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| 6. |
- Frimmel, Hans
(författare)
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Biopsy Needle Optimisation
- 1997
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Ingår i: Proc. 10th Scandinavian Conference on Image Analysis. - : Pattern Recognition Society of Finland. - 9517641451 ; , s. 381-387
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Konferensbidrag (refereegranskat)
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| 10. |
- Frimmel, Hans, 1968-
(författare)
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Positioning Biopsy Needles in the Prostate Gland Using 3D Computer Modelling
- 1999
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In the world of medicine, image diagnostics have, until recently, been based merely on two dimensional information sources. The understanding of three dimensional structures has been limited to creating mental images in the mind of the physician, to wax models and to autopsy. In the last few years, computers have made it possible to model and reconstruct real three dimensional objects and thus give the physician a new tool not only to describe localisation and distribution patterns of diseases, above all cancer, but also as an aid in the understanding of the human body. This thesis contributes in the development of such tools, based on a specific application.Prostate cancer is for men the most common form of cancer. Improvement in diagnostics for this form of cancer would facilitate planning of treatment and hence save, and preserve the quality of, life. One way to diagnose and quantify prostate cancer is to assess its presence and malignancy grade in cylindrical tissue samples taken with a needle biopsy device. Today, two to six such samples are generally taken, with poorly standardised rules for the positioning of the needle, thus interindividual variation exists.In this thesis, 3D models to analyse the problem with the positioning of biopsy needles have been developed. By using information from physical prostates removed from patients by surgery, a 3D cancer probability distribution has been built. Using this information, a standardised biopsy needle protocol has been created that is efficient, stable and easy to use. In this process new methods for morphing images, registrating slices and optimising positions for use with computer modelling have been developed.Many physicians were involved in the study. Thus, an important part of the work has been to make every part of the work understandable for people without special computer programming knowledge. Also, efforts have been made to make it possible to easily examine every piece of information created in order to verify the correctness of the methods used.
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| 13. |
- Kullberg, Joel, 1979-
(författare)
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Assessment of Body Composition Using Magnetic Resonance Imaging
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Methods for assessment of body composition allow studies of the complex relationships between body composition and the wellbeing of humans. The amount and distribution of adipose tissue is of great importance in these studies. This thesis describes, proposes, and evaluates new methods for assessment of body composition (amount and distribution of adipose tissue) using magnetic resonance imaging (MRI). The thesis focuses on the use of MRI but includes results from computed tomography (CT) and dual energy X-ray absorptiometry (DEXA). The subject data used originates from cohorts recruited solely for the purpose of the included studies and from the “Prospective Investigation of Vasculature in Uppsala Seniors” (PIVUS) and the Sib-pair (within the framework of the Swedish Obese Subjects - SOS study) studies.The included studies propose a new acquisition method for whole-body analysis of adipose tissue, a fully automated post processing algorithm for segmentation of visceral and subcutaneous adipose tissue from abdominal MRI data, and anthropometrical measurements for practical estimations of body composition.The proposed acquisition method for whole-body adipose tissue analysis simplified the analysis of adipose tissue and the results strongly correlated with the results from CT and DEXA analysis. The fully automated post processing algorithm gave reproducible results with relatively high accuracy. Transverse and sagittal abdominal diameters gave information about subcutaneous and visceral adipose tissue, respectively, and an elliptical approximation was found useful in estimation of total amount of abdominal adipose tissue.The methods proposed in this thesis were found useful for assessment of body composition. The methods were developed with clinical practice in mind and all proposed methods have been used in further studies for assessment of body composition.
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| 15. |
- Kullberg, Joel, et al.
(författare)
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Whole-body adipose tissue analysis: comparison of MRI, CT and dual energy X-ray absorptiometry.
- 2009
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Ingår i: The British journal of radiology. - : British Institute of Radiology. - 1748-880X .- 0007-1285. ; 82:974, s. 123-30
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this study was to validate a recently proposed MRI-based T(1)-mapping method for analysis of whole-body adipose tissue (AT) using an established CT protocol as reference and to include results from dual energy X-ray absorptiometry (DEXA). 10 subjects, drawn from the Swedish Obese Subjects Sibling-pairs study, were examined using CT, MRI and DEXA. The CT analysis was based on 28 imaged slices. T(1) maps were calculated using contiguous MRI data from two different gradient echo sequences acquired using different flip angles. CT and MRI comparison was performed slice-wise and for the whole-body region. Fat weights were compared between all three modalities. Strong correlations (r > or = 0.977, p<0.0001) were found between MRI and CT whole-body and AT volumes. MRI visceral AT volume was underestimated by 0.79 +/- 0.75 l (p = 0.005), but total AT was not significantly different from that estimated by CT (MRI - CT = -0.61+/-1.17 l; p = 0.114). DEXA underestimated fat weights by 5.23 +/- 1.71 kg (p = 0.005) compared with CT. MRI underestimated whole-body volume by 2.03 +/- 1.61 l (p = 0.005) compared with CT. Weights estimated either by CT or by DEXA were not significantly different from weights measured using scales. In conclusion, strong correlations were found between whole-body AT results from CT, MRI-based T(1) mapping and DEXA. If the differences between the results from T(1)-mapping and CT-based analysis are accepted, the T(1)-mapping method allows fully automated post-processing of whole-body MRI data, allowing longitudinal whole-body studies that are also applicable for children and adolescents.
