Search Results for author: Sebastien Ourselin

Found 102 papers, 41 papers with code

An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field

no code implementations9 Aug 2023 Kelly Payette, Alena Uus, Jordina Aviles Verdera, Carla Avena Zampieri, Megan Hall, Lisa Story, Maria Deprez, Mary A. Rutherford, Joseph V. Hajnal, Sebastien Ourselin, Raphael Tomi-Tricot, Jana Hutter

In this study, we introduce a semi-automatic pipeline using quantitative MRI for the fetal body at low field strength resulting in fast and detailed quantitative T2* relaxometry analysis of all major fetal body organs.

Generative AI for Medical Imaging: extending the MONAI Framework

2 code implementations27 Jul 2023 Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot, Petru-Daniel Tudosiu, Jessica Dafflon, Virginia Fernandez, Pedro Sanchez, Julia Wolleb, Pedro F. da Costa, Ashay Patel, Hyungjin Chung, Can Zhao, Wei Peng, Zelong Liu, Xueyan Mei, Oeslle Lucena, Jong Chul Ye, Sotirios A. Tsaftaris, Prerna Dogra, Andrew Feng, Marc Modat, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas.

Anomaly Detection Denoising +2

Deep Homography Prediction for Endoscopic Camera Motion Imitation Learning

no code implementations24 Jul 2023 Martin Huber, Sebastien Ourselin, Christos Bergeles, Tom Vercauteren

In this work, we investigate laparoscopic camera motion automation through imitation learning from retrospective videos of laparoscopic interventions.

Image Registration Imitation Learning

ARHNet: Adaptive Region Harmonization for Lesion-aware Augmentation to Improve Segmentation Performance

1 code implementation2 Jul 2023 Jiayu Huo, Yang Liu, Xi Ouyang, Alejandro Granados, Sebastien Ourselin, Rachel Sparks

In this paper, we propose a foreground harmonization framework (ARHNet) to tackle intensity disparities and make synthetic images look more realistic.

Data Augmentation Image Harmonization

LoViT: Long Video Transformer for Surgical Phase Recognition

1 code implementation15 May 2023 Yang Liu, Maxence Boels, Luis C. Garcia-Peraza-Herrera, Tom Vercauteren, Prokar Dasgupta, Alejandro Granados, Sebastien Ourselin

Our results demonstrate the effectiveness of our approach in achieving state-of-the-art performance of surgical phase recognition on two datasets of different surgical procedures and temporal sequencing characteristics whilst introducing mechanisms that cope with long videos.

Online surgical phase recognition

VideoSum: A Python Library for Surgical Video Summarization

1 code implementation15 Feb 2023 Luis C. Garcia-Peraza-Herrera, Sebastien Ourselin, Tom Vercauteren

It is thus unsurprising that substantial research efforts are made to develop methods aiming at mitigating the scarcity of annotated SDS data.

Video Summarization

SKiT: a Fast Key Information Video Transformer for Online Surgical Phase Recognition

no code implementations ICCV 2023 Yang Liu, Jiayu Huo, Jingjing Peng, Rachel Sparks, Prokar Dasgupta, Alejandro Granados, Sebastien Ourselin

We highlight that the inference time of SKiT is constant, and independent from the input length, making it a stable choice for keeping a record of important global information, that appears on long surgical videos, essential for phase recognition.

Online surgical phase recognition

Denoising diffusion models for out-of-distribution detection

1 code implementation14 Nov 2022 Mark S. Graham, Walter H. L. Pinaya, Petru-Daniel Tudosiu, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

We propose to use DDPMs to reconstruct an input that has been noised to a range of noise levels, and use the resulting multi-dimensional reconstruction error to classify out-of-distribution inputs.

Denoising Out-of-Distribution Detection

MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation

1 code implementation11 Nov 2022 Jiayu Huo, Liyun Chen, Yang Liu, Maxence Boels, Alejandro Granados, Sebastien Ourselin, Rachel Sparks

Accurate stroke lesion segmentation plays a pivotal role in stroke rehabilitation research, to provide lesion shape and size information which can be used for quantification of the extent of the stroke and to assess treatment efficacy.

