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Publications (10 of 101) Show all publications
Sjöholm, T., Ekström, S., Strand, R., Ahlström, H., Lind, L., Malmberg, F. & Kullberg, J. (2019). A whole-body FDG PET/MR atlas for multiparametric voxel-based analysis. Scientific Reports, 9, Article ID 6158.
Open this publication in new window or tab >>A whole-body FDG PET/MR atlas for multiparametric voxel-based analysis
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2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 6158Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-382934 (URN)10.1038/s41598-019-42613-z (DOI)000464652400029 ()30992502 (PubMedID)
Available from: 2019-04-16 Created: 2019-05-07 Last updated: 2019-06-14Bibliographically approved
Adler, J., Sintorn, I.-M., Strand, R. & Parmryd, I. (2019). Conventional analysis of movement on non-flat surfaces like the plasma membrane makes Brownian motion appear anomalous. Communications Biology, 2, Article ID 12.
Open this publication in new window or tab >>Conventional analysis of movement on non-flat surfaces like the plasma membrane makes Brownian motion appear anomalous
2019 (English)In: Communications Biology, ISSN 2399-3642, Vol. 2, article id 12Article in journal (Refereed) Published
National Category
Biophysics
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-380506 (URN)10.1038/s42003-018-0240-2 (DOI)000461148000001 ()30652124 (PubMedID)
Available from: 2019-01-08 Created: 2019-04-15 Last updated: 2019-05-07Bibliographically approved
Asplund, T., Serna, A., Marcotegui, B., Strand, R. & Luengo Hendriks, C. L. (2019). Mathematical Morphology on Irregularly Sampled Data Applied to Segmentation of 3D Point Clouds of Urban Scenes. In: International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing: . Paper presented at International Symposium on Mathematical Morphology (ISMM 2019).
Open this publication in new window or tab >>Mathematical Morphology on Irregularly Sampled Data Applied to Segmentation of 3D Point Clouds of Urban Scenes
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2019 (English)In: International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes an extension of mathematical morphology on irregularly sampled signals to 3D point clouds. The proposed method is applied to the segmentation of urban scenes to show its applicability to the analysis of point cloud data. Applying the proposed operators has the desirable side-effect of homogenizing signals that are sampled heterogeneously. In experiments we show that the proposed segmentation algorithm yields good results on the Paris-rue-Madame database and is robust in terms of sampling density, i.e. yielding similar labelings for more sparse samplings of the same scene.

National Category
Signal Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-388524 (URN)10.1007/978-3-030-20867-7_29 (DOI)978-3-030-20866-0 (ISBN)978-3-030-20867-7 (ISBN)
Conference
International Symposium on Mathematical Morphology (ISMM 2019)
Funder
Swedish Research Council, 2014-5983
Available from: 2019-07-01 Created: 2019-07-01 Last updated: 2019-07-01
Lind, L., Kullberg, J., Ahlström, H., Michaëlsson, K. & Strand, R. (2019). Proof of principle study of a detailed whole-body image analysis technique, "Imiomics", regarding adipose and lean tissue distribution. Scientific Reports, 9, Article ID 7388.
Open this publication in new window or tab >>Proof of principle study of a detailed whole-body image analysis technique, "Imiomics", regarding adipose and lean tissue distribution
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2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 7388Article in journal (Refereed) Published
Abstract [en]

This "proof-of-principle" study evaluates if the recently presented "Imiomics" technique could visualize how fat and lean tissue mass are associated with local tissue volume and fat content at high/unprecedented resolution. A whole-body quantitative water-fat MRI scan was performed in 159 men and 167 women aged 50 in the population-based POEM study. Total fat and lean mass were measured by DXA. Fat content was measured by the water-fat MRI. Fat mass and distribution measures were associated to the detailed differences in tissue volume and fat concentration throughout the body using Imiomics. Fat mass was positively correlated (r > 0.50, p < 0.05) with tissue volume in all subcutaneous areas of the body, as well as volumes of the liver, intraperitoneal fat, retroperitoneal fat and perirenal fat, but negatively to lung volume. Fat mass correlated positively with volumes of paravertebral muscles, and muscles in the ventral part of the thigh and lower limb. Fat mass was distinctly correlated with the fat content in subcutaneous adipose tissue at the trunk. Lean mass was positively related to the large skeletal muscles and the skeleton. The present study indicates the Imiomics technique to be suitable for studies of fat and lean tissue distribution, and feasible for large scale studies.