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| 17. |
- Lidayová, Kristína, et al.
(författare)
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Airway-tree segmentation in subjects with acute respiratory distress syndrome
- 2017
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Ingår i: 20th Scandinavian Conference on Image Analysis, SCIA 2017. - Cham : Springer. - 9783319591285 ; , s. 76-87
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Konferensbidrag (refereegranskat)abstract
- Acute respiratory distress syndrome (ARDS) is associated with a high mortality rate in intensive care units. To lower the number of fatal cases, it is necessary to customize the mechanical ventilator parameters according to the patient’s clinical condition. For this, lung segmentation is required to assess aeration and alveolar recruitment. Airway segmentation may be used to reach a more accurate lung segmentation. In this paper, we seek to improve lung segmentation results by proposing a novel automatic airway-tree segmentation that is able to address the heterogeneity of ARDS pathology by handling various lung intensities differently. The method detects a simplified airway skeleton, thereby obtains a set of seed points together with an approximate radius and intensity range related to each of the points. These seeds are the input for an onion-kernel region-growing segmentation algorithm where knowledge about radius and intensity range restricts the possible leakage in the parenchyma. The method was evaluated qualitatively on 70 thoracic Computed Tomography volumes of subjects with ARDS, acquired at significantly different mechanical ventilation conditions. It found a large proportion of airway branches including tiny poorly-aerated bronchi. Quantitative evaluation was performed indirectly and showed that the resulting airway segmentation provides important anatomic landmarks. Their correspondences are needed to help a registration-based segmentation of the lungs in difficult ARDS cases where the lung boundary contrast is completely missing. The proposed method takes an average time of 43 s to process a thoracic volume which is valuable for the clinical use.
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| 18. |
- Lidayová, Kristína, et al.
(författare)
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Classification of cross-sections for vascular skeleton extraction using convolutional neural networks
- 2017
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Ingår i: 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017. - Cham : Springer. - 9783319609638 ; , s. 182-194
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Konferensbidrag (refereegranskat)abstract
- Recent advances in Computed Tomography Angiography provide high-resolution 3D images of the vessels. However, there is an inevitable requisite for automated and fast methods to process the increased amount of generated data. In this work, we propose a fast method for vascular skeleton extraction which can be combined with a segmentation algorithm to accelerate the vessel delineation. The algorithm detects central voxels - nodes - of potential vessel regions in the orthogonal CT slices and uses a convolutional neural network (CNN) to identify the true vessel nodes. The nodes are gradually linked together to generate an approximate vascular skeleton. The CNN classifier yields a precision of 0.81 and recall of 0.83 for the medium size vessels and produces a qualitatively evaluated enhanced representation of vascular skeletons.
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| 19. |
- Lidayová, Kristína, et al.
(författare)
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Coverage segmentation of 3D thin structures
- 2015
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Ingår i: Image Processing Theory, Tools and Applications (IPTA), 2015 International Conference on. - Piscataway, NJ : IEEE conference proceedings. - 9781479986361 ; , s. 23-28
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Konferensbidrag (refereegranskat)abstract
- We present a coverage segmentation method for extracting thin structures in three-dimensional images. The proposed method is an improved extension of our coverage segmentation method for 2D thin structures. We suggest implementation that enables low memory consumption and processing time, and by that applicability of the method on real CTA data. The method needs a reliable crisp segmentation as an input and uses information from linear unmixing and the crisp segmentation to create a high-resolution crisp reconstruction of the object, which can then be used as a final result, or down-sampled to a coverage segmentation at the starting image resolution. Performed quantitative and qualitative analysis confirm excellent performance of the proposed method, both on synthetic and on real data, in particular in terms of robustness to noise.