Lesion Segmentation

PIPPI2021: An Approach to Automated Diagnosis and Texture Analysis of the Fetal Liver & Placenta in Fetal Growth Restriction

no code implementations1 Nov 2022 Aya Mutaz Zeidan, Paula Ramirez Gilliland, Ashay Patel, Zhanchong Ou, Dimitra Flouri, Nada Mufti, Kasia Maksym, Rosalind Aughwane, Sebastien Ourselin, Anna David, Andrew Melbourne

We explore the application of model fitting techniques, linear regression machine learning models, deep learning regression, and Haralick textured features from multi-contrast MRI for multi-fetal organ analysis of FGR.

regression Texture Classification

Brain Imaging Generation with Latent Diffusion Models

no code implementations15 Sep 2022 Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro F Da Costa, Virginia Fernandez, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

In this study, we explore using Latent Diffusion Models to generate synthetic images from high-resolution 3D brain images.

Morphology-preserving Autoregressive 3D Generative Modelling of the Brain

1 code implementation7 Sep 2022 Petru-Daniel Tudosiu, Walter Hugo Lopez Pinaya, Mark S. Graham, Pedro Borges, Virginia Fernandez, Dai Yang, Jeremy Appleyard, Guido Novati, Disha Mehra, Mike Vella, Parashkev Nachev, Sebastien Ourselin, Jorge Cardoso

Still, the ability to produce high-resolution 3D volumetric imaging data with correct anatomical morphology has been hampered by data scarcity and algorithmic and computational limitations.

Anatomy Anomaly Detection

Driving Points Prediction For Abdominal Probabilistic Registration

1 code implementation5 Aug 2022 Samuel Joutard, Reuben Dorent, Sebastien Ourselin, Tom Vercauteren, Marc Modat

Among the various registration methods proposed for this task, probabilistic displacement registration models estimate displacement distribution for a subset of points by comparing feature vectors of points from the two images.


Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder

no code implementations5 Aug 2022 Jiayu Huo, Vejay Vakharia, Chengyuan Wu, Ashwini Sharan, Andrew Ko, Sebastien Ourselin, Rachel Sparks

Concretely, our framework consists of two sequential networks: a mask synthesis network and a mask-guided lesion synthesis network.

Data Augmentation

Augmentation based unsupervised domain adaptation

no code implementations23 Feb 2022 Mauricio Orbes-Arteaga, Thomas Varsavsky, Lauge Sorensen, Mads Nielsen, Akshay Pai, Sebastien Ourselin, Marc Modat, M Jorge Cardoso

The insertion of deep learning in medical image analysis had lead to the development of state-of-the art strategies in several applications such a disease classification, as well as abnormality detection and segmentation.

Anomaly Detection Unsupervised Domain Adaptation

Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data

1 code implementation11 Feb 2022 Daniel Sobotka, Michael Ebner, Ernst Schwartz, Karl-Heinz Nenning, Athena Taymourtash, Tom Vercauteren, Sebastien Ourselin, Gregor Kasprian, Daniela Prayer, Georg Langs, Roxane Licandro

Here, we propose a novel framework, which estimates a high-resolution reference volume by using outlier-robust motion correction, and by utilizing Huber L2 regularization for intra-stack volumetric reconstruction of the motion-corrected fetal brain fMRI.

L2 Regularization Motion Estimation +2

Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models

no code implementations29 Nov 2021 Guilherme Pombo, Robert Gray, Jorge Cardoso, Sebastien Ourselin, Geraint Rees, John Ashburner, Parashkev Nachev

The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations.

Acquisition-invariant brain MRI segmentation with informative uncertainties

no code implementations7 Nov 2021 Pedro Borges, Richard Shaw, Thomas Varsavsky, Kerstin Klaser, David Thomas, Ivana Drobnjak, Sebastien Ourselin, M Jorge Cardoso

Combining multi-site data can strengthen and uncover trends, but is a task that is marred by the influence of site-specific covariates that can bias the data and therefore any downstream analyses.

