276 research outputs found

    Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences

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    In this article, we present a graph-based method using a cubic template for volumetric segmentation of vertebrae in magnetic resonance imaging (MRI) acquisitions. The user can define the degree of deviation from a regular cube via a smoothness value Delta. The Cube-Cut algorithm generates a directed graph with two terminal nodes (s-t-network), where the nodes of the graph correspond to a cubic-shaped subset of the image's voxels. The weightings of the graph's terminal edges, which connect every node with a virtual source s or a virtual sink t, represent the affinity of a voxel to the vertebra (source) and to the background (sink). Furthermore, a set of infinite weighted and non-terminal edges implements the smoothness term. After graph construction, a minimal s-t-cut is calculated within polynomial computation time, which splits the nodes into two disjoint units. Subsequently, the segmentation result is determined out of the source-set. A quantitative evaluation of a C++ implementation of the algorithm resulted in an average Dice Similarity Coefficient (DSC) of 81.33% and a running time of less than a minute.Comment: 23 figures, 2 tables, 43 references, PLoS ONE 9(4): e9338

    Probabilistic and geometric shape based segmentation methods.

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    Image segmentation is one of the most important problems in image processing, object recognition, computer vision, medical imaging, etc. In general, the objective of the segmentation is to partition the image into the meaningful areas using the existing (low level) information in the image and prior (high level) information which can be obtained using a number of features of an object. As stated in [1,2], the human vision system aims to extract and use as much information as possible in the image including but not limited to the intensity, possible motion of the object (in sequential images), spatial relations (interaction) as the existing information, and the shape of the object which is learnt from the experience as the prior information. The main objective of this dissertation is to couple the prior information with the existing information since the machine vision system cannot predict the prior information unless it is given. To label the image into meaningful areas, the chosen information is modelled to fit progressively in each of the regions by an optimization process. The intensity and spatial interaction (as the existing information) and shape (as the prior information) are modeled to obtain the optimum segmentation in this study. The intensity information is modelled using the Gaussian distribution. Spatial interaction that describes the relation between neighboring pixels/voxels is modelled by assuming that the pixel intensity depends on the intensities of the neighboring pixels. The shape model is obtained using occurrences of histogram of training shape pixels or voxels. The main objective is to capture the shape variation of the object of interest. Each pixel in the image will have three probabilities to be an object and a background class based on the intensity, spatial interaction, and shape models. These probabilistic values will guide the energy (cost) functionals in the optimization process. This dissertation proposes segmentation frameworks which has the following properties: i) original to solve some of the existing problems, ii) robust under various segmentation challenges, and iii) fast enough to be used in the real applications. In this dissertation, the models are integrated into different methods to obtain the optimum segmentation: 1) variational (can be considered as the spatially continuous), and 2) statistical (can be considered as the spatially discrete) methods. The proposed segmentation frameworks start with obtaining the initial segmentation using the intensity / spatial interaction models. The shape model, which is obtained using the training shapes, is registered to the image domain. Finally, the optimal segmentation is obtained using the optimization of the energy functionals. Experiments show that the use of the shape prior improves considerably the accuracy of the alternative methods which use only existing or both information in the image. The proposed methods are tested on the synthetic and clinical images/shapes and they are shown to be robust under various noise levels, occlusions, and missing object information. Vertebral bodies (VBs) in clinical computed tomography (CT) are segmented using the proposed methods to help the bone mineral density measurements and fracture analysis in bones. Experimental results show that the proposed solutions eliminate some of the existing problems in the VB segmentation. One of the most important contributions of this study is to offer a segmentation framework which can be suitable to the clinical works

    Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression

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    [EN] Purpose: The development of automatic and reliable algorithms for the detection and segmentation of the vertebrae are of great importance prior to any diagnostic task. However, an important problem found to accurately segment the vertebrae is the presence of the ribs in the thoracic region. To overcome this problem, a probabilistic atlas of the spine has been developed dealing with the proximity of other structures, with a special focus on ribs suppression. Methods: The data sets used consist of Computed Tomography images corresponding to 21 patients suffering from spinal metastases. Two methods have been combined to obtain the final result: firstly, an initial segmentation is performed using a fully automatic level-set method; secondly, to refine the initial segmentation, a 3D volume indicating the probability of each voxel of belonging to the spine has been developed. In this way, a probability map is generated and deformed to be adapted to each testing case. Results: To validate the improvement obtained after applying the atlas, the Dice coefficient (DSC), the Hausdorff distance (HD), and the mean surface-to-surface distance (MSD) were used. The results showed up an average of 10 mm of improvement accuracy in terms of HD, obtaining an overall final average of 15.51 2.74 mm. Also, a global value of 91.01 3.18% in terms of DSC and a MSD of 0.66 0.25 mm were obtained. The major improvement using the atlas was achieved in the thoracic region, as ribs were almost perfectly suppressed. Conclusion: The study demonstrated that the atlas is able to detect and appropriately eliminate the ribs while improving the segmentation accuracy.The authors thank the financial support of the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under Grants TEC2012-33778 and BFU2015-64380-C2-2-R (D.M.) and DPI2013-4572-R (J.D., E.D.)Ruiz-España, S.; Domingo, J.; Díaz-Parra, A.; Dura, E.; D'ocon-Alcaniz, V.; Arana, E.; Moratal, D. (2017). Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression. Medical Physics. 44(9):4695-4707. https://doi.org/10.1002/mp.12431S46954707449Harris, R. I., & Macnab, I. (1954). STRUCTURAL CHANGES IN THE LUMBAR INTERVERTEBRAL DISCS. The Journal of Bone and Joint Surgery. British volume, 36-B(2), 304-322. doi:10.1302/0301-620x.36b2.304Oliveira, M. F. de, Rotta, J. M., & Botelho, R. V. (2015). Survival analysis in patients with metastatic spinal disease: the influence of surgery, histology, clinical and neurologic status. Arquivos de Neuro-Psiquiatria, 73(4), 330-335. doi:10.1590/0004-282x20150003Chou, R. (2011). Diagnostic Imaging for Low Back Pain: Advice for High-Value Health Care From the American College of Physicians. Annals of Internal Medicine, 154(3), 181. doi:10.7326/0003-4819-154-3-201102010-00008Brayda-Bruno, M., Tibiletti, M., Ito, K., Fairbank, J., Galbusera, F., Zerbi, A., … Sivan, S. S. (2013). Advances in the diagnosis of degenerated lumbar discs and their possible clinical application. European Spine Journal, 23(S3), 315-323. doi:10.1007/s00586-013-2960-9Quattrocchi, C. C., Santini, D., Dell’Aia, P., Piciucchi, S., Leoncini, E., Vincenzi, B., … Zobel, B. B. (2007). A prospective analysis of CT density measurements of bone metastases after treatment with zoledronic acid. Skeletal Radiology, 36(12), 1121-1127. doi:10.1007/s00256-007-0388-1Doi, K. (2007). Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 31(4-5), 198-211. doi:10.1016/j.compmedimag.2007.02.002Ruiz-España, S., Arana, E., & Moratal, D. (2015). Semiautomatic computer-aided classification of degenerative lumbar spine disease in magnetic resonance imaging. Computers in Biology and Medicine, 62, 196-205. doi:10.1016/j.compbiomed.2015.04.028Alomari, R. S., Ghosh, S., Koh, J., & Chaudhary, V. (2014). Vertebral Column Localization, Labeling, and Segmentation. Lecture Notes in Computational Vision and Biomechanics, 193-229. doi:10.1007/978-3-319-12508-4_7Hamarneh, G., & Li, X. (2009). Watershed segmentation using prior shape and appearance knowledge. Image and Vision Computing, 27(1-2), 59-68. doi:10.1016/j.imavis.2006.10.009Ghebreab, S., & Smeulders, A. W. (2004). Combining Strings and Necklaces for Interactive Three-Dimensional Segmentation of Spinal Images Using an Integral Deformable Spine Model. IEEE Transactions on Biomedical Engineering, 51(10), 1821-1829. doi:10.1109/tbme.2004.831540Mastmeyer, A., Engelke, K., Fuchs, C., & Kalender, W. A. (2006). A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine. Medical Image Analysis, 10(4), 560-577. doi:10.1016/j.media.2006.05.005Rasoulian, A., Rohling, R., & Abolmaesumi, P. (2013). Lumbar Spine Segmentation Using a Statistical Multi-Vertebrae Anatomical Shape+Pose Model. IEEE Transactions on Medical Imaging, 32(10), 1890-1900. doi:10.1109/tmi.2013.2268424Ma, J., & Lu, L. (2013). Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model. Computer Vision and Image Understanding, 117(9), 1072-1083. doi:10.1016/j.cviu.2012.11.016Kim, Y., & Kim, D. (2009). A fully automatic vertebra segmentation method using 3D deformable fences. Computerized Medical Imaging and Graphics, 33(5), 343-352. doi:10.1016/j.compmedimag.2009.02.006Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., & Lorenz, C. (2009). Automated model-based vertebra detection, identification, and segmentation in CT images. Medical Image Analysis, 13(3), 471-482. doi:10.1016/j.media.2009.02.004Štern, D., Likar, B., Pernuš, F., & Vrtovec, T. (2011). Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images. Physics in Medicine and Biology, 56(23), 7505-7522. doi:10.1088/0031-9155/56/23/011Korez, R., Ibragimov, B., Likar, B., Pernus, F., & Vrtovec, T. (2015). A Framework for Automated Spine and Vertebrae Interpolation-Based Detection and Model-Based Segmentation. IEEE Transactions on Medical Imaging, 34(8), 1649-1662. doi:10.1109/tmi.2015.2389334Castro-Mateos, I., Pozo, J. M., Pereanez, M., Lekadir, K., Lazary, A., & Frangi, A. F. (2015). Statistical Interspace Models (SIMs): Application to Robust 3D Spine Segmentation. IEEE Transactions on Medical Imaging, 34(8), 1663-1675. doi:10.1109/tmi.2015.2443912Pereanez, M., Lekadir, K., Castro-Mateos, I., Pozo, J. M., Lazary, A., & Frangi, A. F. (2015). Accurate Segmentation of Vertebral Bodies and Processes Using Statistical Shape Decomposition and Conditional Models. IEEE Transactions on Medical Imaging, 34(8), 1627-1639. doi:10.1109/tmi.2015.2396774Michael Kelm, B., Wels, M., Kevin Zhou, S., Seifert, S., Suehling, M., Zheng, Y., & Comaniciu, D. (2013). Spine detection in CT and MR using iterated marginal space learning. Medical Image Analysis, 17(8), 1283-1292. doi:10.1016/j.media.2012.09.007Yan Kang, Engelke, K., & Kalender, W. A. (2003). A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data. IEEE Transactions on Medical Imaging, 22(5), 586-598. doi:10.1109/tmi.2003.812265Huang, J., Jian, F., Wu, H., & Li, H. (2013). An improved level set method for vertebra CT image segmentation. BioMedical Engineering OnLine, 12(1), 48. doi:10.1186/1475-925x-12-48Lim, P. H., Bagci, U., & Bai, L. (2013). Introducing Willmore Flow Into Level Set Segmentation of Spinal Vertebrae. IEEE Transactions on Biomedical Engineering, 60(1), 115-122. doi:10.1109/tbme.2012.2225833Forsberg, D., Lundström, C., Andersson, M., & Knutsson, H. (2013). Model-based registration for assessment of spinal deformities in idiopathic scoliosis. Physics in Medicine and Biology, 59(2), 311-326. doi:10.1088/0031-9155/59/2/311Yao, J., Burns, J. E., Forsberg, D., Seitel, A., Rasoulian, A., Abolmaesumi, P., … Li, S. (2016). A multi-center milestone study of clinical vertebral CT segmentation. Computerized Medical Imaging and Graphics, 49, 16-28. doi:10.1016/j.compmedimag.2015.12.006Shi, C., Wang, J., & Cheng, Y. (2015). Sparse Representation-Based Deformation Model for Atlas-Based Segmentation of Liver CT Images. Image and Graphics, 410-419. doi:10.1007/978-3-319-21969-1_36Domingo, J., Dura, E., Ayala, G., & Ruiz-España, S. (2015). Means of 2D and 3D Shapes and Their Application in Anatomical Atlas Building. Lecture Notes in Computer Science, 522-533. doi:10.1007/978-3-319-23192-1_44Hyunjin Park, Bland, P. H., & Meyer, C. R. (2003). Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Transactions on Medical Imaging, 22(4), 483-492. doi:10.1109/tmi.2003.809139Cabezas, M., Oliver, A., Lladó, X., Freixenet, J., & Bach Cuadra, M. (2011). A review of atlas-based segmentation for magnetic resonance brain images. Computer Methods and Programs in Biomedicine, 104(3), e158-e177. doi:10.1016/j.cmpb.2011.07.015Fortunati, V., Verhaart, R. F., van der Lijn, F., Niessen, W. J., Veenland, J. F., Paulides, M. M., & van Walsum, T. (2013). Tissue segmentation of head and neck CT images for treatment planning: A multiatlas approach combined with intensity modeling. Medical Physics, 40(7), 071905. doi:10.1118/1.4810971Zhuang, X., Bai, W., Song, J., Zhan, S., Qian, X., Shi, W., … Rueckert, D. (2015). Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection. Medical Physics, 42(7), 3822-3833. doi:10.1118/1.4921366Zhou, J., Yan, Z., Lasio, G., Huang, J., Zhang, B., Sharma, N., … D’Souza, W. (2015). Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT. Computerized Medical Imaging and Graphics, 46, 47-55. doi:10.1016/j.compmedimag.2015.07.003Linguraru, M. G., Sandberg, J. K., Li, Z., Shah, F., & Summers, R. M. (2010). Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation. Medical Physics, 37(2), 771-783. doi:10.1118/1.3284530Xu, Y., Xu, C., Kuang, X., Wang, H., Chang, E. I.-C., Huang, W., & Fan, Y. (2016). 3D-SIFT-Flow for atlas-based CT liver image segmentation. Medical Physics, 43(5), 2229-2241. doi:10.1118/1.4945021Michopoulou, S. K., Costaridou, L., Panagiotopoulos, E., Speller, R., Panayiotakis, G., & Todd-Pokropek, A. (2009). Atlas-Based Segmentation of Degenerated Lumbar Intervertebral Discs From MR Images of the Spine. IEEE Transactions on Biomedical Engineering, 56(9), 2225-2231. doi:10.1109/tbme.2009.2019765Taso, M., Le Troter, A., Sdika, M., Ranjeva, J.-P., Guye, M., Bernard, M., & Callot, V. (2013). Construction of an in vivo human spinal cord atlas based on high-resolution MR images at cervical and thoracic levels: preliminary results. Magnetic Resonance Materials in Physics, Biology and Medicine, 27(3), 257-267. doi:10.1007/s10334-013-0403-6Lévy, S., Benhamou, M., Naaman, C., Rainville, P., Callot, V., & Cohen-Adad, J. (2015). White matter atlas of the human spinal cord with estimation of partial volume effect. NeuroImage, 119, 262-271. doi:10.1016/j.neuroimage.2015.06.040Hardisty, M., Gordon, L., Agarwal, P., Skrinskas, T., & Whyne, C. (2007). Quantitative characterization of metastatic disease in the spine. Part I. Semiautomated segmentation using atlas-based deformable registration and the level set method. Medical Physics, 34(8), 3127-3134. doi:10.1118/1.2746498Forsberg, D. (2015). Atlas-Based Registration for Accurate Segmentation of Thoracic and Lumbar Vertebrae in CT Data. Lecture Notes in Computational Vision and Biomechanics, 49-59. doi:10.1007/978-3-319-14148-0_5Ibañez MV Schroeder W Cates L Insight software Consortium. The ITK Software Guide 2016 http://www.itk.org/ItkSoftwareGuide.pdfLoader C R package: Local regression, likelihood and density estimation. CRAN repository 2013 2016 http://cran.r-project.org/web/packages/locfitPARK, H., HERO, A., BLAND, P., KESSLER, M., SEO, J., & MEYER, C. (2010). Construction of Abdominal Probabilistic Atlases and Their Value in Segmentation of Normal Organs in Abdominal CT Scans. IEICE Transactions on Information and Systems, E93-D(8), 2291-2301. doi:10.1587/transinf.e93.d.2291Pohl, K. M., Fisher, J., Bouix, S., Shenton, M., McCarley, R. W., Grimson, W. E. L., … Wells, W. M. (2007). Using the logarithm of odds to define a vector space on probabilistic atlases. Medical Image Analysis, 11(5), 465-477. doi:10.1016/j.media.2007.06.003Baddeley, A., & Molchanov, I. (1998). Journal of Mathematical Imaging and Vision, 8(1), 79-92. doi:10.1023/a:1008214317492De Bruijne, M., van Ginneken, B., Viergever, M. A., & Niessen, W. J. (2003). Adapting Active Shape Models for 3D Segmentation of Tubular Structures in Medical Images. Information Processing in Medical Imaging, 136-147. doi:10.1007/978-3-540-45087-0_12Zhang, K., Zhang, L., Song, H., & Zhou, W. (2010). Active contours with selective local or global segmentation: A new formulation and level set method. Image and Vision Computing, 28(4), 668-676. doi:10.1016/j.imavis.2009.10.009Kalpathy-Cramer, J., Awan, M., Bedrick, S., Rasch, C. R. N., Rosenthal, D. I., & Fuller, C. D. (2013). Development of a Software for Quantitative Evaluation Radiotherapy Target and Organ-at-Risk Segmentation Comparison. Journal of Digital Imaging, 27(1), 108-119. doi:10.1007/s10278-013-9633-4Huttenlocher, D. P., Klanderman, G. A., & Rucklidge, W. J. (1993). Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9), 850-863. doi:10.1109/34.232073Aspert, N., Santa-Cruz, D., & Ebrahimi, T. (s. f.). MESH: measuring errors between surfaces using the Hausdorff distance. Proceedings. IEEE International Conference on Multimedia and Expo. doi:10.1109/icme.2002.103587

