| 1. |
- Maria Marreiros, Filipe Miguel, 1978-, et al.
(author)
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Non-rigid Deformation Pipeline for Compensation of Superficial Brain Shift
- 2013
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In: Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642407628 - 9783642407635 ; , s. 141-148
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Conference paper (peer-reviewed)abstract
- The correct visualization of anatomical structures is a critical component of neurosurgical navigation systems, to guide the surgeon to the areas of interest as well as to avoid brain damage. A major challenge for neuronavigation systems is the brain shift, or deformation of the exposed brain in comparison to preoperative Magnetic Resonance (MR) image sets. In this work paper, a non-rigid deformation pipeline is proposed for brain shift compensation of preoperative imaging datasets using superficial blood vessels as landmarks. The input was preoperative and intraoperative 3D image sets of superficial vessel centerlines. The intraoperative vessels (obtained using 3 Near-Infrared cameras) were registered and aligned with preoperative Magnetic Resonance Angiography vessel centerlines using manual interaction for the rigid transformation and, for the non-rigid transformation, the non-rigid point set registration method Coherent Point Drift. The rigid registration transforms the intraoperative points from the camera coordinate system to the preoperative MR coordinate system, and the non-rigid registration deals with local transformations in the MR coordinate system. Finally, the generation of a new deformed volume is achieved with the Thin-Plate Spline (TPS) method using as control points the matches in the MR coordinate system found in the previous step. The method was tested in a rabbit brain exposed via craniotomy, where deformations were produced by a balloon inserted into the brain. There was a good correlation between the real state of the brain and the deformed volume obtained using the pipeline. Maximum displacements were approximately 4.0 mm for the exposed brain alone, and 6.7 mm after balloon inflation.
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| 2. |
- Maria Marreiros, Filipe Miguel, 1978-, et al.
(author)
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Non-rigid point set registration of curves : registration of the superficial vessel centerlines of the brain
- 2016
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In: MEDICAL IMAGING 2016. - : SPIE - International Society for Optical Engineering. - 9781510600218
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Conference paper (peer-reviewed)abstract
- In this study we present a non-rigid point set registration for 3D curves (composed by 3D set of points). The method was evaluated in the task of registration of 3D superficial vessels of the brain where it was used to match vessel centerline points. It consists of a combination of the Coherent Point Drift (CPD) and the Thin-Plate Spline (TPS) semilandmarks. The CPD is used to perform the initial matching of centerline 3D points, while the semilandmark method iteratively relaxes/slides the points. For the evaluation, a Magnetic Resonance Angiography (MRA) dataset was used. Deformations were applied to the extracted vessels centerlines to simulate brain bulging and sinking, using a TPS deformation where a few control points were manipulated to obtain the desired transformation (T-1). Once the correspondences are known, the corresponding points are used to define a new TPS deformation(T-2). The errors are measured in the deformed space, by transforming the original points using T-1 and T-2 and measuring the distance between them. To simulate cases where the deformed vessel data is incomplete, parts of the reference vessels were cut and then deformed. 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 in the presence of noise and incomplete data.
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| 3. |
- Medrano-Gracia, Pau, et al.
(author)
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Construction of a coronary artery atlas from CT angiography
- 2014
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In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. - Cham : Springer. ; , s. 513-520
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Conference paper (peer-reviewed)abstract
- Describing the detailed statistical anatomy of the coronary artery tree is important for determining the ætiology of heart disease. A number of studies have investigated geometrical features and have found that these correlate with clinical outcomes, e.g. bifurcation angle with major adverse cardiac events. These methodologies were mainly two-dimensional, manual and prone to inter-observer variability, and the data commonly relates to cases already with pathology. We propose a hybrid atlasing methodology to build a population of computational models of the coronary arteries to comprehensively and accurately assess anatomy including 3D size, geometry and shape descriptors. A random sample of 122 cardiac CT scans with a calcium score of zero was segmented and analysed using a standardised protocol. The resulting atlas includes, but is not limited to, the distributions of the coronary tree in terms of angles, diameters, centrelines, principal component shape analysis and cross-sectional contours. This novel resource will facilitate the improvement of stent design and provide a reference for hemodynamic simulations, and provides a basis for large normal and pathological databases.
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| 4. |
- Moreno, Rodrigo, 1973-, et al.
(author)
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Vessel Wall Segmentation Using Implicit Models and Total Curvature Penalizers
- 2013
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In: IMAGE ANALYSIS, SCIA 2013. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642388859 - 9783642388866 ; , s. 299-308
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Conference paper (peer-reviewed)abstract
- This book constitutes the refereed proceedings of the 18th Scandinavian Conference on Image Analysis, SCIA 2013, held in Espoo, Finland, in June 2013. The 67 revised full papers presented were carefully reviewed and selected from 132 submissions. The papers are organized in topical sections on feature extraction and segmentation, pattern recognition and machine learning, medical and biomedical image analysis, faces and gestures, object and scene recognition, matching, registration, and alignment, 3D vision, color and multispectral image analysis, motion analysis, systems and applications, human-centered computing, and video and multimedia analysis.