MRI segmentation

The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties

no code implementations4 Nov 2021 Pedro Borges, Richard Shaw, Thomas Varsavsky, Kerstin Klaser, David Thomas, Ivana Drobnjak, Sebastien Ourselin, M Jorge Cardoso

Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter dependent biases.

Brain Segmentation

A Decoupled Uncertainty Model for MRI Segmentation Quality Estimation

no code implementations6 Sep 2021 Richard Shaw, Carole H. Sudre, Sebastien Ourselin, M. Jorge Cardoso, Hugh G. Pemberton

We aim to automate the process using a probabilistic network that estimates segmentation uncertainty through a heteroscedastic noise model, providing a measure of task-specific quality.

MRI segmentation

Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation

1 code implementation1 Jul 2021 Reuben Dorent, Samuel Joutard, Jonathan Shapey, Aaron Kujawa, Marc Modat, Sebastien Ourselin, Tom Vercauteren

We introduce $\textit{InExtremIS}$, a weakly supervised 3D approach to train a deep image segmentation network using particularly weak train-time annotations: only 6 extreme clicks at the boundary of the objects of interest.

Image Segmentation Semantic Segmentation +1

Estimating MRI Image Quality via Image Reconstruction Uncertainty

no code implementations21 Jun 2021 Richard Shaw, Carole H. Sudre, Sebastien Ourselin, M. Jorge Cardoso

Thus, we argue that quality control for visual assessment cannot be equated to quality control for algorithmic processing.

Image Quality Assessment Image Reconstruction

Automated triaging of head MRI examinations using convolutional neural networks

no code implementations15 Jun 2021 David A. Wood, Sina Kafiabadi, Ayisha Al Busaidi, Emily Guilhem, Antanas Montvila, Siddharth Agarwal, Jeremy Lynch, Matthew Townend, Gareth Barker, Sebastien Ourselin, James H. Cole, Thomas C. Booth

The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans around the world.

MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning

2 code implementations25 Apr 2021 Xiangde Luo, Guotai Wang, Tao Song, Jingyang Zhang, Michael Aertsen, Jan Deprest, Sebastien Ourselin, Tom Vercauteren, Shaoting Zhang

To solve these problems, we propose a novel deep learning-based interactive segmentation method that not only has high efficiency due to only requiring clicks as user inputs but also generalizes well to a range of previously unseen objects.

Image Segmentation Interactive Segmentation +2

Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021

no code implementations15 Apr 2021 Thomas Booth, Bernice Akpinar, Andrei Roman, Haris Shuaib, Aysha Luis, Alysha Chelliah, Ayisha Al Busaidi, Ayesha Mirchandani, Burcu Alparslan, Nina Mansoor, Keyoumars Ashkan, Sebastien Ourselin, Marc Modat

The small numbers of patient included in studies, the high risk of bias and concerns of applicability in the study designs (particularly in relation to the reference standard and patient selection due to confounding), and the low level of evidence, suggest that limited conclusions can be drawn from the data.

BIG-bench Machine Learning Specificity

Unsupervised Brain Anomaly Detection and Segmentation with Transformers

no code implementations23 Feb 2021 Walter Hugo Lopez Pinaya, Petru-Daniel Tudosiu, Robert Gray, Geraint Rees, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

Pathological brain appearances may be so heterogeneous as to be intelligible only as anomalies, defined by their deviation from normality rather than any specific pathological characteristic.

Unsupervised Anomaly Detection

Scale factor point spread function matching: Beyond aliasing in image resampling

no code implementations16 Jan 2021 M. Jorge Cardoso, Marc Modat, Tom Vercauteren, Sebastien Ourselin

Imaging devices exploit the Nyquist-Shannon sampling theorem to avoid both aliasing and redundant oversampling by design.

