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Klintström, B., Klintström, E., Smedby, Ö. & Moreno, R. (2017). Feature space clustering for trabecular bone segmentation. In: Sharma P., Bianchi F. (Ed.), Image Analysis - 20th Scandinavian Conference on Image Analysis, SCIA 2017, Proceedings: . Paper presented at 20th Scandinavian Conference on Image Analysis (SCIA), Tromsö 12-14 juni 2017 (pp. 65-70). Springer, 10270.
Open this publication in new window or tab >>Feature space clustering for trabecular bone segmentation
2017 (English)In: Image Analysis - 20th Scandinavian Conference on Image Analysis, SCIA 2017, Proceedings / [ed] Sharma P., Bianchi F., Springer, 2017, Vol. 10270, 65-70 p.Conference paper, Published paper (Refereed)
Abstract [en]

Trabecular bone structure has been shown to impact bone strength and fracture risk. In vitro, this structure can be measured by micro-computed tomography (micro-CT). For clinical use, it would be valuable if multi-slice computed tomography (MSCT) could be used to analyse trabecular bone structure. One important step in the analysis is image volume segmentation. Previous segmentation techniques have either been computer resource intensive or produced suboptimal results when used on MSCT data. This paper proposes a new segmentation method that tries to balance good results against computational complexity.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10270
Keyword
Clustering, Feature-space, Segmentation, Trabecular bone
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-142938 (URN)10.1007/978-3-319-59129-2_6 (DOI)978-3-319-59128-5 (ISBN)978-3-319-59129-2 (ISBN)
Conference
20th Scandinavian Conference on Image Analysis (SCIA), Tromsö 12-14 juni 2017
Available from: 2017-11-13 Created: 2017-11-13 Last updated: 2017-11-13
Chowdhury, M., Klintström, B., Klintström, E., Smedby, Ö. & Moreno, R. (2017). Granulometry-Based Trabecular Bone Segmentation. In: Sharma P., Bianchi F. (Ed.), Image Analysis - 20th Scandinavian Conference on Image Analysis, SCIA 2017, Proceedings: . Paper presented at 20th Scandinavian Conference on Image Analysis (SCIA), Tromsö 12-14 juni 2017 (pp. 100-108). Springer, 10270.
Open this publication in new window or tab >>Granulometry-Based Trabecular Bone Segmentation
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2017 (English)In: Image Analysis - 20th Scandinavian Conference on Image Analysis, SCIA 2017, Proceedings / [ed] Sharma P., Bianchi F., Springer, 2017, Vol. 10270, 100-108 p.Conference paper, Published paper (Refereed)
Abstract [en]

The accuracy of the analyses for studying the three dimensionaltrabecular bone microstructure rely on the quality of the segmentationbetween trabecular bone and bone marrow. Such segmentationis challenging for images from computed tomography modalities thatcan be used in vivo due to their low contrast and resolution. For thispurpose, we propose in this paper a granulometry-based segmentationmethod. In a first step, the trabecular thickness is estimated by usingthe granulometry in gray scale, which is generated by applying the openingmorphological operation with ball-shaped structuring elements ofdifferent diameters. This process mimics the traditional sphere-fittingmethod used for estimating trabecular thickness in segmented images.The residual obtained after computing the granulometry is comparedto the original gray scale value in order to obtain a measurement ofhow likely a voxel belongs to trabecular bone. A threshold is applied toobtain the final segmentation. Six histomorphometric parameters werecomputed on 14 segmented bone specimens imaged with cone-beam computedtomography (CBCT), considering micro-computed tomography(micro-CT) as the ground truth. Otsu’s thresholding and AutomatedRegion Growing (ARG) segmentation methods were used for comparison.For three parameters (Tb.N, Tb.Th and BV/TV), the proposedsegmentation algorithm yielded the highest correlations with micro-CT,while for the remaining three (Tb.Nd, Tb.Tm and Tb.Sp), its performancewas comparable to ARG. The method also yielded the strongestaverage correlation (0.89). When Tb.Th was computed directly fromthe gray scale images, the correlation was superior to the binary-basedmethods. The results suggest that the proposed algorithm can be usedfor studying trabecular bone in vivo through CBCT.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10270
Keyword
Cone beam computed tomography; Segmentation; Granulometry; Trabecular bone
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-142961 (URN)10.1007/978-3-319-59129-2_9 (DOI)
Conference
20th Scandinavian Conference on Image Analysis (SCIA), Tromsö 12-14 juni 2017
Available from: 2017-11-13 Created: 2017-11-13 Last updated: 2017-12-01Bibliographically approved
Lidayova, K., Frimmel, H., Wang, C., Bengtsson, E. & Smedby, Ö. (2016). Fast vascular skeleton extraction algorithm. Pattern Recognition Letters, 76, 67-75.
Open this publication in new window or tab >>Fast vascular skeleton extraction algorithm
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2016 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 76, 67-75 p.Article in journal (Refereed) Published
Abstract [en]

