most citedEvaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge

29 citations · 29 across the 1 of their papers we have counts for

collaborators

5 papers

eess.IV2019

Automated Segmentation of Optical Coherence Tomography Angiography Images: Benchmark Data and Clinically Relevant Metrics

Ylenia Giarratano, Eleonora Bianchi, Calum Gray +4

Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging modality for the visualisation of microvasculature in vivo that has encountered broad adoption in re…

cs.CV201929 cited

Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge

Xiahai Zhuang, Lei Li, Christian Payer +31

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable…

cs.CV2018

Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks

Chengjia Wang, Gillian Macnaught, Giorgos Papanastasiou +2

Recently, the cycle-consistent generative adversarial networks (CycleGAN) has been widely used for synthesis of multi-domain medical images. The domain-specific nonlinear deformati…

cs.CV2018

A two-stage 3D Unet framework for multi-class segmentation on full resolution image

Chengjia Wang, Tom MacGillivray, Gillian Macnaught +2

Deep convolutional neural networks (CNNs) have been intensively used for multi-class segmentation of data from different modalities and achieved state-of-the-art performances. Howe…

q-bio.NC2018

Machine learning of neuroimaging to diagnose cognitive impairment and dementia: a systematic review and comparative analysis

Enrico Pellegrini, Lucia Ballerini, Maria del C. Valdes Hernandez +11

INTRODUCTION: Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear. METHODS: We systematically reviewed t…