Active Annotation of Informative Overlapping Frames in Video Mosaicking Applications

1 code implementation30 Dec 2020 Loic Peter, Marcel Tella-Amo, Dzhoshkun Ismail Shakir, Jan Deprest, Sebastien Ourselin, Juan Eugenio Iglesias, Tom Vercauteren

In addition to the efficient construction of a mosaic, our framework provides, as a by-product, ground truth landmark correspondences which can be used for evaluation or learning purposes.

Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge

1 code implementation3 Nov 2020 Lucas Fidon, Sebastien Ourselin, Tom Vercauteren

We stuck to a generic and state-of-the-art 3D U-Net architecture and experimented with a non-standard per-sample loss function, the generalized Wasserstein Dice loss, a non-standard population loss function, corresponding to distributionally robust optimization, and a non-standard optimizer, Ranger.

Brain Tumor Segmentation Tumor Segmentation

Accessible Data Curation and Analytics for International-Scale Citizen Science Datasets

1 code implementation2 Nov 2020 Benjamin Murray, Eric Kerfoot, Mark S. Graham, Carole H. Sudre, Erika Molteni, Liane S. Canas, Michela Antonelli, Kerstin Klaser, Alessia Visconti, Andrew T. Chan, Paul W. Franks, Richard Davies, Jonathan Wolf, Tim Spector, Claire J. Steves, Marc Modat, Sebastien Ourselin

We present ExeTera, an open source data curation software designed to address scalability challenges and to enable reproducible research across an international research group for datasets such as the Covid Symptom Study dataset.

Hierarchical brain parcellation with uncertainty

no code implementations16 Sep 2020 Mark S. Graham, Carole H. Sudre, Thomas Varsavsky, Petru-Daniel Tudosiu, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

We introduce a hierarchically-aware brain parcellation method that works by predicting the decisions at each branch in the label tree.

Enhancing Fiber Orientation Distributions using convolutional Neural Networks

1 code implementation12 Aug 2020 Oeslle Lucena, Sjoerd B. Vos, Vejay Vakharia, John Duncan, Keyoumars Ashkan, Rachel Sparks, Sebastien Ourselin

We evaluate how well each CNN model can resolve local fiber orientation 1) when training and testing on datasets with the same dMRI acquisition protocol; 2) when testing on a dataset with a different dMRI acquisition protocol than used to train the CNN models; and 3) when testing on a dataset with a fewer number of gradient directions than used to train the CNN models.

Deep Placental Vessel Segmentation for Fetoscopic Mosaicking

1 code implementation8 Jul 2020 Sophia Bano, Francisco Vasconcelos, Luke M. Shepherd, Emmanuel Vander Poorten, Tom Vercauteren, Sebastien Ourselin, Anna L. David, Jan Deprest, Danail Stoyanov

We propose a solution utilising the U-Net architecture for performing placental vessel segmentation in fetoscopic videos.

Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices

1 code implementation2 Jul 2020 Guotai Wang, Michael Aertsen, Jan Deprest, Sebastien Ourselin, Tom Vercauteren, Shaoting Zhang

Experimental results show that: (1) our proposed CNN obtains uncertainty estimation in real time which correlates well with mis-segmentations, (2) the proposed interactive level set is effective and efficient for refinement, (3) UGIR obtains accurate refinement results with around 30% improvement of efficiency by using uncertainty to guide user interactions.

Brain Segmentation

The Future of Digital Health with Federated Learning

no code implementations18 Mar 2020 Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett Landman, Klaus Maier-Hein, Sebastien Ourselin, Micah Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso

Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems.

Federated Learning

Neuromorphologicaly-preserving Volumetric data encoding using VQ-VAE

no code implementations MIDL 2019 Petru-Daniel Tudosiu, Thomas Varsavsky, Richard Shaw, Mark Graham, Parashkev Nachev, Sebastien Ourselin, Carole H. Sudre, M. Jorge Cardoso

The increasing efficiency and compactness of deep learning architectures, together with hardware improvements, have enabled the complex and high-dimensional modelling of medical volumetric data at higher resolutions.