Vascular diseases are a common cause of death, particularly in developed countries. Computerized image analysis tools play a potentially important role in diagnosing and quantifying vascular pathologies. Given the size and complexity of modern angiographic data acquisition, fast, automatic and accurate vascular segmentation is a challenging task. In this paper we introduce a fully automatic high-speed vascular skeleton extraction algorithm that is intended as a first step in a complete vascular tree segmentation program. The method takes a 3D unprocessed Computed Tomography Angiography (CTA) scan as input and produces a graph in which the nodes are centrally located artery voxels and the edges represent connections between them. The algorithm works in two passes where the first pass is designed to extract the skeleton of large arteries and the second pass focuses on smaller vascular structures. Each pass consists of three main steps. The first step sets proper parameters automatically using Gaussian curve fitting. In the second step different filters are applied to detect voxels nodes - that are part of arteries. In the last step the nodes are connected in order to obtain a continuous centerline tree for the entire vasculature. Structures found, that do not belong to the arteries, are removed in a final anatomy-based analysis. The proposed method is computationally efficient with an average execution time of 29 s and has been tested on a set of CTA scans of the lower limbs achieving an average overlap rate of 97% and an average detection rate of 71%. (C) 2015 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV, 2016
Keyword
Skeleton extraction; Centerline tree; Vascular tree; Blood vessels; CT angiography
National Category
Clinical Medicine
Identifiers
urn:nbn:se:liu:diva-128720 (URN)10.1016/j.patrec.2015.06.024 (DOI)000375135600009 ()
Note

Funding Agencies|Swedish Council for Research [VR-NT 2014-6153]

Available from: 2016-06-07 Created: 2016-05-30 Last updated: 2017-11-30
Maria Marreiros, F. M., Wang, C., Rossitti, S. & Smedby, Ö. (2016). Non-rigid point set registration of curves: registration of the superficial vessel centerlines of the brain. In: Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling. Paper presented at Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, California, United States, February 27, 2016 (pp. 978611-1-978611-8). SPIE - International Society for Optical Engineering, 9786.
Open this publication in new window or tab >>Non-rigid point set registration of curves: registration of the superficial vessel centerlines of the brain
2016 (English)In: Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE - International Society for Optical Engineering, 2016, Vol. 9786, 8 p.978611-1-978611-8 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this study we present a non-rigid point set registration for 3D curves (composed by 3D set of points). Themethod was evaluated in the task of registration of 3D superficial vessels of the brain where it was used to matchvessel centerline points. It consists of a combination of the Coherent Point Drift (CPD) and the Thin-PlateSpline (TPS) semilandmarks. The CPD is used to perform the initial matching of centerline 3D points, whilethe semilandmark method iteratively relaxes/slides the points.

For the evaluation, a Magnetic Resonance Angiography (MRA) dataset was used. Deformations were appliedto the extracted vessels centerlines to simulate brain bulging and sinking, using a TPS deformation where afew control points were manipulated to obtain the desired transformation (T1). Once the correspondences areknown, the corresponding points are used to define a new TPS deformation(T2). The errors are measured in thedeformed space, by transforming the original points using T1 and T2 and measuring the distance between them.To simulate cases where the deformed vessel data is incomplete, parts of the reference vessels were cut and thendeformed. Furthermore, anisotropic normally distributed noise was added.

