3.1k citations · 15.9k across the 136 of their papers we have counts for
17 papers · 1 filter
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation
Guotai Wang, Wenqi Li, Sebastien Ourselin +1
Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have…
PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation
Mauricio Orbes Arteaga, Lauge Sørensen, M. Jorge Cardoso +6
For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear sha…
Elastic Registration of Geodesic Vascular Graphs
Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jager +3
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-sect…
Deep Boosted Regression for MR to CT Synthesis
Kerstin Kläser, Pawel Markiewicz, Marta Ranzini +7
Attenuation correction is an essential requirement of positron emission tomography (PET) image reconstruction to allow for accurate quantification. However, attenuation correction…
Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
Mauricio Orbes-Arteaga, M. Jorge Cardoso, Lauge Sørensen +4
Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on whi…
Robust training of recurrent neural networks to handle missing data for disease progression modeling
Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai +4
Disease progression modeling (DPM) using longitudinal data is a challenging task in machine learning for healthcare that can provide clinicians with better tools for diagnosis and…
Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration
Yipeng Hu, Marc Modat, Eli Gibson +11
One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a…
Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation
Lorenz Berger, Eoin Hyde, Matt Gibb +4
Training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory. In this work, we address the task of perfor…
Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
Guotai Wang, Wenqi Li, Michael Aertsen +3
Despite the state-of-the-art performance for medical image segmentation, deep convolutional neural networks (CNNs) have rarely provided uncertainty estimations regarding their segm…
Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions
Zach Eaton-Rosen, Felix Bragman, Sotirios Bisdas +2
Automated medical image segmentation, specifically using deep learning, has shown outstanding performance in semantic segmentation tasks. However, these methods rarely quantify the…
Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning
Felix J. S. Bragman, Ryutaro Tanno, Zach Eaton-Rosen +6
Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources. It is ideal in the context of MR-only radiotherapy planning a…
VTrails: Inferring Vessels with Geodesic Connectivity Trees
Stefano Moriconi, Maria A. Zuluaga, H. Rolf Jäger +3
The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative…
Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery
Sebastian Bodenstedt, Max Allan, Anthony Agustinos +17
Intraoperative segmentation and tracking of minimally invasive instruments is a prerequisite for computer- and robotic-assisted surgery. Since additional hardware like tracking sys…
Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia
Luis C. Garcia-Peraza-Herrera, Martin Everson, Wenqi Li +10
In this work, we have concentrated our efforts on the interpretability of classification results coming from a fully convolutional neural network. Motivated by the classification o…
Retrieval and Registration of Long-Range Overlapping Frames for Scalable Mosaicking of In Vivo Fetoscopy
Loïc Peter, Marcel Tella-Amo, Dzhoshkun Ismail Shakir +5
Purpose: The standard clinical treatment of Twin-to-Twin Transfusion Syndrome consists in the photo-coagulation of undesired anastomoses located on the placenta which are responsib…
A Log-Euclidean and Total Variation based Variational Framework for Computational Sonography
Jyotirmoy Banerjee, Premal A. Patel, Fred Ushakov +5
We propose a spatial compounding technique and variational framework to improve 3D ultrasound image quality by compositing multiple ultrasound volumes acquired from different probe…
Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections
Juan Eugenio Iglesias, Marc Modat, Loic Peter +6
Nonlinear registration of 2D histological sections with corresponding slices of MRI data is a critical step of 3D histology reconstruction. This task is difficult due to the large…