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

A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality

no code implementations MIDL 2019 Richard Shaw, Carole H. Sudre, Sebastien Ourselin, M. Jorge Cardoso

By augmenting the training data with different types of simulated k-space artefacts, we propose a novel cascading CNN architecture based on a student-teacher framework to decouple sources of uncertainty related to different k-space augmentations in an entirely self-supervised manner.

SGD with Hardness Weighted Sampling for Distributionally Robust Deep Learning

no code implementations25 Sep 2019 Lucas Fidon, Sebastien Ourselin, Tom Vercauteren

Similar to a hard example mining strategy in essence and in practice, the proposed algorithm is straightforward to implement and computationally as efficient as SGD-based optimizers used for deep learning.

Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning

no code implementations21 Aug 2019 Kerstin Kläser, Thomas Varsavsky, Pawel Markiewicz, Tom Vercauteren, David Atkinson, Kris Thielemans, Brian Hutton, M. Jorge Cardoso, Sebastien Ourselin

Quantitative results show that the network generates pCTs that seem less accurate when evaluating the Mean Absolute Error on the pCT (69. 68HU) compared to a baseline CNN (66. 25HU), but lead to significant improvement in the PET reconstruction - 115a. u.

Imitation Learning

Knowledge distillation for semi-supervised domain adaptation

no code implementations16 Aug 2019 Mauricio Orbes-Arteaga, Jorge Cardoso, Lauge Sørensen, Christian Igel, Sebastien Ourselin, Marc Modat, Mads Nielsen, Akshay Pai

As a result, their performance is significantly lower on data from unseen sources compared to the performance on data from the same source as the training data.

Domain Adaptation Knowledge Distillation +1

As easy as 1, 2... 4? Uncertainty in counting tasks for medical imaging

no code implementations25 Jul 2019 Zach Eaton-Rosen, Thomas Varsavsky, Sebastien Ourselin, M. Jorge Cardoso

Counting is a fundamental task in biomedical imaging and count is an important biomarker in a number of conditions.

Deep Sequential Mosaicking of Fetoscopic Videos

1 code implementation15 Jul 2019 Sophia Bano, Francisco Vasconcelos, Marcel Tella Amo, George Dwyer, Caspar Gruijthuijsen, Jan Deprest, Sebastien Ourselin, Emmanuel Vander Poorten, Tom Vercauteren, Danail Stoyanov

Mosaicking can align multiple overlapping images to generate an image with increased FoV, however, existing techniques apply poorly to fetoscopy due to the low visual quality, texture paucity, and hence fail in longer sequences due to the drift accumulated over time.

Data Augmentation

Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets

no code implementations7 Jul 2019 Reuben Dorent, Wenqi Li, Jinendra Ekanayake, Sebastien Ourselin, Tom Vercauteren

Developing a DNN for such joint task is currently hampered by the fact that annotated datasets typically address only one specific task and rely on a task-specific hetero-modal imaging protocol.

Lesion Segmentation

Permutohedral Attention Module for Efficient Non-Local Neural Networks

1 code implementation1 Jul 2019 Samuel Joutard, Reuben Dorent, Amanda Isaac, Sebastien Ourselin, Tom Vercauteren, Marc Modat

Medical image processing tasks such as segmentation often require capturing non-local information.

Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss

no code implementations10 Jun 2019 Guotai Wang, Jonathan Shapey, Wenqi Li, Reuben Dorent, Alex Demitriadis, Sotirios Bisdas, Ian Paddick, Robert Bradford, Sebastien Ourselin, Tom Vercauteren

Automatic segmentation of vestibular schwannoma (VS) tumors from magnetic resonance imaging (MRI) would facilitate efficient and accurate volume measurement to guide patient management and improve clinical workflow.

Management Tumor Segmentation

Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling

no code implementations17 Mar 2019 Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Lauge Sørensen

The proposed LSTM algorithm is applied to model the progression of Alzheimer's disease (AD) using six volumetric magnetic resonance imaging (MRI) biomarkers, i. e., volumes of ventricles, hippocampus, whole brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is compared to standard LSTM networks with data imputation and a parametric, regression-based DPM method.

