88 research outputs found

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

    Full text link
    [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

    Quintessence as k-essence

    Get PDF
    Quintessence and k-essence have been proposed as candidates for the dark energy component of the universe that would be responsible of the currently observed accelerated expansion. In this paper we investigate the degree of resemblance between those two theoretical setups, and find that every quintessence model can be viewed as a k-essence model generated by a kinetic linear function. In addition, we show the true effects of k-essence begin at second order in the expansion of the kinetic function in powers of the kinetic energy.Comment: 14 pages, improved discussion, matches published versio

    Dissecting quasars with the J-PAS narrow-band photometric survey

    Get PDF
    Nuclear Activity in Galaxies Across Cosmic Time, Proceedings of the conference held 7-11 October 2019 in Addis Ababa, Ethiopia. Edited by Mirjana Pović et al. Proceedings of the International Astronomical Union, Volume 356, pp. 12-16The J-PAS survey will soon start observing thousands of square degrees of the Northern Sky with its unique set of 56 narrow band filters covering the entire optical wavelength range, providing, effectively, a low resolution spectra for every object detected. Active galaxies and quasars, thanks to their strong emission lines, can be easily identified and characterized with J-PAS data. A variety of studies can be performed, from IFU-like analysis of local AGN, to clustering of high-z quasars. We also expect to be able to extract intrinsic physical quasar properties from the J-PAS pseudo-spectra, including continuum slope and emission line luminosities. Here we show the first attempts of using the QSFit software package to derive the properties for 22 quasars at 0.8 < z < 2 observed by the miniJPAS survey, the first deg2 of J-PAS data obtained with an interim camera. Results are compared with the ones obtained by applying the same software to SDSS quasar spectra.Financial support from the State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa" award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709

    Templating of carbon in zeolites under pressure: synthesis of pelletized zeolite templated carbons with improved porosity and packing density for superior gas (CO2 and H2) uptake properties

    Get PDF
    This report explores the use of compacted zeolite pellets as templates for the preparation of pelletized zeolite templated carbons (ZTCs). The effects of zeolite compaction before use as hard templates were investigated through the compression of powder forms of zeolites at 370 MPa or 740 MPa prior to their use as templates. The resulting carbon samples were compared to compacted conventionally templated (with powdered zeolites) ZTC. The use of compacted zeolite pellets results in pelletized ZTCs that simultaneously exhibit higher porosity and higher packing density, which translates to highly enhanced volumetric gas (CO2 and hydrogen) uptake. For CVD-derived samples, the pelletized ZTCs achieve 10% higher surface area than powdered samples (and reach > 2000 m2 g-1) despite their packing density increasing from ca. 0.55 to 0.85 g cm-3, which means that the surface area per unit volume increases by ca. 60% from between 1000 and 1100 m2 cm-3 for the powdered ZTCs to ca. 1670 m2 cm-3 for pelletized samples. Thus their volumetric CO2 uptake at 25 oC increases by 140% and 85% at 1 and 20 bar, respectively, compared to powdered ZTCs. Pelletized ZTCs prepared via a combination of liquid impregnation and CVD achieve much higher surface area of 3000 m2 g-1 (compared to 2700 m2 g-1 for powder samples) despite an increase in packing density from 0.44 to 0.69 g cm-3, resulting in a surface area per unit volume rise of 75% from 1189 m2 cm-3 to 2085 m2 cm-3. The high surface area pelletized ZTCs have attractive gravimetric hydrogen uptake of 6.6 wt% (5.5 wt% for powdered sample) at 20 bar and -196 oC, and reach volumetric hydrogen storage capacity of 46 g l-1 (24 g l-1 for powder sample). For CO2 uptake at 25 oC and at 20 bar, the volumetric uptake of the high surface area pelletized ZTC is nearly twice that of the powdered sample; 668 g l-1 compared to 360 g l-1

    Electronic structure of porphyrin-based metal-organic frameworks and their suitability for solar fuel production photocatalysis

    Get PDF
    Metal-organic frameworks (MOFs) can be exceptionally good catalytic materials thanks to the presence of active metal centres and a porous structure that is advantageous for molecular adsorption and confinement. We present here a first-principles investigation of the electronic structure of a family of MOFs based on porphyrins connected through phenyl-carboxyl ligands and AlOH species, in order to assess their suitability for the photocatalysis of fuel production reactions using sunlight. We consider structures with protonated porphyrins and those with the protons exchanged with late 3d metal cations (Fe2+, Co2+, Ni2+, Cu2+, Zn2+), a process that we find to be thermodynamically favorable from aqueous solution for all these metals. Our band structure calculations, based on an accurate screened hybrid functional, reveal that the bandgaps are in a favorable range (2.0 to 2.6 eV) for efficient adsorption of solar light. Furthermore, by approximating the vacuum level to the pore center potential, we provide the alignment of the MOFs’ band edges with the redox potentials for water splitting and carbon dioxide reduction, and show that the structures studied here have band edges positions suitable for these reactions at neutral pH

    Zirconium-containing metal organic frameworks as solid acid catalysts for the esterification of free fatty acids: Synthesis of biodiesel and other compounds of interest

    Full text link
    Zr-containing metal organic frameworks (MOFs) formed by terephthalate (UiO-66) and 2-aminoterephthalate ligands (UiO-66-NH2) are active and stable catalysts for the acid catalyzed esterification of various saturated and unsaturated fatty acids with MeOH and EtOH, with activities comparable (in some cases superior) to other solid acid catalysts previously reported in literature. Besides the formation of the corresponding fatty acid alkyl esters as biodiesel compounds (FAMEs and FAEEs), esterification of biomass-derived fatty acids with other alcohols catalyzed by the Zr-MOFs allows preparing other compounds of interest, such as oleyl oleate or isopropyl palmitate, with good yields under mild conditions.Financial support from the Consolider-Ingenio 2010 (project MULTICAT), the Severo Ochoa program, and the Spanish Ministry of Science and Innovation (project MAT2011-29020-C02-01) is gratefully acknowledged.García Cirujano, F.; Corma Canós, A.; Llabrés I Xamena, FX. (2015). Zirconium-containing metal organic frameworks as solid acid catalysts for the esterification of free fatty acids: Synthesis of biodiesel and other compounds of interest. Catalysis Today. 257:213-220. https://doi.org/10.1016/j.cattod.2014.08.015S21322025

    Co@NH 2

    Get PDF
    We present a synthetic strategy for the efficient encapsulation of a deriv. of a well-​defined cobaloxime proton redn. catalyst within a photoresponsive metal-​org. framework (NH2- MIL-​125(Ti)​)​. The resulting hybrid system Co@MOF is demonstrated to be a robust heterogeneous composite material. Furthermore, Co@MOF is an efficient and fully recyclable noble metal-​free catalyst system for light-​driven hydrogen evolution from water under visible light illumination

    ChemInform Abstract: MOFs as Nano-Reactors

    No full text
    • …
    corecore