The results show that the error estimates (root mean square error and mean error) are below 1 mm, even inthe presence of noise and incomplete data.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2016. 8 p.
Series
Progress in Biomedical Optics, ISSN 1605-7422 ; 9786
Keyword
Non-rigid registration, brain shift correction, vessel registration
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-126347 (URN)10.1117/12.2208421 (DOI)000382315800036 ()978-1-5106-0021-8 (ISBN)
Conference
Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, California, United States, February 27, 2016
Projects
ARIOR
Funder
Swedish Childhood Cancer Foundation, MT2013-0036
Available from: 2016-03-22 Created: 2016-03-22 Last updated: 2016-09-27Bibliographically approved
Klintström, E., Klintström, B., Moreno, R., Brismar, T. B., Pahr, D. H. & Smedby, Ö. (2016). Predicting Trabecular Bone Stiffness from Clinical Cone-Beam CT and HR-pQCT Data; an In Vitro Study Using Finite Element Analysis. PLoS ONE, 11(8), Article ID e0161101.
Open this publication in new window or tab >>Predicting Trabecular Bone Stiffness from Clinical Cone-Beam CT and HR-pQCT Data; an In Vitro Study Using Finite Element Analysis
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2016 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 8, e0161101Article in journal (Refereed) Published
Abstract [en]

Stiffness and shear moduli of human trabecular bone may be analyzed in vivo by finite element (FE) analysis from image data obtained by clinical imaging equipment such as high resolution peripheral quantitative computed tomography (HR-pQCT). In clinical practice today, this is done in the peripheral skeleton like the wrist and heel. In this cadaveric bone study, fourteen bone specimens from the wrist were imaged by two dental cone beam computed tomography (CBCT) devices and one HR-pQCT device as well as by dual energy X-ray absorptiometry (DXA). Histomorphometric measurements from micro-CT data were used as gold standard. The image processing was done with an in-house developed code based on the automated region growing (ARG) algorithm. Evaluation of how well stiffness (Young’s modulus E3) and minimum shear modulus from the 12, 13, or 23 could be predicted from the CBCT and HR-pQCT imaging data was studied and compared to FE analysis from the micro-CT imaging data. Strong correlations were found between the clinical machines and micro-CT regarding trabecular bone structure parameters, such as bone volume over total volume, trabecular thickness, trabecular number and trabecular nodes (varying from 0.79 to 0.96). The two CBCT devices as well as the HR-pQCT showed the ability to predict stiffness and shear, with adjusted R2 -values between 0.78 and 0.92, based on data derived through our in-house developed code based on the ARG algorithm. These findings indicate that clinically used CBCT may be a feasible method for clinical studies of bone structure and mechanical properties in future osteoporosis research.

Place, publisher, year, edition, pages
Public library of science, 2016
National Category
Clinical Medicine
Identifiers
urn:nbn:se:liu:diva-130798 (URN)10.1371/journal.pone.0161101 (DOI)000381381100120 ()27513664 (PubMedID)
Available from: 2016-08-24 Created: 2016-08-24 Last updated: 2017-11-28Bibliographically approved
Blystad, I., Håkansson, I., Tisell, A., Ernerudh, J., Smedby, Ö., Lundberg, P. & Larsson, E.-M. (2016). Quantitative MRI for Analysis of Active Multiple Sclerosis Lesions without Gadolinium-Based Contrast Agent. American Journal of Neuroradiology, 37(1), 94-100.
Open this publication in new window or tab >>Quantitative MRI for Analysis of Active Multiple Sclerosis Lesions without Gadolinium-Based Contrast Agent
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2016 (English)In: American Journal of Neuroradiology, ISSN 0195-6108, E-ISSN 1936-959X, Vol. 37, no 1, 94-100 p.Article in journal (Refereed) Published
Abstract [en]