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| 20. |
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| 21. |
- Lidayová, Kristína, 1987-
(författare)
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Fast Methods for Vascular Segmentation Based on Approximate Skeleton Detection
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Modern medical imaging techniques have revolutionized health care over the last decades, providing clinicians with high-resolution 3D images of the inside of the patient's body without the need for invasive procedures. Detailed images of the vascular anatomy can be captured by angiography, providing a valuable source of information when deciding whether a vascular intervention is needed, for planning treatment, and for analyzing the success of therapy. However, increasing level of detail in the images, together with a wide availability of imaging devices, lead to an urgent need for automated techniques for image segmentation and analysis in order to assist the clinicians in performing a fast and accurate examination.To reduce the need for user interaction and increase the speed of vascular segmentation, we propose a fast and fully automatic vascular skeleton extraction algorithm. This algorithm first analyzes the volume's intensity histogram in order to automatically adapt the internal parameters to each patient and then it produces an approximate skeleton of the patient's vasculature. The skeleton can serve as a seed region for subsequent surface extraction algorithms. Further improvements of the skeleton extraction algorithm include the expansion to detect the skeleton of diseased arteries and the design of a convolutional neural network classifier that reduces false positive detections of vascular cross-sections. In addition to the complete skeleton extraction algorithm, the thesis presents a segmentation algorithm based on modified onion-kernel region growing. It initiates the growing from the previously extracted skeleton and provides a rapid binary segmentation of tubular structures. To provide the possibility of extracting precise measurements from this segmentation we introduce a method for obtaining a segmentation with subpixel precision out of the binary segmentation and the original image. This method is especially suited for thin and elongated structures, such as vessels, since it does not shrink the long protrusions. The method supports both 2D and 3D image data.The methods were validated on real computed tomography datasets and are primarily intended for applications in vascular segmentation, however, they are robust enough to work with other anatomical tree structures after adequate parameter adjustment, which was demonstrated on an airway-tree segmentation.
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| 22. |
- Lidayová, Kristína, et al.
(författare)
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Fast vascular skeleton extraction algorithm
- 2016
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Ingår i: Pattern Recognition Letters. - : Elsevier. - 0167-8655 .- 1872-7344. ; 76, s. 67-75
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Tidskriftsartikel (refereegranskat)abstract
- 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 29s 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%.
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| 23. |
- Lidayová, Kristína, et al.
(författare)
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Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation
- 2017
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Ingår i: Journal of Medical Imaging. - : SPIE - International Society for Optical Engineering. - 2329-4302 .- 2329-4310. ; 4:2
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Tidskriftsartikel (refereegranskat)abstract
- Vascular segmentation plays an important role in the assessment of peripheral arterial disease. The segmentation is very challenging especially for arteries with severe stenosis or complete occlusion. We present a cascading algorithm for vascular centerline tree detection specializing in detecting centerlines in diseased peripheral arteries. It takes a three-dimensional computed tomography angiography (CTA) volume and returns a vascular centerline tree, which can be used for accelerating and facilitating the vascular segmentation. The algorithm consists of four levels, two of which detect healthy arteries of varying sizes and two that specialize in different types of vascular pathology: severe calcification and occlusion. We perform four main steps at each level: appropriate parameters for each level are selected automatically, a set of centrally located voxels is detected, these voxels are connected together based on the connection criteria, and the resulting centerline tree is corrected from spurious branches. The proposed method was tested on 25 CTA scans of the lower limbs, achieving an average overlap rate of 89% and an average detection rate of 82%. The average execution time using four CPU cores was 70 s, and the technique was successful also in detecting very distal artery branches, e. g., in the foot.
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| 30. |
- Tizon, X., et al.
(författare)
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Identification of the main arterial branches by whole-body contrast-enhanced MRA in elderly subjects using limited user interaction fast marching
- 2007
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Ingår i: Journal of Magnetic Resonance Imaging. - : Wiley. - 1053-1807 .- 1522-2586. ; 25:4, s. 806-814
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To extract a graph model corresponding to a predefined set of arterial branches from whole-body contrast-enhanced magnetic resonance angiography (CE-MRA) data sets in elderly asymptomatic subjects, a high-incidence group. Materials and Methods: Maximum intensity projections (MIPs) were used as an interface to place landmarks in the three-dimensional (3D) data sets. These landmarks were linked together using fast marching to form a graph model of the arterial tree. Only vessels of interest were identified. Results: We tested our method on 10 subjects. We were able to build a graph model of the main arterial branches that performed well in the presence of vascular pathologies, such as stenosis and aneurysm. The results were rated by an experienced radiologist, with an overall success rate of 80%. Conclusion: We were able to extract chosen arterial branches in 3D whole-body CE-MRA images with a moderate amount of interaction using a single MIP projection. © 2007 Wiley-Liss, Inc.