National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:uu:diva-383519 (URN)10.1038/s41598-019-43690-w (DOI)000467839800059 ()31089168 (PubMedID)
Available from: 2019-05-16 Created: 2019-05-16 Last updated: 2019-06-19Bibliographically approved
Roodakker, K. R., Alhuseinalkhudhur, A., Al-Jaff, M., Georganaki, M., Zetterling, M., Berntsson, S. G., . . . Smits, A. (2019). Region-by-region analysis of PET, MRI, and histology in en bloc-resected oligodendrogliomas reveals intra-tumoral heterogeneity. European Journal of Nuclear Medicine and Molecular Imaging, 46(3), 569-579
Open this publication in new window or tab >>Region-by-region analysis of PET, MRI, and histology in en bloc-resected oligodendrogliomas reveals intra-tumoral heterogeneity
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2019 (English)In: European Journal of Nuclear Medicine and Molecular Imaging, ISSN 1619-7070, E-ISSN 1619-7089, Vol. 46, no 3, p. 569-579Article in journal (Refereed) Published
National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-356591 (URN)10.1007/s00259-018-4107-z (DOI)000457151600005 ()30109401 (PubMedID)
Funder
Erik, Karin och Gösta Selanders Foundation
Available from: 2018-08-14 Created: 2018-08-08 Last updated: 2019-04-06Bibliographically approved
Nagy, B., Strand, R. & Normand, N. (2018). Distance Functions Based on Multiple Types of Weighted Steps Combined with Neighborhood Sequences. Journal of Mathematical Imaging and Vision, 60(8), 1209-1219
Open this publication in new window or tab >>Distance Functions Based on Multiple Types of Weighted Steps Combined with Neighborhood Sequences
2018 (English)In: Journal of Mathematical Imaging and Vision, ISSN 0924-9907, E-ISSN 1573-7683, Vol. 60, no 8, p. 1209-1219Article in journal (Refereed) Published
Abstract [en]

In this paper, we present a general framework for digital distance functions, defined as minimal cost paths, on the square grid. Each path is a sequence of pixels, where any two consecutive pixels are adjacent and associated with a weight. The allowed weights between any two adjacent pixels along a path are given by a weight sequence, which can hold an arbitrary number of weights. We build on our previous results, where only two or three unique weights are considered, and present a framework that allows any number of weights. We show that the rotational dependency can be very low when as few as three or four unique weights are used. Moreover, by using n weights, the Euclidean distance can be perfectly obtained on the perimeter of a square with side length 2n. A sufficient condition for weight sequences to provide metrics is proven.

Keywords
Distance functions, Weight sequences, Neighborhood sequences, Chamfer distances, Approximation of Euclidean distance
National Category
Computer Sciences
Identifiers
urn:nbn:se:uu:diva-364167 (URN)10.1007/s10851-018-0805-1 (DOI)000443369800003 ()
Available from: 2018-11-05 Created: 2018-11-05 Last updated: 2018-11-16Bibliographically approved
Guglielmo, P., Sjöholm, T., Enblad, G., Strand, R., Kullberg, J., Malberg, F. & Ahlström, H. (2018). Imiomics Using Whole-body FDG PET/MR in Staging and Treatment Response Evaluation of Non-Hodgkin Lymphoma Patients Treated With CAR-T Cells. Paper presented at 31st Annual Congress of the European-Association-of-Nuclear-Medicine (EANM), OCT 13-17, 2018, Dusseldorf, GERMANY. European Journal of Nuclear Medicine and Molecular Imaging, 45, S37-S38
Open this publication in new window or tab >>Imiomics Using Whole-body FDG PET/MR in Staging and Treatment Response Evaluation of Non-Hodgkin Lymphoma Patients Treated With CAR-T Cells
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2018 (English)In: European Journal of Nuclear Medicine and Molecular Imaging, ISSN 1619-7070, E-ISSN 1619-7089, Vol. 45, p. S37-S38Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
Springer, 2018
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:uu:diva-372960 (URN)000449266200052 ()
Conference
31st Annual Congress of the European-Association-of-Nuclear-Medicine (EANM), OCT 13-17, 2018, Dusseldorf, GERMANY
Available from: 2019-01-24 Created: 2019-01-24 Last updated: 2019-01-24Bibliographically approved
Dhara, A. K., Arids, E., Fahlström, M., Wikström, J., Larsson, E.-M. & Strand, R. (2018). Interactive segmentation of glioblastoma for post-surgical treatment follow-up. In: Proc. 24th International Conference on Pattern Recognition: . Paper presented at ICPR 2018, August 20–24, Beijing, China (pp. 1199-1204). IEEE
Open this publication in new window or tab >>Interactive segmentation of glioblastoma for post-surgical treatment follow-up
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2018 (English)In: Proc. 24th International Conference on Pattern Recognition, IEEE, 2018, p. 1199-1204Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2018
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-368290 (URN)10.1109/ICPR.2018.8545105 (DOI)000455146801036 ()978-1-5386-3788-3 (ISBN)
Conference
ICPR 2018, August 20–24, Beijing, China
Note

