| 121. |
- Petridou, Elia, et al.
(författare)
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Breast fat volume measurement in a wide-bore 3T MR: comparison of traditional mammographic density evaluation with MR density measurements using automatic segmentation.
- 2015
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Aims and objectivesVariations in breast density in imaging are caused by varying proportions of fat and fibro-glandular tissue. Breast density is an independent marker of breast cancer risk and therefore a number of techniques have been developed to measure breast density using different imaging modalities. The aim of this research was to compare a fully automated technique of producing volumetric measurements of fat and fibroglandular breast tissue from segmented magnetic resonance imaging (MRI) and to compare with the well-established, observer-dependent Breast Imaging Reporting and Data Systems (BI-RADS) density classification using mammography.Methods and materialsThis was a prospective inter-method comparison study. The study design was a prospective analysis of volumetric breast density obtained from breast MRI scans compared with mammographic breast density using BIRADS. Ethical approval for the study was obtained from the local Research Ethics Committee. 40 women undergoing mammography and dynamic breast MRI as part of their clinical management were recruited. Fat-water separated MR images derived from a 2 point Dixon technique using phase-sensitive reconstruction and atlas based segmentation were obtained before and after the administration of intravenous gadolinium. Breast density, which was defined the proportion of breast fat subtracted from the total volume of the breast, was assessed using proprietary software (Advanced MR Analytics (AMRA), Linköping, Sweden). The method was previously described and first used for measurement of abdominal fat.The results were compared to the widely used four-quartile quantitative BIRADS scale undertaken by two experienced breast radiologists. ResultsThe mean unenhanced breast percentage of fibro-glandular tissue measured on MRI was 0.31 ± 0.22 (mean ± SD) for the left and 0.29 ± 0.21 for the right. The mean density on the contrast-enhanced images was 0.32 ± 0.19 for the left and 0.32 ± 0.2 for right. There was "almost perfect" correlation between the quantification pre and post-contrast breast fibro- glandular tissue quantification: Spearman correlation rho=0.98 (95% confidence intervals (CI): 0.97-0.99) for the left and rho=0.99 (CI: 0.98-0.99) for the right.For each of the BIRADS scores 1-4 observer 1 scored a total number of breasts as n=2,35,26,15 (total 80) and observer 2 scored n=4,25,45,16 respectively. Correlation between BIRADS scores and automated MRI breast density was significant for both operators, Spearman Correlation coefficient rho=0.75. ConclusionAutomated breast fat density measurement using MR correlates strongly with the current mammographic standard BIRADS. Results for percentage fibro-glandular component on unenhanced breast MR correlate very closely with post-contrast MR. Breast density measurements derived from automated segmentation of unenhanced breast MRI could be used instead of mammographic measurements for assessing breast cancer risk.
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| 122. |
- Pettersson, Johanna, 1977-, et al.
(författare)
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A hip surgery simulator based on patient specific models generated by automatic segmentation
- 2006
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Ingår i: Medicine Meets Virtual Reality 14: Accelerating Change in Healthcare. - Amsterdam, Nederländerna : IOS Press. - 9781586035839 ; , s. 431-436
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Konferensbidrag (refereegranskat)abstract
- The use of surgical simulator systems for education and preoperative planning is likely to increase in the future. A natural course of development of these systems is to incorporate patient specific anatomical models. This step requires some kind of segmentation process in which the different anatomical parts are extracted. Anatomical datasets are, however, usually very large and manual processing would be too demanding. Hence, automatic, or semi-automatic, methods to handle this step are required. The framework presented in this paper uses nonrigid registration, based on the morphon method, to automatically segment the hip anatomy and generate models for a hip surgery simulator system.
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| 123. |
- Pettersson, Johanna, 1977-, et al.
(författare)
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Automatic hip bone segmentation using non-rigid registration
- 2006
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Ingår i: 18th International Conference on Pattern Recognition, 2006, ICPR 2006 (Volume:3 ). - : IEEE Computer Society. - 0769525210 ; , s. 946-949
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Konferensbidrag (refereegranskat)abstract
- This paper presents a method for automatic segmentation of bone from volumetric computed tomography (CT) data. Due to osteoporosis, which degenerates the bone density and hence decreases the intensity of the bone in the CT dataset, it is not possible to use conventional thresholding techniques to handle the segmentation. Furthermore we want to use prior knowledge about shapes and relations of the bones in the area of interest to be able to e.g. separate adjoining bones from each other. The method we suggest is the morphon algorithm in Knutsson and Andersson (2005). This is a non-rigid registration technique where an 2D or 3D image is iteratively deformed to match the corresponding structure in a target image. The method uses difference in local quadrature phase and certainty measures to estimate the deformations
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| 124. |
- Pettersson, Johanna, et al.
