57 citations · 107 across the 13 of their papers we have counts for
15 papers
Effect of User-Prompted Priors on Semi-Automated Cancer Lesion Segmentation in Whole-Body Computed Tomography
Isac Stark, Johan Öfverstedt, Elin Lundström +3
In clinical oncology studies, metastatic cancer is commonly evaluated using "Response Evaluation Criteria in Solid Tumors" (RECIST), in which the diameter of up to five lesions is…
Time-Conditioned and Multi-Time Survival Prediction from 2D PET/CT Projections in Lung Cancer
Ashish Chauhan, Sambit Tarai, Elin Lundström +3
Accurate prediction of overall survival (OS) from positron emission tomography/computed tomography (PET/CT) can support personalized treatment and follow-up strategies in oncology.…
Time-driven Survival Analysis from FDG-PET/CT in Non-Small Cell Lung Cancer
Sambit Tarai, Ashish Chauhan, Elin Lundström +5
Purpose: Automated medical image-based prediction of clinical outcomes, such as overall survival (OS), has great potential in improving patient prognostics and personalized treatme…
A method for tissue-mask supported whole-body image registration in the UK Biobank
Yasemin Utkueri, Elin Lundström, Håkan Ahlström +2
The UK Biobank is a large-scale study collecting imaging and non-imaging health data. Robust and accurate inter-subject image registration of these whole-body MR images would enabl…
Towards prediction of morphological heart age from computed tomography angiography
Johan Öfverstedt, Elin Lundström, Håkan Ahlström +1
Age prediction from medical images or other health-related non-imaging data is an important approach to data-driven aging research, providing knowledge of how much information a sp…
A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms
Johan Öfverstedt, Elin Lundström, Göran Bergström +2
The study of associations between an individual's age and imaging and non-imaging data is an active research area that attempts to aid understanding of the effects and patterns of…
MIMIR: Deep Regression for Automated Analysis of UK Biobank Body MRI
Taro Langner, Andrés Martínez Mora, Robin Strand +2
UK Biobank (UKB) conducts large-scale examinations of more than half a million volunteers, collecting health-related information on genetics, lifestyle, blood biochemistry, and mor…
Deep regression for uncertainty-aware and interpretable analysis of large-scale body MRI
Taro Langner, Robin Strand, Håkan Ahlström +1
Large-scale medical studies such as the UK Biobank examine thousands of volunteer participants with medical imaging techniques. Combined with the vast amount of collected metadata,…
Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI
Taro Langner, Fredrik K. Gustafsson, Benny Avelin +3
Along with rich health-related metadata, medical images have been acquired for over 40,000 male and female UK Biobank participants, aged 44-82, since 2014. Phenotypes derived from…
Large-scale inference of liver fat with neural networks on UK Biobank body MRI
Taro Langner, Robin Strand, Håkan Ahlström +1
The UK Biobank Imaging Study has acquired medical scans of more than 40,000 volunteer participants. The resulting wealth of anatomical information has been made available for resea…
Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants
Taro Langner, Andreas Östling, Lukas Maldonis +8
The UK Biobank is collecting extensive data on health-related characteristics of over half a million volunteers. The biological samples of blood and urine can provide valuable insi…
Large-scale biometry with interpretable neural network regression on UK Biobank body MRI
Taro Langner, Robin Strand, Håkan Ahlström +1
In a large-scale medical examination, the UK Biobank study has successfully imaged more than 32,000 volunteer participants with magnetic resonance imaging (MRI). Each scan is linke…
Separation of water and fat signal in whole-body gradient echo scans using convolutional neural networks
Jonathan Andersson, Håkan Ahlström, Joel Kullberg
Purpose: To perform and evaluate water-fat signal separation of whole-body gradient echo scans using convolutional neural networks. Methods: Whole-body gradient echo scans of 240 s…
Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume Images
Simon Ekström, Filip Malmberg, Håkan Ahlström +2
Objective: Deformable image registration is a fundamental problem in medical image analysis, with applications such as longitudinal studies, population modeling, and atlas based im…
Fully Convolutional Networks for Automated Segmentation of Abdominal Adipose Tissue Depots in Multicenter Water-Fat MRI
Taro Langner, Anders Hedström, Katharina Mörwald +5
Purpose: An approach for the automated segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) in multicenter water-fat MRI scans of the abdomen was inv…