Best paper award

Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2019-02-21Bibliographically approved
Dhara, A. K., Ayyalasomayajula, K. R., Arvids, E., Fahlström, M., Wikström, J., Larsson, E.-M. & Strand, R. (2018). Segmentation of Post-operative Glioblastoma in MRI by U-Net with Patient-specific Interactive Refinement. In: Proceedings, Brain Lesion (BrainLes) workshop: . Paper presented at 21st INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING & COMPUTER ASSISTED INTERVENTION, September 16-20, 2018, Granada, Spain.
Open this publication in new window or tab >>Segmentation of Post-operative Glioblastoma in MRI by U-Net with Patient-specific Interactive Refinement
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2018 (English)In: Proceedings, Brain Lesion (BrainLes) workshop, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Accurate volumetric change estimation of glioblastoma is very important for post-surgical treatment follow-up. In this paper, an interactive segmentation method was developed and evaluated with the aim to guide volumetric estimation of glioblastoma. U-Net based fully convolutional network is used for initial segmentation of glioblastoma from post contrast MR images. The max flow algorithm is applied on the probability map of U-Net to update the initial segmentation and the result is displayed to the user for interactive refinement. Network update is performed based on the corrected contour by considering patient specific learning to deal with large context variations among dierent images. The proposed method is evaluated on a clinical MR image databas eof 15 glioblastoma patients with longitudinal scan data. The experimental results depict an improvement of segmentation performance due to patient specific fine-tuning. The proposed method is computationally fast and efficient as compared to state-of-the-art interactive segmentation tools. This tool could be useful for post-surgical treatment follow-upwith minimal user intervention.

National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-366550 (URN)
Conference
21st INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING & COMPUTER ASSISTED INTERVENTION, September 16-20, 2018, Granada, Spain
Funder
Swedish Research Council, 2014-6199Vinnova, 2017-02447
Note

Extended versions of all accepted papers will be published as LCNS proceedings by Springer-Verlag. http://www.brainlesion-workshop.org/

Available from: 2018-11-21 Created: 2018-11-21 Last updated: 2019-03-14Bibliographically approved
Malmberg, F. & Strand, R. (2018). When Can lp-norm Objective Functions Be Minimized via Graph Cuts?. In: Barneva R., Brimkov V., Tavares J. (Ed.), Combinatorial Image Analysis: . Paper presented at IWCIA 2018, International Workshop on Combinatorial Image Analysis, Nov 22, 2018 - Nov 24, Porto, Portugal (pp. 112-117). Springer
Open this publication in new window or tab >>When Can lp-norm Objective Functions Be Minimized via Graph Cuts?
2018 (English)In: Combinatorial Image Analysis / [ed] Barneva R., Brimkov V., Tavares J., Springer, 2018, p. 112-117Conference paper, Published paper (Refereed)
Abstract [en]

Techniques based on minimal graph cuts have become a standard tool for solving combinatorial optimization problems arising in image processing and computer vision applications. These techniques can be used to minimize objective functions written as the sum of a set of unary and pairwise terms, provided that the objective function is sub-modular. This can be interpreted as minimizing the l1-norm of the vector containing all pairwise and unary terms. By raising each term to a power p, the same technique can also be used to minimize the lp-norm of the vector. Unfortunately, the submodularity of an l1-norm objective function does not guarantee the submodularity of the corresponding lp-norm objective function. The contribution of this paper is to provide useful conditions under which an lp-norm objective function is submodular for all p>= 1, thereby identifying a large class of lp-norm objective functions that can be minimized via minimal graph cuts.

Techniques based on minimal graph cuts have become a standard tool for solving combinatorial optimization problems arising in image processing and computer vision applications. These techniques can be used to minimize objective functions written as the sum of a set of unary and pairwise terms, provided that the objective function is submodular. This can be interpreted as minimizing the l1l1-norm of the vector containing all pairwise and unary terms. By raising each term to a power p, the same technique can also be used to minimize the lplp-norm of the vector. Unfortunately, the submodularity of an l1l1-norm objective function does not guarantee the submodularity of the corresponding lplp-norm objective function. The contribution of this paper is to provide useful conditions under which an lplp-norm objective function is submodular for all p≥1p≥1, thereby identifying a large class of lplp-norm objective functions that can be minimized via minimal graph cuts.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Keywords
Minimal graph cuts, lp -norm, Submodularity
National Category
Discrete Mathematics Computer Sciences
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-366961 (URN)10.1007/978-3-030-05288-1_9 (DOI)978-3-030-05287-4 (ISBN)
Conference
IWCIA 2018, International Workshop on Combinatorial Image Analysis, Nov 22, 2018 - Nov 24, Porto, Portugal
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2019-03-14Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7764-1787

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