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| 31. |
- Wang, Chunliang, 1980-, et al.
(författare)
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An interactive software module for visualizing coronary arteries in CT angiography
- 2008
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Ingår i: International Journal of Computer Assisted Radiology and Surgery. - Heidelberg/Berlin : Springer. - 1861-6410 .- 1861-6429. ; 3:1-2, s. 11-18
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Tidskriftsartikel (refereegranskat)abstract
- A new software module for coronary artery segmentation and visualization in CT angiography (CTA) datasets is presented, which aims to interactively segment coronary arteries and visualize them in 3D with maximum intensity projection (MIP) and volume rendering (VRT).Materials and Methods: The software was built as a plug-in for the open-source PACS workstation OsiriX. The main segmentation function is based an optimized “virtual contrast injection” algorithm, which uses fuzzy connectedness of the vessel lumen to separate the contrast-filled structures from each other. The software was evaluated in 42 clinical coronary CTA datasets acquired with 64-slice CT using isotropic voxels of 0.3–0.5 mm.Results: The median processing time was 6.4 min, and 100% of main branches (right coronary artery, left circumflex artery and left anterior descending artery) and 86.9% (219/252) of visible minor branches were intact. Visually correct centerlines were obtained automatically in 94.7% (321/339) of the intact branches.Conclusion: The new software is a promising tool for coronary CTA post-processing providing good overviews of the coronary artery with limited user interaction on low-end hardware, and the coronary CTA diagnosis procedure could potentially be more time-efficient than using thin-slab technique.
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| 32. |
- Wang, Chunliang, 1980-
(författare)
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Computer-Assisted Coronary CT Angiography Analysis : From Software Development to Clinical Application
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Advances in coronary Computed Tomography Angiography (CTA) have resulted in a boost in the use of this new technique in recent years, creating a challenge for radiologists due to the increasing number of exams and the large amount of data for each patient. The main goal of this study was to develop a computer tool to facilitate coronary CTA analysis by combining knowledge of medicine and image processing, and to evaluate the performance in clinical settings.Firstly, a competing fuzzy connectedness tree algorithm was developed to segment the coronary arteries and extract centerlines for each branch. The new algorithm, which is an extension of the “virtual contrast injection” (VC) method, preserves the low-density soft tissue around the artery, and thus reduces the possibility of introducing false positive stenoses during segmentation. Visually reasonable results were obtained in clinical cases.Secondly, this algorithm was implemented in open source software in which multiple visualization techniques were integrated into an intuitive user interface to facilitate user interaction and provide good overviews of the processing results. An automatic seeding method was introduced into the interactive segmentation workflow to eliminate the requirement of user initialization during post-processing. In 42 clinical cases, all main arteries and more than 85% of visible branches were identified, and testing the centerline extraction in a reference database gave results in good agreement with the gold standard.Thirdly, the diagnostic accuracy of coronary CTA using the segmented 3D data from the VC method was evaluated on 30 clinical coronary CTA datasets and compared with the conventional reading method and a different 3D reading method, region growing (RG), from a commercial software. As a reference method, catheter angiography was used. The percentage of evaluable arteries, accuracy and negative predictive value (NPV) for detecting stenosis were, respectively, 86%, 74% and 93% for the conventional method, 83%, 71% and 92% for VC, and 64%, 56% and 93% for RG. Accuracy was significantly lower for the RG method than for the other two methods (p<0.01), whereas there was no significant difference in accuracy between the VC method and the conventional method (p = 0.22).Furthermore, we developed a fast, level set-based algorithm for vessel segmentation, which is 10-20 times faster than the conventional methods without losing segmentation accuracy. It enables quantitative stenosis analysis at interactive speed.In conclusion, the presented software provides fast and automatic coronary artery segmentation and visualization. The NPV of using only segmented 3D data is as good as using conventional 2D viewing techniques, which suggests a potential of using them as an initial step, with access to 2D reviewing techniques for suspected lesions and cases with heavy calcification. Combining the 3D visualization of segmentation data with the clinical workflow could shorten reading time.
