Search Results for author: Tom MacGillivray

Found 12 papers, 4 papers with code

OCTolyzer: Fully automatic analysis toolkit for segmentation and feature extracting in optical coherence tomography (OCT) and scanning laser ophthalmoscopy (SLO) data

no code implementations19 Jul 2024 Jamie Burke, Justin Engelmann, Samuel Gibbon, Charlene Hamid, Diana Moukaddem, Dan Pugh, Tariq Farrah, Niall Strang, Neeraj Dhaun, Tom MacGillivray, Stuart King, Ian J. C. MacCormick

Results: At the population-level, choroid region metrics were highly reproducible (Mean absolute error/Pearson/Spearman correlation for macular volume choroid thickness (CT):6. 7$\mu$m/0. 9933/0. 9969, macular B-scan CT:11. 6$\mu$m/0. 9858/0. 9889, peripapillary CT:5. 0$\mu$m/0. 9942/0. 9940).

SLOctolyzer: Fully automatic analysis toolkit for segmentation and feature extracting in scanning laser ophthalmoscopy images

no code implementations24 Jun 2024 Jamie Burke, Samuel Gibbon, Justin Engelmann, Adam Threlfall, Ylenia Giarratano, Charlene Hamid, Stuart King, Ian J. C. MacCormick, Tom MacGillivray

The segmentation module use deep learning methods to delineate retinal anatomy, while the measurement module quantifies key retinal vascular features such as vessel complexity, density, tortuosity, and calibre.

Anatomy Segmentation

A publicly available vessel segmentation algorithm for SLO images

no code implementations29 Nov 2023 Adam Threlfall, Samuel Gibbon, James Cameron, Tom MacGillivray

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.

Retinal Vessel Segmentation Segmentation +1

An open-source deep learning algorithm for efficient and fully-automatic analysis of the choroid in optical coherence tomography

1 code implementation3 Jul 2023 Jamie Burke, Justin Engelmann, Charlene Hamid, Megan Reid-Schachter, Tom Pearson, Dan Pugh, Neeraj Dhaun, Stuart King, Tom MacGillivray, Miguel O. Bernabeu, Amos Storkey, Ian J. C. MacCormick

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

1 code implementation19 Jun 2023 Jamie Burke, Dan Pugh, Tariq Farrah, Charlene Hamid, Emily Godden, Tom MacGillivray, Neeraj Dhaun, J. Kenneth Baillie, Stuart King, Ian J. C. MacCormick

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).

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

no code implementations12 Aug 2018 Chengjia Wang, Gillian Macnaught, Giorgos Papanastasiou, Tom MacGillivray, David Newby

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

Image Generation

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

no code implementations12 Apr 2018 Chengjia Wang, Tom MacGillivray, Gillian Macnaught, Guang Yang, David Newby

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.

Image Super-Resolution Segmentation

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

no code implementations5 Apr 2018 Enrico Pellegrini, Lucia Ballerini, Maria del C. Valdes Hernandez, Francesca M. Chappell, Victor González-Castro, Devasuda Anblagan, Samuel Danso, Susana Muñoz Maniega, Dominic Job, Cyril Pernet, Grant Mair, Tom MacGillivray, Emanuele Trucco, Joanna Wardlaw

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.

BIG-bench Machine Learning

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