Search Results for author: Frederik Barkhof

Found 12 papers, 5 papers with code

A coupled-mechanisms modelling framework for neurodegeneration

no code implementations10 Aug 2023 Tiantian He, Elinor Thompson, Anna Schroder, Neil P. Oxtoby, Ahmed Abdulaal, Frederik Barkhof, Daniel C. Alexander

We account for the heterogeneity of disease by fitting the model at the individual level, allowing the epicenters and rate of progression to vary among subjects.

Feature Importance Model Selection

Computer-aided diagnosis and prediction in brain disorders

no code implementations29 Jun 2022 Vikram Venkatraghavan, Sebastian R. van der Voort, Daniel Bos, Marion Smits, Frederik Barkhof, Wiro J. Niessen, Stefan Klein, Esther E. Bron

Regarding prediction, i. e. estimation of the future 'condition' of the patient, we will zoom in on use cases such as predicting the disease course in multiple sclerosis and predicting patient outcomes after treatment in brain cancer.

Benchmarking Decision Making +1

An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training

no code implementations7 Jun 2022 Daniele Ravi, Frederik Barkhof, Daniel C. Alexander, Lemuel Puglisi, Geoffrey JM Parker, Arman Eshaghi

To tackle this problem, we propose a framework with four main components: 1) artefact generators inspired by magnetic resonance physics to corrupt brain MRI scans and augment a training dataset, 2) abstract and engineered features to represent images compactly, 3) a feature selection process depending on the artefact class to improve classification, and 4) SVM classifiers to identify artefacts.

Computational Efficiency Data Augmentation +1

Disentangling Human Error from Ground Truth in Segmentation of Medical Images

1 code implementation NeurIPS 2020 Le Zhang, Ryutaro Tanno, MouCheng Xu, Chen Jin, Joseph Jacob, Olga Cicarrelli, Frederik Barkhof, Daniel Alexander

In all cases, our method outperforms competing methods and relevant baselines particularly in cases where the number of annotations is small and the amount of disagreement is large.

Medical Image Segmentation Segmentation

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

4 code implementations9 Feb 2020 Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young, Esther E. Bron, Arthur W. Toga, Michael W. Weiner, Frederik Barkhof, Nick C. Fox, Arman Eshaghi, Tina Toni, Marcin Salaterski, Veronika Lunina, Manon Ansart, Stanley Durrleman, Pascal Lu, Samuel Iddi, Dan Li, Wesley K. Thompson, Michael C. Donohue, Aviv Nahon, Yarden Levy, Dan Halbersberg, Mariya Cohen, Huiling Liao, Tengfei Li, Kaixian Yu, Hongtu Zhu, Jose G. Tamez-Pena, Aya Ismail, Timothy Wood, Hector Corrada Bravo, Minh Nguyen, Nanbo Sun, Jiashi Feng, B. T. Thomas Yeo, Gang Chen, Ke Qi, Shiyang Chen, Deqiang Qiu, Ionut Buciuman, Alex Kelner, Raluca Pop, Denisa Rimocea, Mostafa M. Ghazi, Mads Nielsen, Sebastien Ourselin, Lauge Sorensen, Vikram Venkatraghavan, Keli Liu, Christina Rabe, Paul Manser, Steven M. Hill, James Howlett, Zhiyue Huang, Steven Kiddle, Sach Mukherjee, Anais Rouanet, Bernd Taschler, Brian D. M. Tom, Simon R. White, Noel Faux, Suman Sedai, Javier de Velasco Oriol, Edgar E. V. Clemente, Karol Estrada, Leon Aksman, Andre Altmann, Cynthia M. Stonnington, Yalin Wang, Jianfeng Wu, Vivek Devadas, Clementine Fourrier, Lars Lau Raket, Aristeidis Sotiras, Guray Erus, Jimit Doshi, Christos Davatzikos, Jacob Vogel, Andrew Doyle, Angela Tam, Alex Diaz-Papkovich, Emmanuel Jammeh, Igor Koval, Paul Moore, Terry J. Lyons, John Gallacher, Jussi Tohka, Robert Ciszek, Bruno Jedynak, Kruti Pandya, Murat Bilgel, William Engels, Joseph Cole, Polina Golland, Stefan Klein, Daniel C. Alexander

TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.

Alzheimer's Disease Detection Disease Prediction

Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia

no code implementations3 Dec 2019 Daniele Ravi, Stefano B. Blumberg, Silvia Ingala, Frederik Barkhof, Daniel C. Alexander, Neil P. Oxtoby

To evaluate our approach, we trained the framework on 9852 T1-weighted MRI scans from 876 participants in the Alzheimer's Disease Neuroimaging Initiative dataset and held out a separate test set of 1283 MRI scans from 170 participants for quantitative and qualitative assessment of the personalised time series of synthetic images.

Image Quality Assessment Super-Resolution +4

Cannot find the paper you are looking for? You can Submit a new open access paper.