Diagnostic Image Analysis Group

Nijmegen, Netherlands

Diagnostic Image Analysis Group

Nijmegen, Netherlands
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Venhuizen F.G.,Diagnostic Image Analysis Group | Van Ginneken B.,Diagnostic Image Analysis Group | Bloemen B.,Diagnostic Image Analysis Group | Van Grinsven M.J.J.P.,Diagnostic Image Analysis Group | And 4 more authors.
Progress in Biomedical Optics and Imaging - Proceedings of SPIE | Year: 2015

Age-related Macular Degeneration (AMD) is a common eye disorder with high prevalence in elderly people. The disease mainly affects the central part of the retina, and could ultimately lead to permanent vision loss. Optical Coherence Tomography (OCT) is becoming the standard imaging modality in diagnosis of AMD and the assessment of its progression. However, the evaluation of the obtained volumetric scan is time consuming, expensive and the signs of early AMD are easy to miss. In this paper we propose a classification method to automatically distinguish AMD patients from healthy subjects with high accuracy. The method is based on an unsupervised feature learning approach, and processes the complete image without the need for an accurate pre-segmentation of the retina. The method can be divided in two steps: an unsupervised clustering stage that extracts a set of small descriptive image patches from the training data, and a supervised training stage that uses these patches to create a patch occurrence histogram for every image on which a random forest classifier is trained. Experiments using 384 volume scans show that the proposed method is capable of identifying AMD patients with high accuracy, obtaining an area under the Receiver Operating Curve of 0:984. Our method allows for a quick and reliable assessment of the presence of AMD pathology in OCT volume scans without the need for accurate layer segmentation algorithms. © 2015 SPIE.


Koek M.,Erasmus University Rotterdam | Goncalves F.B.,Erasmus University Rotterdam | Poldermans D.,Erasmus University Rotterdam | Niessen W.,Erasmus University Rotterdam | And 3 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

In this study we propose a novel method for semi-automated 3D quantification of subcutaneous and visceral adipose tissue from CTA data. The method differentiates between subcutaneous and visceral adipose tissue by using gradient based deformable models using simplex meshes. The performance of the method is evaluated against a reference standard containing 27 manually annotated CTA scans made by expert observers. The quality of the reference standard is assessed by intra- and interobserver variability. The performance of the semi-automated method is evaluated against the reference standard by Pearson linear correlation and Bland and Altman analysis. © 2012 Springer-Verlag.


Kockelkorn T.T.J.P.,University Utrecht | De Jong P.A.,University Utrecht | Schaefer-Prokop C.M.,Meander Medical Center | Schaefer-Prokop C.M.,Diagnostic Image Analysis Group | And 8 more authors.
Physics in Medicine and Biology | Year: 2016

The textural patterns in the lung parenchyma, as visible on computed tomography (CT) scans, are essential to make a correct diagnosis in interstitial lung disease. We developed one automatic and two interactive protocols for classification of normal and seven types of abnormal lung textures. Lungs were segmented and subdivided into volumes of interest (VOIs) with homogeneous texture using a clustering approach. In the automatic protocol, VOIs were classified automatically by an extra-trees classifier that was trained using annotations of VOIs from other CT scans. In the interactive protocols, an observer iteratively trained an extra-trees classifier to distinguish the different textures, by correcting mistakes the classifier makes in a slice-by-slice manner. The difference between the two interactive methods was whether or not training data from previously annotated scans was used in classification of the first slice. The protocols were compared in terms of the percentages of VOIs that observers needed to relabel. Validation experiments were carried out using software that simulated observer behavior. In the automatic classification protocol, observers needed to relabel on average 58% of the VOIs. During interactive annotation without the use of previous training data, the average percentage of relabeled VOIs decreased from 64% for the first slice to 13% for the second half of the scan. Overall, 21% of the VOIs were relabeled. When previous training data was available, the average overall percentage of VOIs requiring relabeling was 20%, decreasing from 56% in the first slice to 13% in the second half of the scan. © 2016 Institute of Physics and Engineering in Medicine.


Arntz R.M.,Donders Institute for Brain | Van Den Broek S.M.A.,Donders Institute for Brain | Van Uden I.W.M.,Donders Institute for Brain | Ghafoorian M.,Diagnostic Image Analysis Group | And 8 more authors.
Neurology | Year: 2016

Objective: To study the long-term prevalence of small vessel disease after young stroke and to compare this to healthy controls. Methods: This prospective cohort study comprises 337 patients with an ischemic stroke or TIA, aged 18-50 years, without a history of TIA or stroke. In addition, 90 age- and sex-matched controls were included. At follow-up, lacunes, microbleeds, and white matter hyperintensity (WMH) volume were assessed using MRI. To investigate the relation between risk factors and small vessel disease, logistic and linear regression were used. Results: After mean follow-up of 9.9 (SD 8.1) years, 337 patients were included (227 with an ischemic stroke and 110 with a TIA). Mean age of patients was 49.8 years (SD 10.3) and 45.4% were men; for controls, mean age was 49.4 years (SD 11.9) and 45.6% were men. Compared with controls, patients more often had at least 1 lacune (24.0% vs 4.5%, p < 0.0001). In addition, they had a higher WMH volume (median 1.5 mL [interquartile range (IQR) 0.5-3.7] vs 0.4 mL [IQR 0.0-1.0], p < 0.001). Compared with controls, patients had the same volume WMHs on average 10-20 years earlier. In the patient group, age at stroke (β 0.03, 95% confidence interval [CI] 0.02-0.04) hypertension (β 0.22, 95% CI 0.04-0.39), and smoking (β 0.18, 95% CI 0.01-0.34) at baseline were associated with WMH volume. Conclusions: Patients with a young stroke have a higher burden of small vessel disease than controls adjusted for confounders. Cerebral aging seems accelerated by 10-20 years in these patients, which may suggest an increased vulnerability to vascular risk factors. © 2016 American Academy of Neurology.


Muenzing S.E.A.,University Utrecht | van Ginneken B.,University Utrecht | van Ginneken B.,Diagnostic Image Analysis Group | Murphy K.,University Utrecht | Pluim J.P.W.,University Utrecht
Medical Image Analysis | Year: 2012

A novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal multi-feature model, classifies local alignments into three quality categories: correct, poor or wrong alignment. We establish a reference registration error set as basis for training and testing of the method. It consists of image registrations obtained from different non-rigid registration algorithms and manually established point correspondences of automatically determined landmarks. We employ a set of different classifiers and evaluate the performance of the proposed image features based on the classification performance of corresponding single-feature classifiers. Feature selection is conducted to find an optimal subset of image features and the resulting multi-feature model is validated against the set of single-feature classifiers. We consider the setup generic, however, its application is demonstrated on 51 CT follow-up scan pairs of the lung. On this data, the proposed method performs with an overall classification accuracy of 90%. © 2012 Elsevier B.V.

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