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Woisetschläger, M., Blomma, J., Dahlström, N., Bivik Stadler, C. & Forsberg, D. (2019). Liver data from the Visual Sweden project DROID: Analytic Imaging Diagnostics Arena (AIDA). Linköping: Analytic Imaging Diagnostics Arena
Open this publication in new window or tab >>Liver data from the Visual Sweden project DROID: Analytic Imaging Diagnostics Arena (AIDA)
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2019 (English)Data set
Place, publisher, year
Linköping: Analytic Imaging Diagnostics Arena, 2019
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-154903 (URN)10.23698/aida/drli (DOI)
Note

Restricted access, Please contact Mischa.Woisetschlager@regionostergotland.se, claes.lundstrom@liu.se or joel.hedlund@liu.se to request access.

Available from: 2019-03-04 Created: 2019-03-04 Last updated: 2019-03-13Bibliographically approved
Woisetschläger, M., Landgren, F., Bivik Stadler, C. & Forsberg, D. (2019). Skeletal data from the Visual Sweden project DROID: Analytic Imaging Diagnostics Arena (AIDA). Linköping: Analytic Imaging Diagnostics Arena
Open this publication in new window or tab >>Skeletal data from the Visual Sweden project DROID: Analytic Imaging Diagnostics Arena (AIDA)
2019 (English)Data set
Place, publisher, year
Linköping: Analytic Imaging Diagnostics Arena, 2019
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-154900 (URN)10.23698/aida/drske (DOI)
Note

Restricted access, please contact Mischa.Woisetschlager@regionostergotland.se, claes.lundstrom@liu.se or joel.hedlund@liu.se to request access.

Available from: 2019-03-04 Created: 2019-03-04 Last updated: 2019-03-13Bibliographically approved
Bustamante, M., Gupta, V., Forsberg, D., Carlhäll, C., Engvall, J. & Ebbers, T. (2018). Automated multi-atlas segmentation of cardiac 4D flow MRI. Medical Image Analysis, 49, 128-140
Open this publication in new window or tab >>Automated multi-atlas segmentation of cardiac 4D flow MRI
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2018 (English)In: Medical Image Analysis, ISSN 1361-8415, E-ISSN 1361-8423, Vol. 49, p. 128-140Article in journal (Refereed) Published
Abstract [en]

Four-dimensional (4D) flow magnetic resonance imaging (4D Flow MRI) enables acquisition of time-resolved three-directional velocity data in the entire heart and all major thoracic vessels. The segmentation of these tissues is typically performed using semi-automatic methods. Some of which primarily rely on the velocity data and result in a segmentation of the vessels only during the systolic phases. Other methods, mostly applied on the heart, rely on separately acquired balanced Steady State Free Precession (b-SSFP) MR images, after which the segmentations are superimposed on the 4D Flow MRI. While b-SSFP images typically cover the whole cardiac cycle and have good contrast, they suffer from a number of problems, such as large slice thickness, limited coverage of the cardiac anatomy, and being prone to displacement errors caused by respiratory motion. To address these limitations we propose a multi-atlas segmentation method, which relies only on 4D Flow MRI data, to automatically generate four-dimensional segmentations that include the entire thoracic cardiovascular system present in these datasets. The approach was evaluated on 4D Flow MR datasets from a cohort of 27 healthy volunteers and 83 patients with mildly impaired systolic left-ventricular function. Comparison of manual and automatic segmentations of the cardiac chambers at end-systolic and end-diastolic timeframes showed agreements comparable to those previously reported for automatic segmentation methods of b-SSFP MR images. Furthermore, automatic segmentation of the entire thoracic cardiovascular system improves visualization of 4D Flow MRI and facilitates computation of hemodynamic parameters.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
4D Flow MRI, Cardiac segmentation, Multi-atlas segmentation, Heart, Magnetic resonance imaging, Automatic segmentations, Directional velocities, Hemodynamic parameters, Left ventricular function, Segmentation methods, Semiautomatic methods, Steady state free precessions, Image segmentation, adult, anatomy, article, cohort analysis, controlled study, error, female, heart cycle, heart left ventricle function, human, human tissue, major clinical study, male, motion, nuclear magnetic resonance imaging, steady state, thickness, volunteer
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-150788 (URN)10.1016/j.media.2018.08.003 (DOI)000446286600011 ()30144652 (PubMedID)2-s2.0-85051830661 (Scopus ID)
Note

Funding details: 310612; Funding details: FP7, Seventh Framework Programme; Funding details: 621-2014-6191, VR, Vetenskapsrådet; Funding details: 223615; Funding details: 20140398; Funding text: This work was partially funded by the FP7-funded project DOPPLER-CIP [grant number 223615]; the European Union’s Seventh Framework Programme ( FP7/2007-2013 ) [grant number 310612 ]; the Swedish Research Council [grant number 621-2014-6191 ]; and the Swedish Heart and Lung Foundation [grant number 20140398 ]. 

Available from: 2018-08-31 Created: 2018-08-31 Last updated: 2018-10-17Bibliographically approved
Vavruch, L., Forsberg, D., Dahlström, N. & Tropp, H. (2018). Vertebral Axial Asymmetry in Adolescent Idiopathic Scoliosis.. Spine Deformity, 6(2), 112-120.e1
Open this publication in new window or tab >>Vertebral Axial Asymmetry in Adolescent Idiopathic Scoliosis.
2018 (English)In: Spine Deformity, ISSN 2212-134X, Vol. 6, no 2, p. 112-120.e1Article in journal (Refereed) Published
Abstract [en]

Study Design

Retrospective study.

Objectives

To investigate parameters of axial vertebral deformation in patients with scoliosis compared to a control group, and to determine whether these parameters correlated with the severity of spine curvature, measured as the Cobb angle.

Summary of Background Data

Adolescent idiopathic scoliosis (AIS) is the most common type of spinal deformity. Many studies have investigated vertebral deformation, in terms of wedging and pedicle deformations, but few studies have investigated actual structural changes within vertebrae.

Methods

This study included 20 patients with AIS (Lenke 1–3, mean age: 15.6 years, range: 11–20). We compared preoperative low-dose computed tomography(CT) examinations of patients with AIS to those of a control group matched for age and sex. The control individuals had no spinal deformity, but they were admitted to the emergency department for trauma CTs. We measured the Cobb angles and the axial vertebral rotation (AVR), axial vertebral bodyasymmetry (AVBA), and frontal vertebral body rotation (FVBR) for the superior end, inferior end, and apical vertebrae, with in-house–developed software. Correlations between entities were investigated with the Pearson correlation test.

Results

The average Cobb angles were 49.3° and 1.3° for the scoliotic and control groups, respectively. The patient and control groups showed significant differences in the AVRs of all three vertebra levels (p < .01), the AVBAs of the superior end and apical vertebrae (p < .008), and the FVBR of the apical vertebra (p = .011). Correlations were only found between the AVBA and FVBR in the superior end vertebra (r = 0.728, p < .001) and in the apical vertebra (r = 0.713, p < .001).

Conclusions

Compared with controls, patients with scoliosis showed clear morphologic differences in the midaxial plane vertebrae. Differences in AVR, AVBA, and FVBR were most pronounced at the apical vertebra. The FVBR provided valuable additional information about the internal rotation and deformation of vertebrae.

Level of Evidence

Level III.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Scoliosis; Morphology; Three-dimensional; Vertebral rotation; Low-dose CT
National Category
Orthopaedics
Identifiers
urn:nbn:se:liu:diva-145864 (URN)10.1016/j.jspd.2017.09.001 (DOI)29413732 (PubMedID)2-s2.0-85032338953 (Scopus ID)
Available from: 2018-03-20 Created: 2018-03-20 Last updated: 2019-05-01Bibliographically approved
Yao, J., Burns, J. E., Forsberg, D., Seitel, A., Rasoulian, A., Abolmaesumi, P., . . . Li, S. (2016). A multi-center milestone study of clinical vertebral CT segmentation. Computerized Medical Imaging and Graphics, 49, 16-28
Open this publication in new window or tab >>A multi-center milestone study of clinical vertebral CT segmentation
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2016 (English)In: Computerized Medical Imaging and Graphics, ISSN 0895-6111, E-ISSN 1879-0771, Vol. 49, p. 16-28Article in journal (Refereed) Published
Abstract [en]

A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention. (C) 2016 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2016
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-127747 (URN)10.1016/j.compmedimag.2015.12.006 (DOI)000374071500002 ()26878138 (PubMedID)
Available from: 2016-05-12 Created: 2016-05-12 Last updated: 2017-11-30
Sjölund, J., Forsberg, D., Andersson, M. & Knutsson, H. (2015). Generating patient specific pseudo-CT of the head from MR using atlas-based regression. Physics in Medicine and Biology, 60(2), 825-839
Open this publication in new window or tab >>Generating patient specific pseudo-CT of the head from MR using atlas-based regression
2015 (English)In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 60, no 2, p. 825-839Article in journal (Refereed) Published
Abstract [en]

Radiotherapy planning and attenuation correction of PET images require simulation of radiation transport. The necessary physical properties are typically derived from computed tomography (CT) images, but in some cases, including stereotactic neurosurgery and combined PET/MR imaging, only magnetic resonance (MR) images are available. With these applications in mind, we describe how a realistic, patient-specific, pseudo-CT of the head can be derived from anatomical MR images. We refer to the method as atlas-based regression, because of its similarity to atlas-based segmentation. Given a target MR and an atlas database comprising MR and CT pairs, atlas-based regression works by registering each atlas MR to the target MR, applying the resulting displacement fields to the corresponding atlas CTs and, finally, fusing the deformed atlas CTs into a single pseudo-CT. We use a deformable registration algorithm known as the Morphon and augment it with a certainty mask that allows a tailoring of the influence certain regions are allowed to have on the registration. Moreover, we propose a novel method of fusion, wherein the collection of deformed CTs is iteratively registered to their joint mean and find that the resulting mean CT becomes more similar to the target CT. However, the voxelwise median provided even better results; at least as good as earlier work that required special MR imaging techniques. This makes atlas-based regression a good candidate for clinical use.

National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-113297 (URN)10.1088/0031-9155/60/2/825 (DOI)000347675100023 ()25565133 (PubMedID)
Available from: 2015-01-15 Created: 2015-01-15 Last updated: 2018-01-16Bibliographically approved
Forsberg, D., Lundström, C. & Knutsson, H. (2014). Eigenspine: Computing the Correlation between Measures Describing Vertebral Pose for Patients with Adolescent Idiopathic Scoliosis. Computerized Medical Imaging and Graphics, 38(7), 549-557
Open this publication in new window or tab >>Eigenspine: Computing the Correlation between Measures Describing Vertebral Pose for Patients with Adolescent Idiopathic Scoliosis
2014 (English)In: Computerized Medical Imaging and Graphics, ISSN 0895-6111, E-ISSN 1879-0771, Vol. 38, no 7, p. 549-557Article in journal (Refereed) Published
Abstract [en]

This paper describes the concept of eigenspine, a concept applicable for determining the correlation between pair-wise combinationsof measures useful for describing the three-dimensional spinal deformities associated with adolescent idiopathic scoliosis. Theproposed data analysis scheme is based upon the use of principal component analysis (PCA) and canonical correlation analysis(CCA). PCA is employed to reduce the dimensionality of the data space, thereby providing a regularization of the measurements,and CCA is employed to determine the linear dependence between pair-wise combinations of different measures. The usefulness ofthe eigenspine concept is demonstrated by analyzing the position and the rotation of all lumbar and thoracic vertebrae as obtainedfrom 46 patients suffering from adolescent idiopathic scoliosis. The analysis showed that the strongest linear relationship is foundbetween the lateral displacement and the coronal rotation of the vertebrae, and that a somewhat weaker but still strong correlationis found between the coronal rotation and the axial rotation of the vertebrae. These results are well in-line with the generalunderstanding of idiopathic scoliosis. Noteworthy though is that the correlation between the anterior-posterior displacement and thesagittal rotation was not as strong as expected and that the obtained results further indicate the need for including the axial vertebralrotation as a measure when characterizing different types of idiopathic scoliosis. Apart from analyzing pair-wise correlationsbetween different measures, the method is believed to be suitable for finding a maximally descriptive low-dimensional combinationof measures describing spinal deformities in idiopathic scoliosis.

Place, publisher, year, edition, pages
Elsevier, 2014
Keywords
Spine imaging, Principal component analysis, Canonical correlation analysis, Pose estimation, idiopathic scoliosis
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-108976 (URN)10.1016/j.compmedimag.2014.06.011 (DOI)000343350100001 ()
Funder
VINNOVA, 2012-01213
Available from: 2014-07-16 Created: 2014-07-16 Last updated: 2017-12-05
Forsberg, D., Lundström, C., Andersson, M. & Knutsson, H. (2014). Eigenspine: Eigenvector Analysis of Spinal Deformities in Idiopathic Scoliosis. In: Jianhua Yao, Tobias Kinder and Shuo Li Shuo (Ed.), Jianhua Yao,Tobias Klinder, Shuo Li (Ed.), Computational Methods and Clinical Applications for Spine Imaging: Proceedings of the Workshop held at the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, September 22-26, 2013, Nagoya, Japan. Paper presented at 16th International Conference on Medical Image Computing and Computer Assisted Intervention, September 22-26, 2013, Nagoya, Japan (pp. 123-134). Springer, 17
Open this publication in new window or tab >>Eigenspine: Eigenvector Analysis of Spinal Deformities in Idiopathic Scoliosis
2014 (English)In: Computational Methods and Clinical Applications for Spine Imaging: Proceedings of the Workshop held at the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, September 22-26, 2013, Nagoya, Japan / [ed] Jianhua Yao,Tobias Klinder, Shuo Li, Springer, 2014, Vol. 17, p. 123-134Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose the concept of eigenspine, a data analysis schemeuseful for quantifying the linear correlation between different measures relevant fordescribing spinal deformities associated with spinal diseases, such as idiopathic scoliosis.The proposed concept builds upon the use of principal component analysis(PCA) and canonical correlation analysis (CCA), where PCA is used to reduce thenumber of dimensions in the measurement space, thereby providing a regularizationof the measurements, and where CCA is used to determine the linear dependence betweenpair-wise combinations of the different measures. To demonstrate the usefulnessof the eigenspine concept, the measures describing position and rotation of thelumbar and the thoracic vertebrae of 22 patients suffering from idiopathic scoliosiswere analyzed. The analysis showed that the strongest linear relationship is foundbetween the anterior-posterior displacement and the sagittal rotation of the vertebrae,and that a somewhat weaker but still strong correlation is found between thelateral displacement and the frontal rotation of the vertebrae. These results are wellin-line with the general understanding of idiopathic scoliosis. Noteworthy though isthat the obtained results from the analysis further proposes axial vertebral rotationas a differentiating measure when characterizing idiopathic scoliosis. Apart fromanalyzing pair-wise linear correlations between different measures, the method isbelieved to be suitable for finding a maximally descriptive low-dimensional combinationof measures describing spinal deformities in idiopathic scoliosis.

Place, publisher, year, edition, pages
Springer, 2014
Series
Lecture Notes in Computational Vision and Biomechanics, ISSN 2212-9391 ; 17
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-108975 (URN)10.1007/978-3-319-07269-2_11 (DOI)978-3-319-07268-5 (ISBN)978-3-319-07269-2 (ISBN)
Conference
16th International Conference on Medical Image Computing and Computer Assisted Intervention, September 22-26, 2013, Nagoya, Japan
Funder
Swedish Research Council, 2007-4786Swedish Foundation for Strategic Research , SM10-0022
Available from: 2014-07-16 Created: 2014-07-16 Last updated: 2015-06-04Bibliographically approved
Forsberg, D. & Monsef, N. (2014). Evaluating Cell Nuclei Segmentation for Use on Whole-Slide Images in Lung Cytology. In: 2014 22nd International Conference on Pattern Recognition (ICPR): . Paper presented at 22nd International Conference on Pattern Recognition (ICPR, Stockholm, Sweden, 24-28 Aug. 2014 (pp. 3380-3385). IEEE Computer Society
Open this publication in new window or tab >>Evaluating Cell Nuclei Segmentation for Use on Whole-Slide Images in Lung Cytology
2014 (English)In: 2014 22nd International Conference on Pattern Recognition (ICPR), IEEE Computer Society, 2014, p. 3380-3385Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents results from an evaluation of three previously presented methods for segmentation of cell nuclei in lung cytology samples scanned by whole-slide scanners. Whole-slide images from seven cases of endobronchial ultrasound-guided transbronchial needle aspiration samples were used for extracting a number of regions of interest, in which approximately 2700 cell nuclei were manually segmented to form the ground truth. The segmented cells included benign bronchial epithelium, lymphocytes, granulocytes, histiocytes and malignant epithelial cells. The best results were obtained with a method based upon adaptive thresholding and an added step of clustering for distinguishing between cytoplasm and cell nuclei. This method achieved a mean DICE-score of 0.81 and a sensitivity and specificity of 0.88 and 0.81 respectively. In addition, this method was by far the fastest method, with a mean processing time of 7.8 s per image (2048 x 2048 pixels per image). By further improvements, such as lowering the false positive rate and using parallel computing hardware, this method has the potential to form the first building block in a system for computerized screening of whole-slide images in lung cytology.

Place, publisher, year, edition, pages
IEEE Computer Society, 2014
Series
International Conference on Pattern Recognition, ISSN 1051-4651
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-121334 (URN)10.1109/ICPR.2014.582 (DOI)000359818003086 ()978-1-4799-5208-3 (ISBN)
Conference
22nd International Conference on Pattern Recognition (ICPR, Stockholm, Sweden, 24-28 Aug. 2014
Available from: 2015-09-14 Created: 2015-09-14 Last updated: 2015-10-01Bibliographically approved
Forsberg, D., Lundström, C., Andersson, M. & Knutsson, H. (2014). Model-based registration for assessment of spinal deformities in idiopathic scoliosis. Physics in Medicine and Biology, 59(2), 311-326
Open this publication in new window or tab >>Model-based registration for assessment of spinal deformities in idiopathic scoliosis
2014 (English)In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 59, no 2, p. 311-326Article in journal (Refereed) Published
Abstract [en]

Detailed analysis of spinal deformity is important within orthopaedic healthcare, in particular for assessment of idiopathic scoliosis. This paper addresses this challenge by proposing an image analysis method, capable of providing a full three-dimensional spine characterization. The proposed method is based on the registration of a highly detailed spine model to image data from computed tomography. The registration process provides an accurate segmentation of each individual vertebra and the ability to derive various measures describing the spinal deformity. The derived measures are estimated from landmarks attached to the spine model and transferred to the patient data according to the registration result. Evaluation of the method provides an average point-to-surface error of 0.9 mm ± 0.9 (comparing segmentations), and an average target registration error of 2.3 mm ± 1.7 (comparing landmarks). Comparing automatic and manual measurements of axial vertebral rotation provides a mean absolute difference of 2.5° ± 1.8, which is on a par with other computerized methods for assessing axial vertebral rotation. A significant advantage of our method, compared to other computerized methods for rotational measurements, is that it does not rely on vertebral symmetry for computing the rotational measures. The proposed method is fully automatic and computationally efficient, only requiring three to four minutes to process an entire image volume covering vertebrae L5 to T1. Given the use of landmarks, the method can be readily adapted to estimate other measures describing a spinal deformity by changing the set of employed landmarks. In addition, the method has the potential to be utilized for accurate segmentations of the vertebrae in routine computed tomography examinations, given the relatively low point-to-surface error.

Place, publisher, year, edition, pages
Institute of Physics and Engineering in Medicine, 2014
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-91233 (URN)10.1088/0031-9155/59/2/311 (DOI)000332842000005 ()
Funder
Swedish Research Council, 2007-4786Swedish Foundation for Strategic Research , SM10-0022
Available from: 2013-04-17 Created: 2013-04-17 Last updated: 2017-12-06Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0908-9470

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