BACKGROUND AND PURPOSE: Contrast-enhancing MS lesions are important markers of active inflammation in the diagnostic work-up of MS and in disease monitoring with MR imaging. Because intravenous contrast agents involve an expense and a potential risk of adverse events, it would be desirable to identify active lesions without using a contrast agent. The purpose of this study was to evaluate whether pre-contrast injection tissue-relaxation rates and proton density of MS lesions, by using a new quantitative MR imaging sequence, can identify active lesions. MATERIALS AND METHODS: Forty-four patients with a clinical suspicion of MS were studied. MR imaging with a standard clinical MS protocol and a quantitative MR imaging sequence was performed at inclusion (baseline) and after 1 year. ROIs were placed in MS lesions, classified as nonenhancing or enhancing. Longitudinal and transverse relaxation rates, as well as proton density were obtained from the quantitative MR imaging sequence. Statistical analyses of ROI values were performed by using a mixed linear model, logistic regression, and receiver operating characteristic analysis. RESULTS: Enhancing lesions had a significantly (P < .001) higher mean longitudinal relaxation rate (1.22 0.36 versus 0.89 +/- 0.24), a higher mean transverse relaxation rate (9.8 +/- 2.6 versus 7.4 +/- 1.9), and a lower mean proton density (77 +/- 11.2 versus 90 +/- 8.4) than nonenhancing lesions. An area under the receiver operating characteristic curve value of 0.832 was obtained. CONCLUSIONS: Contrast-enhancing MS lesions often have proton density and relaxation times that differ from those in nonenhancing lesions, with lower proton density and shorter relaxation times in enhancing lesions compared with nonenhancing lesions.

Place, publisher, year, edition, pages
AMER SOC NEURORADIOLOGY, 2016
National Category
Clinical Medicine
Identifiers
urn:nbn:se:liu:diva-124482 (URN)10.3174/ajnr.A4501 (DOI)000367466500019 ()26471751 (PubMedID)
Note

Funding Agencies|National Science and Engineering Research Council; University of Linkoping; University Hospital Research Funds

Available from: 2016-02-02 Created: 2016-02-01 Last updated: 2017-11-16
Bernard, O., Bosch, J. G., Heyde, B., Alessandrini, M., Barbosa, D., Camarasu-Pop, S., . . . Dhooge, J. (2016). Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography. IEEE Transactions on Medical Imaging, 35(4), 967-977.
Open this publication in new window or tab >>Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography
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2016 (English)In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 35, no 4, 967-977 p.Article in journal (Refereed) Published
Abstract [en]

Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts variability range. The platform remains open for new submissions.

Place, publisher, year, edition, pages
IEEE Press, 2016
Keyword
Endocardium; left ventricle segmentation; real-time 3D echocardiography; standardized evaluation system
National Category
Clinical Medicine
Identifiers
urn:nbn:se:liu:diva-127780 (URN)10.1109/TMI.2015.2503890 (DOI)000374164800004 ()26625409 (PubMedID)
Note

Funding agencies: Riksbankens Jubileumsfond; VINNOVA

Available from: 2016-05-12 Created: 2016-05-12 Last updated: 2017-11-30
Maria Marreiros, F. M., Rossitti, S., Karlsson, P., Wang, C., Gustafsson, T., Carleberg, P. & Smedby, Ö. (2016). Superficial vessel reconstruction with a multiview camera system. Journal of Medical Imaging, 3(1), 015001-1-015001-13.
Open this publication in new window or tab >>Superficial vessel reconstruction with a multiview camera system
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2016 (English)In: Journal of Medical Imaging, ISSN 2329-4302, E-ISSN 2329-4310, Vol. 3, no 1, 015001-1-015001-13 p.Article in journal (Refereed) Published
Abstract [en]

We aim at reconstructing superficial vessels of the brain. Ultimately, they will serve to guide the deformationmethods to compensate for the brain shift. A pipeline for three-dimensional (3-D) vessel reconstructionusing three mono-complementary metal-oxide semiconductor cameras has been developed. Vessel centerlinesare manually selected in the images. Using the properties of the Hessian matrix, the centerline points areassigned direction information. For correspondence matching, a combination of methods was used. The processstarts with epipolar and spatial coherence constraints (geometrical constraints), followed by relaxation labelingand an iterative filtering where the 3-D points are compared to surfaces obtained using the thin-plate spline withdecreasing relaxation parameter. Finally, the points are shifted to their local centroid position. Evaluation invirtual, phantom, and experimental images, including intraoperative data from patient experiments, showsthat, with appropriate camera positions, the error estimates (root-mean square error and mean error) are∼1 mm.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2016
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-123661 (URN)10.1117/1.JMI.3.1.015001 (DOI)
Projects
ARIOR
Funder
Swedish Childhood Cancer Foundation, MT2013-0036
Available from: 2016-01-05 Created: 2016-01-05 Last updated: 2018-01-10Bibliographically approved
Moreno, R., Borga, M., Klintström, E., Brismar, T. & Smedby, Ö. (2015). Anisotropy Estimation of Trabecular Bone in Gray-Scale: Comparison Between Cone Beam and Micro Computed Tomography Data. In: João Manuel R.S. Tavares and Renato Natal Jorge (Ed.), Developments in Medical Image Processing and Computational Vision: (pp. 207-220). Springer.
Open this publication in new window or tab >>Anisotropy Estimation of Trabecular Bone in Gray-Scale: Comparison Between Cone Beam and Micro Computed Tomography Data
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2015 (English)In: Developments in Medical Image Processing and Computational Vision / [ed] João Manuel R.S. Tavares and Renato Natal Jorge, Springer, 2015, 207-220 p.Chapter in book (Refereed)
Abstract [en]

