57 citations · 107 across the 15 of their papers we have counts for
9 papers · 1 filter
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…
Leveraging point annotations in segmentation learning with boundary loss
Eva Breznik, Hoel Kervadec, Filip Malmberg +4
This paper investigates the combination of intensity-based distance maps with boundary loss for point-supervised semantic segmentation. By design the boundary loss imposes a strong…
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…