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Joldes GR, Miller K, Wittek A, Doyle B. A simple, effective and clinically applicable method to compute abdominal aortic aneurysm wall stress. J Mech Behav Biomed Mater 2016; 58:139-148. [PMID: 26282385 DOI: 10.1016/j.jmbbm.2015.07.029] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Revised: 07/22/2015] [Accepted: 07/27/2015] [Indexed: 10/23/2022]
Affiliation(s)
- Grand Roman Joldes
- Vascular Engineering, Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia
| | - Karol Miller
- Vascular Engineering, Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia.
| | - Adam Wittek
- Vascular Engineering, Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia
| | - Barry Doyle
- Vascular Engineering, Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia; Centre for Cardiovascular Science, The University of Edinburgh, UK
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Gao Y, Zhu L, Cates J, MacLeod RS, Bouix S, Tannenbaum A. A Kalman Filtering Perspective for Multiatlas Segmentation. SIAM JOURNAL ON IMAGING SCIENCES 2015; 8:1007-1029. [PMID: 26807162 PMCID: PMC4722821 DOI: 10.1137/130933423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas segmentation, inspired by the following fact: The transformation that aligns the current atlas to the novel image can be not only computed by direct registration but also inferred from the transformation that aligns the previous atlas to the image together with the transformation between the two atlases. This process is similar to the global positioning system on a vehicle, which gets position by inquiring from the satellite and by employing the previous location and velocity-neither answer in isolation being perfect. To solve this problem, a dynamical system scheme is crucial to combine the two pieces of information; for example, a Kalman filtering scheme is used. Accordingly, in this work, a Kalman multiatlas segmentation is proposed to stabilize the global/affine registration step. The contributions of this work are twofold. First, it provides a new dynamical systematic perspective for standard independent multiatlas registrations, and it is solved by Kalman filtering. Second, with very little extra computation, it can be combined with most existing multiatlas segmentation schemes for better registration/segmentation accuracy.
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Affiliation(s)
- Yi Gao
- Department of Biomedical Informatics and Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794
| | - Liangjia Zhu
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11790
| | - Joshua Cates
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112
| | - Rob S. MacLeod
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112
| | - Sylvain Bouix
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215
| | - Allen Tannenbaum
- Department of Computer Science and Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794
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Marami B, Sirouspour S, Ghoul S, Cepek J, Davidson SRH, Capson DW, Trachtenberg J, Fenster A. Elastic registration of prostate MR images based on estimation of deformation states. Med Image Anal 2015; 21:87-103. [PMID: 25624044 DOI: 10.1016/j.media.2014.12.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Revised: 12/15/2014] [Accepted: 12/19/2014] [Indexed: 10/24/2022]
Abstract
Magnetic resonance imaging (MRI) is being used increasingly for image-guided targeted biopsy and focal therapy of prostate cancer. In this paper, a combined rigid and deformable registration technique is proposed to register pre-treatment diagnostic 3T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5T scanner before insertion of the needle with patients in the semi-lithotomy position. Both the rigid and deformable registration algorithms employ an intensity-based distance metric defined based on the modality independent neighborhood descriptors (MIND) between images. The optimization routine for estimating the rigid transformation parameters is initialized using four pairs of manually selected approximate corresponding points on the boundaries of the prostate. In this paper, the problem of deformable image registration is approached from the perspective of state estimation for dynamical systems. The registration algorithm employs a rather generic dynamic linear elastic model of the tissue deformation discretized by the finite element method (FEM). We use the model in a classical state estimation framework to estimate the deformation of the prostate based on the distance metric between pre- and intra-treatment images. Our deformable registration results using 17 sets of prostate MR images showed that the proposed method yielded a target registration error (TRE) of 1.87 ± 0.94 mm,2.03 ± 0.94 mm, and 1.70 ± 0.93 mm for the whole gland (WG), central gland (CG), and peripheral zone (PZ), respectively, using 76 manually-identified fiducial points. This was an improvement over the 2.67 ± 1.31 mm, 2.95 ± 1.43 mm, and 2.34 ± 1.11 mm, respectively for the WG, CG, and PZ after rigid registration alone. Dice similarity coefficients (DSC) in the WG, CG and PZ were 88.2 ± 5.3, 85.6 ± 7.6 and 68.7 ± 6.9 percent, respectively. Furthermore, the mean absolute distances (MAD) between surfaces was 1.26 ± 0.56 mm and 1.27 ± 0.55 mm in the WG and CG, after deformable registration. These results indicate that the proposed registration technique has sufficient accuracy for localizing prostate tumors in MRI-guided targeted biopsy or focal therapy of clinically localized prostate cancer.