    Automatic Localization and Identification of Vertebrae in Arbitrary Field-of-View CT Scans

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    Abstract. This paper presents a new method for automatic localiza-tion and identification of vertebrae in arbitrary field-of-view CT scans. No assumptions are made about which section of the spine is visible or to which extent. Thus, our approach is more general than previous work while being computationally efficient. Our algorithm is based on re-gression forests and probabilistic graphical models. The discriminative, regression part aims at roughly detecting the visible part of the spine. Ac-curate localization and identification of individual vertebrae is achieved through a generative model capturing spinal shape and appearance. The system is evaluated quantitatively on 200 CT scans, the largest dataset reported for this purpose. We obtain an overall median localization error of less than 6mm, with an identification rate of 81%.

    CAD-Based Porous Scaffold Design of Intervertebral Discs in Tissue Engineering

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    With the development and maturity of three-dimensional (3D) printing technology over the past decade, 3D printing has been widely investigated and applied in the field of tissue engineering to repair damaged tissues or organs, such as muscles, skin, and bones, Although a number of automated fabrication methods have been developed to create superior bio-scaffolds with specific surface properties and porosity, the major challenges still focus on how to fabricate 3D natural biodegradable scaffolds that have tailor properties such as intricate architecture, porosity, and interconnectivity in order to provide the needed structural integrity, strength, transport, and ideal microenvironment for cell- and tissue-growth. In this dissertation, a robust pipeline of fabricating bio-functional porous scaffolds of intervertebral discs based on different innovative porous design methodologies is illustrated. Firstly, a triply periodic minimal surface (TPMS) based parameterization method, which has overcome the integrity problem of traditional TPMS method, is presented in Chapter 3. Then, an implicit surface modeling (ISM) approach using tetrahedral implicit surface (TIS) is demonstrated and compared with the TPMS method in Chapter 4. In Chapter 5, we present an advanced porous design method with higher flexibility using anisotropic radial basis function (ARBF) and volumetric meshes. Based on all these advanced porous design methods, the 3D model of a bio-functional porous intervertebral disc scaffold can be easily designed and its physical model can also be manufactured through 3D printing. However, due to the unique shape of each intervertebral disc and the intricate topological relationship between the intervertebral discs and the spine, the accurate localization and segmentation of dysfunctional discs are regarded as another obstacle to fabricating porous 3D disc models. To that end, we discuss in Chapter 6 a segmentation technique of intervertebral discs from CT-scanned medical images by using deep convolutional neural networks. Additionally, some examples of applying different porous designs on the segmented intervertebral disc models are demonstrated in Chapter 6

    Automatic Segmentation of the Lumbar Spine from Medical Images

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    Segmentation of the lumbar spine in 3D is a necessary step in numerous medical applications, but remains a challenging problem for computational methods due to the complex and varied shape of the anatomy and the noise and other artefacts often present in the images. While manual annotation of anatomical objects such as vertebrae is often carried out with the aid of specialised software, obtaining even a single example can be extremely time-consuming. Automating the segmentation process is the only feasible way to obtain accurate and reliable segmentations on any large scale. This thesis describes an approach for automatic segmentation of the lumbar spine from medical images; specifically those acquired using magnetic resonance imaging (MRI) and computed tomography (CT). The segmentation problem is formulated as one of assigning class labels to local clustered regions of an image (called superpixels in 2D or supervoxels in 3D). Features are introduced in 2D and 3D which can be used to train a classifier for estimating the class labels of the superpixels or supervoxels. Spatial context is introduced by incorporating the class estimates into a conditional random field along with a learned pairwise metric. Inference over the resulting model can be carried out very efficiently, enabling an accurate pixel- or voxel-level segmentation to be recovered from the labelled regions. In contrast to most previous work in the literature, the approach does not rely on explicit prior shape information. It therefore avoids many of the problems associated with these methods, such as the need to construct a representative prior model of anatomical shape from training data and the approximate nature of the optimisation. The general-purpose nature of the proposed method means that it can be used to accurately segment both vertebrae and intervertebral discs from medical images without fundamental change to the model. Evaluation of the approach shows it to obtain accurate and robust performance in the presence of significant anatomical variation. The median average symmetric surface distances for 2D vertebra segmentation were 0.27mm on MRI data and 0.02mm on CT data. For 3D vertebra segmentation the median surface distances were 0.90mm on MRI data and 0.20mm on CT data. For 3D intervertebral disc segmentation a median surface distance of 0.54mm was obtained on MRI data
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