Measurement of anisotropy of trabecular bone has clinical relevance in osteoporosis. In this study, anisotropy measurements of 15 trabecular bone biopsies from the radius estimated by different fabric tensors on images acquired through cone beam computed tomography (CBCT) and micro computed tomography (micro-CT) were compared. The results show that the generalized mean intercept length (MIL) tensor performs better than the global gray-scale structure tensor, especially when the von Mises-Fisher kernel is applied. Also, the generalized MIL tensor yields consistent results between the two scanners. These results suggest that this tensor is appropriate for estimating anisotropy in images acquired in vivo through CBCT. 

Place, publisher, year, edition, pages
Springer, 2015
Series
Lecture Notes in Computational Vision and Biomechanics, ISSN 2212-9391 ; 19
National Category
Medical Image Processing Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-117950 (URN)10.1007/978-3-319-13407-9_13 (DOI)978-3-319-13406-2 (ISBN)978-3-319-13407-9 (ISBN)
Available from: 2015-05-18 Created: 2015-05-18 Last updated: 2015-07-10Bibliographically approved
Klintström, E., Klintström, B., Brismar, T., Smedby, Ö. & Moreno, R. (2015). Clinical dental cone beam computed tomography - a tool for monitoring trabecular bone structure?. In: : . Paper presented at European Congress of Radiology (ECR), Vienna, Austria, March 4-8 2015 (pp. C1213). .
Open this publication in new window or tab >>Clinical dental cone beam computed tomography - a tool for monitoring trabecular bone structure?
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2015 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Purpose

The aim of this in vitro study was to develop a method for quantitative assessment of trabecular bone micro-architecture by using three-dimensional image processing. The imaging data were acquired with cone beam computed tomography (CBCT), traditionally used for facial and temporal bone imaging but also applicable for peripheral skeleton, and with a dedicated high resolution peripheral computed tomograph (HRpQCT), used for in vivo measurements in bone research. The data from micro-computed tomography (µCT) was used as reference.

 

Methods & Materials

15 bone samples from the radius, were examined by CBCT and HRpQCT at a resolution of 80 and 82 µm, respectively. After segmentation, the bone structure parameters bone volume (BV/TV), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp), trabecular number (Tb.N), trabecular nodes (Tb.Nd) and trabecular termini (Tb.Tm) were quantified. Calculations were performed on an ordinary PC using a MATLAB developed in house.

 

Results

CBCT and HRpQCT overestimated BV/TV and Tb.Th approximately three times, compared to µCT. On the other hand Tb.Nd was highly underestimated. All parameters from CBCT were strongly correlated to µCT, with correlation coefficients above 0.91 for all studied parameters (0.92-0.98) except for Tb.Tm with a correlation of 0.83. For HRpQCT the correlations were slightly weaker, varying from 0.78 to 0.95.

 

Conclusion

The strong correlations between bone structure parameters computed from CBCT and µCT suggests that CBCT may be a good alternative to HRpQCT for monitoring trabecular bone microarchitecture in vivo.

 

Keyword
Osteoporosis, Computer Applications-3D, PACS, CT, Musculoskeletal bone, Head and neck
National Category
Medical Image Processing Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-121371 (URN)10.1594/ecr2015/C-1213 (DOI)
Conference
European Congress of Radiology (ECR), Vienna, Austria, March 4-8 2015
Available from: 2015-09-15 Created: 2015-09-15 Last updated: 2017-11-14Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7750-1917

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