(författare)
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Automatic segmentation of CT and MR volume data using non-rigid morphon registration
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Automatic segmentation of anatomical structures is often performed using model based non-rigid registration methods. The morphon algorithm is one such method. In this algorithm, two or three dimensional images are registered using a multi-resolution deformation scheme. A prototype image is iteratively registered to a target image, using local phase difference to estimate the displacement between the images. This method has been extended with normalised convolution, to guide the registration process using prior knowledge about the target image. By defining a certainty mask used in the normalised convolution one can either specify a region of interest in the target image, or, on the other hand, occlude regions in the image that should not affect the matching. Two different applications are presented, which each demonstrates the use of this method for automatic segmentation.
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| 125. |
- Pettersson, Johanna, 1977-, et al.
(författare)
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Generation of patient specific bone models from volume data using morphons
- 2005
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Ingår i: IFMBE Proceedings. - Umeå : IFMBE. ; , s. 199-200
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Konferensbidrag (refereegranskat)abstract
- The use of simulator systems for surgical planning and training is growing as the systems become more advanced. One important feature of these systems is the possibility to work on real patient data. This paper presents a method for generating patient-specific models of the femoral bone and the pelvis to be used in a hip surgery simulator. The bones are segmented from volumetric CT data using the Morphon method [3], where a prototype pattern is iteratively morphed to fit the corresponding structure in the input data.
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| 126. |
- Pettersson, Johanna, 1977-, et al.
(författare)
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Non-rigid registration for automatic fracture segmentation
- 2006
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Ingår i: Image Processing, 2006. - Atlanta : IEEE. - 1424404800 ; , s. 1185-1188
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Konferensbidrag (refereegranskat)abstract
- Automatic segmentation of anatomical structures is often performed using model-based non-rigid registration methods. These algorithms work well when the images do not contain any large deviations from the normal anatomy. We have previously used such a method to generate patient specific models of hip bones for surgery simulation. The method that was used, the morphon method, registers two-or three-dimensional images using a multi-resolution deformation scheme. A prototype image is iteratively registered to a target image using quadrature filter phase difference to estimate the local displacement. The morphon method has in this work been extended to deal with automatic segmentation of fractured bones. Two features have been added. First, the method is modified such that multiple prototypes (in this case two) can be used. Second, normalised convolution is utilized for the displacement estimation, to guide the registration of the second prototype, based on the result of the registration of the first one
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| 127. |
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| 128. |
- Pettersson, Johanna, 1977-, et al.
(författare)
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Segmentation and registration with the Morphon method, four different applications
- 2007
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Ingår i: Proceedings of the {SSBA} Symposium on Image Analysis,2007.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The Morphon method has shown to be a useful non-rigid registration method since it was first presented in 2005. This paper demonstrates how the method has been adapted for four different applications; hip fracture segmentation from CT data, hippocampus segmentation from MR data and registration the prostate and the head-neck region from CT data for radiotherapy planning.
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| 129. |
- Pettersson, Johanna, et al.
(författare)
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Simulation of Patient Specific Cervical Hip Fracture Surgery With a Volume Haptic Interface
- 2008
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Ingår i: IEEE Transactions on Biomedical Engineering. - : IEEE. - 0018-9294 .- 1558-2531. ; 55:4, s. 1255-1265
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Tidskriftsartikel (refereegranskat)abstract
- The interest for surgery simulator systems with anatomical models generated from authentic patient data is growing as these systems evolve.With access to volumetric patient data, e.g., from a computer tomography scan, haptic and visual feedback can be created directly from this dataset. This opens the door for patient specific simulations. Hip fracture surgery is one area where simulator systems is useful to train new surgeons and plan operations. To simulate the drilling procedure in this type of surgery, a repositioning of the fractured bone into correct position is first needed. This requires a segmentation process in which the bone segments are identified and the position of the dislocated part is determined. The segmentation must be automatic to cope with the large amount of data from the computer tomography scan. This work presents the first steps in the development of a hip fracture surgery simulation with patient specific models. Visual and haptic feedback is generated from the computer tomography data by simulating fluoroscopic images and the drilling process. We also present an automatic segmentation method to identify the fractured bone and determine the dislocation. This segmentation method is based on nonrigid registration with the Morphon method.
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| 130. |
- Pettersson, Johanna, 1977-, et al.
(författare)
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Some issues on the segmentation of the femur in CT data
- 2004
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Ingår i: Proceedings of the Swedish Symposium on Image Analysis (2004). ; , s. 158-161
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents a recently started project which goal is to automatically generate patient specific models for visual and haptic simulation of hip fracture surgery. It includes a preliminary study of a computed tomography (CT) dataset of the pelvic region. The paper emphasizes some issues encountered when segmenting bones in this region, especially in the area around the proximal femur.
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