos Kamnitsas, et al. ∙ 0 ∙ share

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Human-level CMR image analysis with deep fully convolutional networks

Cardiovascular magnetic resonance (CMR) imaging is a standard imaging mo...
10/25/2017 ∙ by Wenjia Bai, et al. ∙ 0 ∙ share

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Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

Incorporation of prior knowledge about organ shape and location is key t...
05/22/2017 ∙ by Ozan Oktay, et al. ∙ 0 ∙ share

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DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks

In this paper, we propose DeepCut, a method to obtain pixelwise object s...
05/25/2016 ∙ by Martin Rajchl, et al. ∙ 0 ∙ share

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Wenjia Bai

Featured Co-authors

Daniel Rueckert

Chen Chen

Ben Glocker

Bernhard Kainz

Shuo Wang

Yike Guo

Andrew P. King

Jo Schlemper

Jinming Duan

Konstantinos Kamnitsas

Ozan Oktay

Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling

Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction

Deep learning for cardiac image segmentation: A review

Transfer Learning from Partial Annotations for Whole Brain Segmentation

Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image

Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation

Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images

VS-Net: Variable splitting network for accelerated parallel MRI reconstruction

Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction

Explainable Shape Analysis through Deep Hierarchical Generative Models: Application to Cardiac Remodeling

Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study

Multi-Task Learning for Left Atrial Segmentation on GE-MRI

A Comprehensive Approach for Learning-based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks

Automatic 3D bi-ventricular segmentation of cardiac images by a shape-constrained multi-task deep learning approach

Recurrent neural networks for aortic image sequence segmentation with sparse annotations

Deep nested level sets: Fully automated segmentation of cardiac MR images in patients with pulmonary hypertension

Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling

Real-time Prediction of Segmentation Quality

Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences

Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents

Domain Adaptation for MRI Organ Segmentation using Reverse Classification Accuracy

Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks

Learning-Based Quality Control for Cardiac MR Images

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Human-level CMR image analysis with deep fully convolutional networks

Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks