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Strand, R., Malmberg, F., Johansson, L., Lind, L., Sundbom, M., Ahlström, H. & Kullberg, J. (2017). A concept for holistic whole body MRI data analysis, Imiomics. PLoS ONE, 12(2), Article ID e0169966.
Open this publication in new window or tab >>A concept for holistic whole body MRI data analysis, Imiomics
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2017 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 2, article id e0169966Article in journal (Refereed) Published
Abstract [en]

Purpose: To present and evaluate a whole-body image analysis concept, Imiomics (imaging omics) and an image registration method that enables Imiomics analyses by deforming all image data to a common coordinate system, so that the information in each voxel can be compared between persons or within a person over time and integrated with non-imaging data.

Methods: The presented image registration method utilizes relative elasticity constraints of different tissue obtained from whole-body water-fat MRI. The registration method is evaluated by inverse consistency and Dice coefficients and the Imiomics concept is evaluated by example analyses of importance for metabolic research using non-imaging parameters where we know what to expect. The example analyses include whole body imaging atlas creation, anomaly detection, and cross-sectional and longitudinal analysis.

Results: The image registration method evaluation on 128 subjects shows low inverse consistency errors and high Dice coefficients. Also, the statistical atlas with fat content intensity values shows low standard deviation values, indicating successful deformations to the common coordinate system. The example analyses show expected associations and correlations which agree with explicit measurements, and thereby illustrate the usefulness of the proposed Imiomics concept.

Conclusions: The registration method is well-suited for Imiomics analyses, which enable analyses of relationships to non-imaging data, e.g. clinical data, in new types of holistic targeted and untargeted big-data analysis.

National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-316830 (URN)10.1371/journal.pone.0169966 (DOI)000395934400002 ()28241015 (PubMedID)
Available from: 2017-02-27 Created: 2017-03-07 Last updated: 2017-11-29Bibliographically approved
Kullberg, J., Hedström, A., Brandberg, J., Strand, R., Johansson, L. E., Bergström, G. & Ahlström, H. (2017). Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies.. Scientific Reports, 7, Article ID 10425.
Open this publication in new window or tab >>Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies.
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2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 10425Article in journal (Refereed) Published
Abstract [en]

Computed Tomography (CT) allows detailed studies of body composition and its association with metabolic and cardiovascular disease. The purpose of this work was to develop and validate automated and manual image processing techniques for detailed and efficient analysis of body composition from CT data. The study comprised 107 subjects examined in the Swedish CArdioPulmonary BioImage Study (SCAPIS) using a 3-slice CT protocol covering liver, abdomen, and thighs. Algorithms were developed for automated assessment of liver attenuation, visceral (VAT) and subcutaneous (SAT) abdominal adipose tissue, thigh muscles, subcutaneous, subfascial (SFAT) and intermuscular adipose tissue. These were validated using manual reference measurements. SFAT was studied in selected subjects were the fascia lata could be visually identified (approx. 5%). In addition, precision of manual measurements of intra- (IPAT) and retroperitoneal adipose tissue (RPAT) and deep- and superficial SAT was evaluated using repeated measurements. Automated measurements correlated strongly to manual reference measurements. The SFAT depot showed the weakest correlation (r = 0.744). Automated VAT and SAT measurements were slightly, but significantly overestimated (≤4.6%, p ≤ 0.001). Manual segmentation of abdominal sub-depots showed high repeatability (CV ≤ 8.1%, r ≥ 0.930). We conclude that the low dose CT-scanning and automated analysis makes the setup suitable for large-scale studies.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-329273 (URN)10.1038/s41598-017-08925-8 (DOI)000409309300013 ()28874743 (PubMedID)
Funder
Swedish Research Council, 2012-2330Swedish Heart Lung FoundationVINNOVA
Available from: 2017-09-11 Created: 2017-09-11 Last updated: 2017-12-06Bibliographically approved
Lundström, E., Strand, R., Forslund, A., Bergsten, P., Weghuber, D., Ahlström, H. & Kullberg, J. (2017). Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images. Scientific Reports, 7, Article ID 3064.
Open this publication in new window or tab >>Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images
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2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 3064Article in journal (Refereed) Published
Abstract [en]

Human brown adipose tissue (BAT), with a major site in the cervical-supraclavicular depot, is a promising anti-obesity target. This work presents an automated method for segmenting cervical-supraclavicular adipose tissue for enabling time-efficient and objective measurements in large cohort research studies of BAT. Fat fraction (FF) and R2* maps were reconstructed from water-fat magnetic resonance imaging (MRI) of 25 subjects. A multi-atlas approach, based on atlases from nine subjects, was chosen as automated segmentation strategy. A semi-automated reference method was used to validate the automated method in the remaining subjects. Automated segmentations were obtained from a pipeline of preprocessing, affine registration, elastic registration and postprocessing. The automated method was validated with respect to segmentation overlap (Dice similarity coefficient, Dice) and estimations of FF, R2* and segmented volume. Bias in measurement results was also evaluated. Segmentation overlaps of Dice = 0.93 +/- 0.03 (mean +/- standard deviation) and correlation coefficients of r > 0.99 (P < 0.0001) in FF, R2* and volume estimates, between the methods, were observed. Dice and BMI were positively correlated (r = 0.54, P = 0.03) but no other significant bias was obtained (P >= 0.07). The automated method compared well with the reference method and can therefore be suitable for time-efficient and objective measurements in large cohort research studies of BAT.

National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-323968 (URN)10.1038/s41598-017-01586-7 (DOI)000402865000003 ()28596551 (PubMedID)
Funder
Swedish Research CouncilEU, FP7, Seventh Framework Programme, 279153
Available from: 2017-06-08 Created: 2017-06-12 Last updated: 2017-09-19Bibliographically approved
Etterlin, P. E., Ekman, S., Strand, R., Olstad, K. & Ley, C. J. (2017). Osteochondrosis, Synovial Fossae, and Articular Indentations in the Talus and Distal Tibia of Growing Domestic Pigs and Wild Boars. Veterinary pathology, 54(3), 445-456
Open this publication in new window or tab >>Osteochondrosis, Synovial Fossae, and Articular Indentations in the Talus and Distal Tibia of Growing Domestic Pigs and Wild Boars
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2017 (English)In: Veterinary pathology, ISSN 0300-9858, E-ISSN 1544-2217, Vol. 54, no 3, p. 445-456Article in journal (Refereed) Published
National Category
Veterinary Science Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-322715 (URN)10.1177/0300985816688743 (DOI)000400089300012 ()28129094 (PubMedID)
Funder
Swedish Research Council Formas, 221-2013-317
Available from: 2017-01-27 Created: 2017-05-29 Last updated: 2017-06-01Bibliographically approved
Ahlström, H., Ekström, S., Sjöholm, T., Strand, R., Kullberg, J., Johansson, E., . . . Malmberg, F. (2017). Registration-based automated lesion detection and therapy evaluation of tumors in whole body PET-MR images. Paper presented at 42nd European-Society-for-Medical-Oncology Congress (ESMO), SEP 08-12, 2017, Madrid, SPAIN. Annals of Oncology, 28(S5), Article ID 78P.
Open this publication in new window or tab >>Registration-based automated lesion detection and therapy evaluation of tumors in whole body PET-MR images
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2017 (English)In: Annals of Oncology, ISSN 0923-7534, E-ISSN 1569-8041, Vol. 28, no S5, article id 78PArticle in journal, Meeting abstract (Other academic) Published
National Category
Radiology, Nuclear Medicine and Medical Imaging Cancer and Oncology
Identifiers
urn:nbn:se:uu:diva-346976 (URN)000411324000073 ()
Conference
42nd European-Society-for-Medical-Oncology Congress (ESMO), SEP 08-12, 2017, Madrid, SPAIN
Available from: 2018-03-26 Created: 2018-03-26 Last updated: 2018-03-26Bibliographically approved
Malmberg, F., Nordenskjöld, R., Strand, R. & Kullberg, J. (2017). SmartPaint: a tool for interactive segmentation of medical volume images. Computer Methods In Biomechanics And Biomedical Engeineering-Imaging And Visualization, 5(1), 36-44
Open this publication in new window or tab >>SmartPaint: a tool for interactive segmentation of medical volume images
2017 (English)In: Computer Methods In Biomechanics And Biomedical Engeineering-Imaging And Visualization, ISSN 2168-1163, Vol. 5, no 1, p. 36-44Article in journal (Refereed) Published
Abstract [en]

We present SmartPaint, a general-purpose method and software for interactive segmentation of medical volume images. SmartPaint uses a novel paint-brush interaction paradigm, where the user segments objects in the image by 'sweeping' over them with the mouse cursor. The key feature of SmartPaint is that the painting tools adapt to the image content, selectively sticking to objects of interest while avoiding other structures. This behaviour is achieved by modulating the effect of the tools by both the Euclidean distance and the range distance (difference in image intensity values) from the mouse cursor. We evaluate SmartPaint on three publicly available medical image datasets, covering different image modalities and segmentation targets. The results show that, with a limited user effort, SmartPaint can produce segmentations whose accuracy is comparable to both the state-of-the-art automatic segmentation methods and manual delineations produced by expert users. The SmartPaint software is freely available, and can be downloaded from the authors' web page (http://www.cb.uu.se/similar to filip/SmartPaint/).

Place, publisher, year, edition, pages
TAYLOR & FRANCIS LTD, 2017
Keywords
image segmentation, medical imaging, interactive segmentation
National Category
Biomaterials Science
Identifiers
urn:nbn:se:uu:diva-319143 (URN)10.1080/21681163.2014.960535 (DOI)000396688800005 ()
Available from: 2017-03-31 Created: 2017-03-31 Last updated: 2018-05-14Bibliographically approved
Strand, R., Ciesielski, K. C., Malmberg, F. & Saha, P. K. (2017). The Minimum Barrier Distance: A Summary of Recent Advances. In: Lecture Notes in Computer Science book series (LNCS, volume 10502): . Paper presented at International Conference on Discrete Geometry for Computer Imagery (pp. 57-68). Switzerland, 10502
Open this publication in new window or tab >>The Minimum Barrier Distance: A Summary of Recent Advances
2017 (English)In: Lecture Notes in Computer Science book series (LNCS, volume 10502), Switzerland, 2017, Vol. 10502, p. 57-68Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present an overview and summary of recent results of the minimum barrier distance (MBD), a distance operator that is a promising tool in several image processing applications. The theory constitutes of the continuous MBD in Rn, its discrete formulation in Zn (in two different natural formulations), and of the discussion of convergence of discrete MBDs to their continuous counterpart. We describe two algorithms that compute MBD, one very fast but returning only approximate MBD, the other a bit slower, but returning the exact MBD. Finally, some image processing applications of MBD are presented and the directions of potential future research in this area are indicated.

Place, publisher, year, edition, pages
Switzerland: , 2017
National Category
Computer Sciences Discrete Mathematics
Identifiers
urn:nbn:se:uu:diva-333683 (URN)10.1007/978-3-319-66272-5_6 (DOI)978-3-319-66272-5 (ISBN)978-3-319-66271-8 (ISBN)
Conference
International Conference on Discrete Geometry for Computer Imagery
Available from: 2017-11-16 Created: 2017-11-16 Last updated: 2018-01-13Bibliographically approved
Asplund, T., Luengo, C., Thurley, M. & Strand, R. (2016). A New Approach to Mathematical Morphology on One Dimensional Sampled Signals. In: IEEE Proceedings, International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, 2016: . Paper presented at International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, 2016.
Open this publication in new window or tab >>A New Approach to Mathematical Morphology on One Dimensional Sampled Signals
2016 (English)In: IEEE Proceedings, International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, 2016, 2016Conference paper, Published paper (Refereed)
Abstract [en]

We present a new approach to approximate continuous-domain mathematical morphology operators. The approach is applicable to irregularly sampled signals. We define a dilation under this new approach, where samples are duplicated and shifted according to the flat, continuous structuring element. We define the erosion by adjunction, and the opening and closing by composition. These new operators will significantly increase precision in image measurements. Experiments show that these operators indeed approximate continuous-domain operators better than the standard operators on sampled one-dimensional signals, and that they may be applied to signals using structuring elements smaller than the distance between samples. We also show that we can apply the operators to scan lines of a two-dimensional image to filter horizontal and vertical linear structures.

National Category
Computer Sciences Computational Mathematics
Identifiers
urn:nbn:se:uu:diva-309925 (URN)10.1109/ICPR.2016.7900244 (DOI)000406771303148 ()
Conference
International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, 2016
Funder
Swedish Research Council, 2014-5983
Available from: 2016-12-08 Created: 2016-12-08 Last updated: 2018-03-16Bibliographically approved
Schold Linnér, E., Kullberg, J. & Strand, R. (2016). Fuzzy Segmentation of Synthetic and MRI Volume Data sampled on Optimal Lattices.
Open this publication in new window or tab >>Fuzzy Segmentation of Synthetic and MRI Volume Data sampled on Optimal Lattices
2016 (English)Article in journal (Other academic) Submitted
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-265336 (URN)
Available from: 2015-10-27 Created: 2015-10-27 Last updated: 2016-02-03Bibliographically approved
Schold Linnér, E., Morén, M., Smed, K.-O., Nysjö, J. & Strand, R. (2016). LatticeLibrary and BccFccRaycaster: Software for processing and viewing 3D data on optimal sampling lattices. SoftwareX, 5, 16-24
Open this publication in new window or tab >>LatticeLibrary and BccFccRaycaster: Software for processing and viewing 3D data on optimal sampling lattices
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2016 (English)In: SoftwareX, ISSN 2352-7110, Vol. 5, p. 16-24Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-265337 (URN)10.1016/j.softx.2016.01.002 (DOI)
Available from: 2016-03-15 Created: 2015-10-27 Last updated: 2016-12-28Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7764-1787

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