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| 33. |
- Wang, Chunliang, 1980-
(författare)
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Computer Assisted Coronary CT Angiography Analysis : Disease-centered Software Development
- 2009
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The substantial advances of coronary CTA have resulted in a boost of use of this new technique in the last several years, which brings a big challenge to radiologists by the increasing number of exams and the large amount of data for each patient. The main goal of this study was to develop a computer tool to facilitate coronary CTA analysis by combining knowledge of medicine and image processing.Firstly, a competing fuzzy connectedness tree algorithm was developed to segment the coronary arteries and extract centerlines for each branch. The new algorithm, which is an extension of the “virtual contrast injection” method, preserves the low density soft tissue around the coronary, which reduces the possibility of introducing false positive stenoses during segmentation.Secondly, this algorithm was implemented in open source software in which multiple visualization techniques were integrated into an intuitive user interface to facilitate user interaction and provide good over¬views of the processing results. Considerable efforts were put on optimizing the computa¬tional speed of the algorithm to meet the clinical requirements.Thirdly, an automatic seeding method, that can automatically remove rib cage and recognize the aortic root, was introduced into the interactive segmentation workflow to further minimize the requirement of user interactivity during post-processing. The automatic procedure is carried out right after the images are received, which saves users time after they open the data. Vessel enhance¬ment and quantitative 2D vessel contour analysis are also included in this new version of the software. In our preliminary experience, visually accurate segmentation results of major branches have been achieved in 74 cases (42 cases reported in paper II and 32 cases in paper III) using our software with limited user interaction. On 128 branches of 32 patients, the average overlap between the centerline created in our software and the manually created reference standard was 96.0%. The average distance between them was 0.38 mm, lower than the mean voxel size. The automatic procedure ran for 3-5 min as a single-thread application in the background. Interactive processing took 3 min in average with the latest version of software. In conclusion, the presented software provides fast and automatic coron¬ary artery segmentation and visualization. The accuracy of the centerline tracking was found to be acceptable when compared to manually created centerlines.
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| 34. |
- Wang, Chunliang, 1980-, et al.
(författare)
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Fast level-set based image segmentation using coherent propagation
- 2014
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Ingår i: Medical physics (Lancaster). - : John Wiley and Sons Ltd. - 0094-2405. ; 41:7
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The level-set method is known to require long computation time for 3D image segmentation, which limits its usage in clinical workflow. The goal of this study was to develop a fast level-set algorithm based on the coherent propagation method and explore its character using clinical datasets. Methods: The coherent propagation algorithm allows level set functions to converge faster by forcing the contour to move monotonically according to a predicted developing trend. Repeated temporary backwards propagation, caused by noise or numerical errors, is then avoided. It also makes it possible to detect local convergence, so that the parts of the boundary that have reached their final position can be excluded in subsequent iterations, thus reducing computation time. To compensate for the overshoot error, forward and backward coherent propagation is repeated periodically. This can result in fluctuations of great magnitude in parts of the contour. In this paper, a new gradual convergence scheme using a damping factor is proposed to address this problem. The new algorithm is also generalized to non-narrow band cases. Finally, the coherent propagation approach is combined with a new distance-regularized level set, which eliminates the needs of reinitialization of the distance. Results: Compared with the sparse field method implemented in the widely available ITKSnap software, the proposed algorithm is about 10 times faster when used for brain segmentation and about 100 times faster for aorta segmentation. Using a multiresolution approach, the new method achieved 50 times speed-up in liver segmentation. The Dice coefficient between the proposed method and the sparse field method is above 99% in most cases. Conclusions: A generalized coherent propagation algorithm for level set evolution yielded substantial improvement in processing time with both synthetic datasets and medical images.
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| 35. |
- Wang, Chunliang, et al.
(författare)
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Level-set based vessel segmentation accelerated with periodic monotonic speed function
- 2011
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Ingår i: Medical Imaging 2011. - : SPIE - International Society for Optical Engineering. - 9780819485045 ; , s. 79621M-1-79621M-7
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Konferensbidrag (refereegranskat)abstract
- To accelerate level-set based abdominal aorta segmentation on CTA data, we propose a periodic monotonic speed function, which allows segments of the contour to expand within one period and to shrink in the next period, i.e., coherent propagation. This strategy avoids the contour’s local wiggling behavior which often occurs during the propagating when certain points move faster than the neighbors, as the curvature force will move them backwards even though the whole neighborhood will eventually move forwards. Using coherent propagation, these faster points will, instead, stay in their places waiting for their neighbors to catch up. A period ends when all the expanding/shrinking segments can no longer expand/shrink, which means that they have reached the border of the vessel or stopped by the curvature force. Coherent propagation also allows us to implement a modified narrow band level set algorithm that prevents the endless computation in points that have reached the vessel border. As these points’ expanding/shrinking trend changes just after several iterations, the computation in the remaining iterations of one period can focus on the actually growing parts. Finally, a new convergence detection method is used to permanently stop updating the local level set function when the 0-level set is stationary in a voxel for several periods. The segmentation stops naturally when all points on the contour are stationary. In our preliminary experiments, significant speedup (about 10 times) was achieved on 3D data with almost no loss of segmentation accuracy.
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