Hippocampus Imputation

Large-scale mammography CAD with Deformable Conv-Nets

no code implementations19 Feb 2019 Stephen Morrell, Zbigniew Wojna, Can Son Khoo, Sebastien Ourselin, Juan Eugenio Iglesias

State-of-the-art deep learning methods for image processing are evolving into increasingly complex meta-architectures with a growing number of modules.

Grey matter sublayer thickness estimation in themouse cerebellum

1 code implementation8 Jan 2019 Da Ma, Manuel J. Cardoso, Maria A. Zuluaga, Marc Modat, Nick. Powell, Frances Wiseman, Victor Tybulewicz, Elizabeth Fisher, Mark. F. Lythgoe, Sebastien Ourselin

In this work, we introduce a framework to extract the Purkinje layer within the grey matter, enabling the estimation of the thickness of the cerebellar grey matter, the granular layer and molecular layer from gadolinium-enhanced ex vivo mouse brain MRI.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

no code implementations18 Oct 2018 Guotai Wang, Wenqi Li, Sebastien Ourselin, Tom Vercauteren

Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors.

Brain Tumor Segmentation Data Augmentation +2

PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation

no code implementations3 Oct 2018 Mauricio Orbes Arteaga, Lauge Sørensen, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin, Stefan Sommer, Mads Nielsen, Christian Igel, Akshay Pai

For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input.

Image Segmentation Medical Image Segmentation +1

Elastic Registration of Geodesic Vascular Graphs

no code implementations14 Sep 2018 Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jager, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso

Vascular graphs can embed a number of high-level features, from morphological parameters, to functional biomarkers, and represent an invaluable tool for longitudinal and cross-sectional clinical inference.

Graph Matching

Deep Boosted Regression for MR to CT Synthesis

no code implementations22 Aug 2018 Kerstin Kläser, Pawel Markiewicz, Marta Ranzini, Wenqi Li, Marc Modat, Brian F. Hutton, David Atkinson, Kris Thielemans, M. Jorge Cardoso, Sebastien Ourselin

Attenuation correction is an essential requirement of positron emission tomography (PET) image reconstruction to allow for accurate quantification.

Computed Tomography (CT) Image Reconstruction +1

Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia

no code implementations2 May 2018 Luis C. Garcia-Peraza-Herrera, Martin Everson, Wenqi Li, Inmanol Luengo, Lorenz Berger, Omer Ahmad, Laurence Lovat, Hsiu-Po Wang, Wen-Lun Wang, Rehan Haidry, Danail Stoyanov, Tom Vercauteren, Sebastien Ourselin

We present a new approach to visualise attention that aims to give some insights on those areas of the oesophageal tissue that lead a network to conclude that the images belong to a particular class and compare them with those visual features employed by clinicians to produce a clinical diagnosis.

General Classification

A Log-Euclidean and Total Variation based Variational Framework for Computational Sonography

no code implementations6 Feb 2018 Jyotirmoy Banerjee, Premal A. Patel, Fred Ushakov, Donald Peebles, Jan Deprest, Sebastien Ourselin, David Hawkes, Tom Vercauteren

We propose a spatial compounding technique and variational framework to improve 3D ultrasound image quality by compositing multiple ultrasound volumes acquired from different probe orientations.

Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections

no code implementations16 Jan 2018 Juan Eugenio Iglesias, Marc Modat, Loic Peter, Allison Stevens, Roberto Annunziata, Tom Vercauteren, Ed Lein, Bruce Fischl, Sebastien Ourselin

Here, we overcome this limitation with a probabilistic method that simultaneously solves for registration and synthesis directly on the target images, without any training data.

