Supporting clinicians with image analysis
Imaging is a crucial element of all stages of medical care: from screening, diagnosis, prognosis and treatment, to the monitoring of therapy. With the increasing size and complexity of images, automated image analysis is an indispensible support for clinicians. Moreover, it opens novel ways to improve care.Read more
Meet some of our Researchers
Bart ter Haar Romenij
Our most recent peer reviewed publications
Pulmonary CT registration through supervised learning with convolutional neural networksIEEE Transactions on Medical Imaging (2019)
Comparing signal-to-noise ratio for prostate imaging at 7T and 3TJournal of Magnetic Resonance Imaging (2019)
Not-so-supervisedMedical Image Analysis (2019)
Detection of acini in histopathology slidesSPIE Medical Imaging 2019 (2019)
Approximation of a pipeline of unsupervised retina image analysis methods with a CNNSPIE Medical Imaging 2019 (2019)
Crowdsourcing for Medical Image Analysis
The amount of medical scans being collected at hospitals offers many opportunities for using machine learning to discover patterns of...
Histopathology Image Analysis
We develop automatic quantitative histopathology image analysis algorithms that will increase the reproducibility and accuracy of pathology...
The alignment of medical images is an ongoing topic of research. Early attempts date back as far as the early 1970s (e.g. Dowsett and Perry,...
Brain Image Analysis
Magnetic resonance imaging (MRI) is nowadays extensively used to study structure and function of the brain, e.g. to diagnose and monitor...
Cardiovascular Image Analysis
The main purpose of medical image analysis is the extraction of meaningful information to support disease diagnosis and therapy.
Retinal Image Analysis
The research program of the Retinal Image Analysis group is aimed at the development and exploitation of computer-aided diagnosis (CAD)...