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| 5. |
- Wang, Chunliang, 1980-, et al.
(author)
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Automatic multi-organ segmentation in non-enhanced CT datasets using hierarchical shape priors
- 2014
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In: 2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR). - : IEEE Computer Society. - 9781479952083 - 9781479952090 ; , s. 3327-3332
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Conference paper (peer-reviewed)abstract
- An automatic multi-organ segmentation method using hierarchical-shape-prior guided level sets is proposed. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, so that major structures with less population variety are at the top and smaller structures with higher irregularities are linked at a lower level. The segmentation is performed in a top-down fashion, where major structures are first segmented with higher confidence, and their location information is then passed down to the lower level to initialize the segmentation, while boundary information from higher-level structures also provides extra cues to guide the segmentation of the lower-level structures. The proposed method was combined with a novel coherent propagating level set method, which is capable to detect local convergence and skip calculation in those parts, therefore significantly reducing computation time. Preliminary experiment results on a small number of clinical datasets are encouraging; the proposed method yielded a Dice coefficient above 90% for most major organs within a reasonable processing time without any user intervention.
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| 6. |
- Wang, Chunliang, 1980-, et al.
(author)
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Automatic multi–organ segmentation using fast model based level set method and hierarchical shape priors
- 2014
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In: Proceedings of the VISCERAL Organ Segmentation and Landmark Detection Challenge, co-located with IEEE International Symposium on Biomedical Imaging (ISBI 2014), Beijing, China, May 1, 2014. - : CEUR-WS. ; , s. 25-31
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Conference paper (peer-reviewed)abstract
- An automatic multi-organ segmentation pipeline is presented. The segmentation starts with stripping the body of skin and subcutaneous fat using threshold-based level-set methods. After registering the image to be processed against a standard subject picked from the training datasets, a series of model-based level set segmentation operations is carried out guided by hierarchical shape priors. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, starting with ventral cavity, and then divided into thoracic cavity and abdominopelvic cavity. The third level contains the individual organs such as liver, spleen and kidneys. The segmentation is performed in a top-down fashion, where major structures are segmented first, and their location information is then passed down to the lower level to initialize the segmentation, while boundary information from higher-level structures also constrains the segmentation of the lower-level structures. In our preliminary experiments, the proposed method yielded a Dice coeffcient around 90% for most major thoracic and abdominal organs in both contrastenhanced CT and non-enhanced datasets, while the average running time for segmenting ten organs was about 10 minutes.
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| 7. |
- Wang, Chunliang, 1980-, et al.
(author)
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Coronary artery segmentation and skeletonization based on competing fuzzy connectedness tree
- 2007
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In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 9783540757566 - 9783540757573 ; , s. 311-318
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Conference paper (peer-reviewed)abstract
- We propose a new segmentation algorithm based on competing fuzzy connectedness theory, which is then used for visualizing coronary arteries in 3D CT angiography (CTA) images. The major difference compared to other fuzzy connectedness algorithms is that an additional data structure, the connectedness tree, is constructed at the same time as the seeds propagate. In preliminary evaluations, accurate result have been achieved with very limited user interaction. In addition to improving computational speed and segmentation results, the fuzzy connectedness tree algorithm also includes automated extraction of the vessel centerlines, which is a promising approach for creating curved plane reformat (CPR) images along arteries’ long axes.
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| 8. |
- Wang, Chunliang, 1980-, et al.
(author)
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Fully automatic brain segmentation using model-guided level sets and skeleton-based models
- 2013
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Conference paper (peer-reviewed)abstract
- A fully automatic brain segmentation method is presented. First the skull is stripped using a model-based level set on T1-weighted inversion recovery images, then the brain ventricles and basal ganglia are segmented using the same method on T1-weighted images. The central white matter is segmented using a regular level set method but with high curvature regulation. To segment the cortical gray matter, a skeleton-based model is created by extracting the mid-surface of the gray matter from a preliminary segmentation using a threshold-based level set. An implicit model is then built by defining the thickness of the gray matter to be 2.7 mm. This model is incorporated into the level set framework and used to guide a second round more precise segmentation. Preliminary experiments show that the proposed method can provide relatively accurate results compared with the segmentation done by human observers. The processing time is considerably shorter than most conventional automatic brain segmentation methods.
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| 9. |
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| 10. |
- Wang, Chunliang, et al.
(author)
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Level-set based vessel segmentation accelerated with periodic monotonic speed function
- 2011
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In: Medical Imaging 2011. - : SPIE - International Society for Optical Engineering. - 9780819485045 ; , s. 79621M-1-79621M-7
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Conference paper (peer-reviewed)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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