Search Results for author:
Found 9 papers, 4 papers with code
Choroidalyzer: An open-source, end-to-end pipeline for choroidal analysis in optical coherence tomography
We analysed segmentation agreement (AUC, Dice) and choroid metrics agreement (Pearson, Spearman, mean absolute error (MAE)) in internal and external test sets.
A publicly available vessel segmentation algorithm for SLO images
Background and Objective: Infra-red scanning laser ophthalmoscope (IRSLO) images are akin to colour fundus photographs in displaying the posterior pole and retinal vasculature fine detail.
An open-source deep learning algorithm for efficient and fully-automatic analysis of the choroid in optical coherence tomography
Results: DeepGPET achieves excellent agreement with GPET on data from 3 clinical studies (AUC=0. 9994, Dice=0. 9664; Pearson correlation of 0. 8908 for choroidal thickness and 0. 9082 for choroidal area), while reducing the mean processing time per image on a standard laptop CPU from 34. 49s ($\pm$15. 09) using GPET to 1. 25s ($\pm$0. 10) using DeepGPET.
Evaluation of an automated choroid segmentation algorithm in a longitudinal kidney donor and recipient cohort
Significant associations were mostly stronger with automated CT (eGFR P<0. 001, creatinine P=0. 004, urea P=0. 04) compared to manual CT (eGFR P=0. 002, creatinine P=0. 01, urea P=0. 03).
Automated Segmentation of Optical Coherence Tomography Angiography Images: Benchmark Data and Clinically Relevant Metrics
Our results show that, for the set of images considered, deep learning architectures (U-Net and CS-Net) achieve the best performance.
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge
This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017.
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks
Recently, the cycle-consistent generative adversarial networks (CycleGAN) has been widely used for synthesis of multi-domain medical images.
A two-stage 3D Unet framework for multi-class segmentation on full resolution image
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.
Machine learning of neuroimaging to diagnose cognitive impairment and dementia: a systematic review and comparative analysis
METHODS: We systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy ageing through to dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries.