Bayesian Inference

Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning

no code implementations11 Oct 2017 Guotai Wang, Wenqi Li, Maria A. Zuluaga, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L. David, Jan Deprest, Sebastien Ourselin, Tom Vercauteren

Experimental results show that 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs; 2) image-specific fine-tuning with the proposed weighted loss function significantly improves segmentation accuracy; and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods.

Image Segmentation Interactive Segmentation +2

Refractive Structure-From-Motion Through a Flat Refractive Interface

no code implementations ICCV 2017 Francois Chadebecq, Francisco Vasconcelos, George Dwyer, Rene Lacher, Sebastien Ourselin, Tom Vercauteren, Danail Stoyanov

By explicitly considering a refractive interface, we develop a succinct derivation of the refractive fundamental matrix in the form of the generalised epipolar constraint for an axial camera.

Pose Estimation

An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

no code implementations8 Sep 2017 Lorenz Berger, Eoin Hyde, M. Jorge Cardoso, Sebastien Ourselin

Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation.

Anatomy Object Recognition +1

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

7 code implementations1 Sep 2017 Guotai Wang, Wenqi Li, Sebastien Ourselin, Tom Vercauteren

A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing tumor core.

Brain Tumor Segmentation Tumor Segmentation

Model based learning for accelerated, limited-view 3D photoacoustic tomography

no code implementations31 Aug 2017 Andreas Hauptmann, Felix Lucka, Marta Betcke, Nam Huynh, Jonas Adler, Ben Cox, Paul Beard, Sebastien Ourselin, Simon Arridge

Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up.

Tomographic Reconstructions

Part-to-whole Registration of Histology and MRI using Shape Elements

no code implementations27 Aug 2017 Jonas Pichat, Juan Eugenio Iglesias, Sotiris Nousias, Tarek Yousry, Sebastien Ourselin, Marc Modat

We propose here a novel automatic approach to the joint problem of multimodal registration between histology and MRI, when only a fraction of tissue is available from histology.

Image Registration

On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

4 code implementations6 Jul 2017 Wenqi Li, Guotai Wang, Lucas Fidon, Sebastien Ourselin, M. Jorge Cardoso, Tom Vercauteren

To illustrate its efficiency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images.

3D Medical Imaging Segmentation Image Segmentation +3

Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks

1 code implementation3 Jul 2017 Lucas Fidon, Wenqi Li, Luis C. Garcia-Peraza-Herrera, Jinendra Ekanayake, Neil Kitchen, Sebastien Ourselin, Tom Vercauteren

3) We show that the joint use of holistic CNNs and generalised Wasserstein Dice scores achieves segmentations that are more semantically meaningful for brain tumour segmentation.

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

1 code implementation3 Jul 2017 Guotai Wang, Maria A. Zuluaga, Wenqi Li, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L. David, Jan Deprest, Sebastien Ourselin, Tom Vercauteren

We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy.

Brain Tumor Segmentation Image Segmentation +3

ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools

no code implementations25 Jun 2017 Luis C. Garcia-Peraza-Herrera, Wenqi Li, Lucas Fidon, Caspar Gruijthuijsen, Alain Devreker, George Attilakos, Jan Deprest, Emmanuel Vander Poorten, Danail Stoyanov, Tom Vercauteren, Sebastien Ourselin

We propose the use of parametric rectified linear units for semantic labeling in these small architectures to increase the regularization ability of the design and maintain the segmentation accuracy without overfitting the training sets.

Fast PET reconstruction using Multi-scale Fully Convolutional Neural Networks

no code implementations24 Apr 2017 Jieqing Jiao, Sebastien Ourselin

Reconstruction of PET images is an ill-posed inverse problem and often requires iterative algorithms to achieve good image quality for reliable clinical use in practice, at huge computational costs.

Image Reconstruction

Similarity Registration Problems for 2D/3D Ultrasound Calibration

no code implementations31 Jul 2016 Francisco Vasconcelos, Donald Peebles, Sebastien Ourselin, Danail Stoyanov

The needle is tracked as a 3D line, and is scanned by the ultrasound as either a 3D line (3D US) or as a 2D point (2D US).

Pose Tracking

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