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Affiliation(s)
- Bahram Marami
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada; Biomedical Engineering Graduate Program, The University of Western Ontario, London, Ontario, Canada.
| | - Shahin Sirouspour
- Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada.
| | - Suha Ghoul
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada; Department of Medical Imaging, London Health Sciences Center, London, Ontario, Canada.
| | - Jeremy Cepek
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada; Biomedical Engineering Graduate Program, The University of Western Ontario, London, Ontario, Canada.
| | - Sean R H Davidson
- Ontario Cancer Institute, University Health Network, Toronto, Ontario, Canada.
| | - David W Capson
- Department of Electrical and Computer Engineering, University of Victoria, Victoria, British Columbia, Canada.
| | - John Trachtenberg
- Department of Surgical Oncology, University Health Network, Toronto, Ontario, Canada.
| | - Aaron Fenster
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada; Biomedical Engineering Graduate Program, The University of Western Ontario, London, Ontario, Canada; Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada.
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Gao Y, Tannenbaum A, Bouix S. A Framework for Joint Image-and-Shape Analysis. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2014; 9034:90340V. [PMID: 25302006 PMCID: PMC4187242 DOI: 10.1117/12.2043276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Techniques in medical image analysis are many times used for the comparison or regression on the intensities of images. In general, the domain of the image is a given Cartesian grids. Shape analysis, on the other hand, studies the similarities and differences among spatial objects of arbitrary geometry and topology. Usually, there is no function defined on the domain of shapes. Recently, there has been a growing needs for defining and analyzing functions defined on the shape space, and a coupled analysis on both the shapes and the functions defined on them. Following this direction, in this work we present a coupled analysis for both images and shapes. As a result, the statistically significant discrepancies in both the image intensities as well as on the underlying shapes are detected. The method is applied on both brain images for the schizophrenia and heart images for atrial fibrillation patients.
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Affiliation(s)
- Yi Gao
- Department of Electrical and Computer Engineering and the Comprehensive Cancer Center, the University of Alabama at Birmingham; 1150 10th Avenue South, Birmingham, AL 35294
| | - Allen Tannenbaum
- Departments of Computer Science and Applied Mathematics/Statistics, Stony Brook University, Stony Brook, New York, 11794
| | - Sylvain Bouix
- Department of Psychiatry, Harvard Medical School, 1249 Boylston St, Boston, MA, 02215
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Bagci U, Udupa JK, Mendhiratta N, Foster B, Xu Z, Yao J, Chen X, Mollura DJ. Joint segmentation of anatomical and functional images: applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images. Med Image Anal 2013; 17:929-45. [PMID: 23837967 PMCID: PMC3795997 DOI: 10.1016/j.media.2013.05.004] [Citation(s) in RCA: 101] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2012] [Revised: 03/09/2013] [Accepted: 05/08/2013] [Indexed: 11/25/2022]
Abstract
We present a novel method for the joint segmentation of anatomical and functional images. Our proposed methodology unifies the domains of anatomical and functional images, represents them in a product lattice, and performs simultaneous delineation of regions based on random walk image segmentation. Furthermore, we also propose a simple yet effective object/background seed localization method to make the proposed segmentation process fully automatic. Our study uses PET, PET-CT, MRI-PET, and fused MRI-PET-CT scans (77 studies in all) from 56 patients who had various lesions in different body regions. We validated the effectiveness of the proposed method on different PET phantoms as well as on clinical images with respect to the ground truth segmentation provided by clinicians. Experimental results indicate that the presented method is superior to threshold and Bayesian methods commonly used in PET image segmentation, is more accurate and robust compared to the other PET-CT segmentation methods recently published in the literature, and also it is general in the sense of simultaneously segmenting multiple scans in real-time with high accuracy needed in routine clinical use.
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Affiliation(s)
- Ulas Bagci
- Center for Infectious Diseases Imaging, National Institutes of Health, Bethesda, MD, United States; Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, United States.
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