Search Results for author: Steffen E. Petersen

Found 18 papers, 3 papers with code

Large-scale, multi-centre, multi-disease validation of an AI clinical tool for cine CMR analysis

no code implementations15 Jun 2022 Jorge Mariscal-Harana, Clint Asher, Vittoria Vergani, Maleeha Rizvi, Louise Keehn, Raymond J. Kim, Robert M. Judd, Steffen E. Petersen, Reza Razavi, Andrew King, Bram Ruijsink, Esther Puyol-Antón

CONCLUSIONS: We show that our proposed tool, which combines a state-of-the-art AI algorithm trained on a large-scale multi-domain CMR dataset with a post-analysis quality control, allows us to robustly deal with routine clinical data from multiple centres, vendors, and cardiac diseases.

Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation

no code implementations23 Jun 2021 Esther Puyol-Anton, Bram Ruijsink, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Reza Razavi, Andrew P. King

The subject of "fairness" in artificial intelligence (AI) refers to assessing AI algorithms for potential bias based on demographic characteristics such as race and gender, and the development of algorithms to address this bias.

Fairness Meta-Learning

A radiomics approach to analyze cardiac alterations in hypertension

no code implementations21 Jul 2020 Irem Cetin, Steffen E. Petersen, Sandy Napel, Oscar Camara, Miguel Angel Gonzalez Ballester, Karim Lekadir

Hypertension is a medical condition that is well-established as a risk factor for many major diseases.

Fully Automated Myocardial Strain Estimation from CMR Tagged Images using a Deep Learning Framework in the UK Biobank

no code implementations15 Apr 2020 Edward Ferdian, Avan Suinesiaputra, Kenneth Fung, Nay Aung, Elena Lukaschuk, Ahmet Barutcu, Edd Maclean, Jose Paiva, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alistair A. Young

The fully automatic framework consisted of 1) a convolutional neural network (CNN) for localization, and 2) a combination of a recurrent neural network (RNN) and a CNN to detect and track the myocardial landmarks through the image sequence for each slice.

Image Registration

A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI

no code implementations25 Sep 2019 Irem Cetin, Gerard Sanroma, Steffen E. Petersen, Sandy Napel, Oscar Camara, Miguel-Angel Gonzalez Ballester, Karim Lekadir

In this paper, we present a new approach to identify CVDs from cine-MRI by estimating large pools of radiomic features (statistical, shape and textural features) encoding relevant changes in anatomical and image characteristics due to CVDs.

Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image

no code implementations20 Aug 2019 Chen Qin, Wenjia Bai, Jo Schlemper, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Daniel Rueckert

Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data.

Image Reconstruction Motion Estimation

Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction

no code implementations5 Jul 2019 Wenjia Bai, Chen Chen, Giacomo Tarroni, Jinming Duan, Florian Guitton, Steffen E. Petersen, Yike Guo, Paul M. Matthews, Daniel Rueckert

In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks.

Image Segmentation Self-Supervised Learning +2

Improving the generalizability of convolutional neural network-based segmentation on CMR images

1 code implementation2 Jul 2019 Chen Chen, Wenjia Bai, Rhodri H. Davies, Anish N. Bhuva, Charlotte Manisty, James C. Moon, Nay Aung, Aaron M. Lee, Mihir M. Sanghvi, Kenneth Fung, Jose Miguel Paiva, Steffen E. Petersen, Elena Lukaschuk, Stefan K. Piechnik, Stefan Neubauer, Daniel Rueckert

We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy.

Image Segmentation Semantic Segmentation

Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank

no code implementations15 Feb 2019 Qiao Zheng, Hervé Delingette, Kenneth Fung, Steffen E. Petersen, Nicholas Ayache

First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion.

Dimensionality Reduction General Classification

High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort

no code implementations10 Jan 2019 Rahman Attar, Marco Pereanez, Ali Gooya, Xenia Alba, Le Zhang, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi

We present a fully automatic, high throughput image parsing workflow for the analysis of cardiac MR images, and test its performance on the UK Biobank (UKB) cardiac dataset.

Image Quality Assessment

Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN

no code implementations6 Nov 2018 Le Zhang, Ali Gooya, Marco Pereanez, Bo Dong, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi

Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and necessary for accurate measurement of cardiac volume and functional assessment.

Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences

1 code implementation11 Jun 2018 Chen Qin, Wenjia Bai, Jo Schlemper, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Daniel Rueckert

Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases.

Cardiac Segmentation Motion Estimation +1

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

1 code implementation25 Oct 2017 Wenjia Bai, Matthew Sinclair, Giacomo Tarroni, Ozan Oktay, Martin Rajchl, Ghislain Vaillant, Aaron M. Lee, Nay Aung, Elena Lukaschuk, Mihir M. Sanghvi, Filip Zemrak, Kenneth Fung, Jose Miguel Paiva, Valentina Carapella, Young Jin Kim, Hideaki Suzuki, Bernhard Kainz, Paul M. Matthews, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Ben Glocker, Daniel Rueckert

By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance on par with human experts in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images.

Clinical Text Prediction with Numerically Grounded Conditional Language Models

no code implementations WS 2016 Georgios P. Spithourakis, Steffen E. Petersen, Sebastian Riedel

In this paper, we investigate how grounded and conditional extensions to standard neural language models can bring improvements in the tasks of word prediction and completion.

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