A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection

[EN] Edge detection in medical imaging is a significant task for object recognition of human organs and is considered a pre-processing step in medical image segmentation and reconstruction. This article proposes an efficient approach based on generalized Hill entropy to find a good solution for detecting edges under noisy conditions in medical images. The proposed algorithm uses a two-phase thresholding: firstly, a global threshold calculated by means of generalized Hill entropy is used to separate the image into object and background. Afterwards, a local threshold value is determined for each part of the image. The final edge map image is a combination of these two separate images based on the three calculated thresholds. The performance of the proposed algorithm is compared to Canny and Tsallis entropy using sets of medical images corrupted by various types of noise. We used Pratt's Figure Of Merit (PFOM) as a quantitative measure for an objective comparison. Experimental results indicated that the proposed algorithm displayed superior noise resilience and better edge detection than Canny and Tsallis entropy methods for the four different types of noise analyzed, and thus it can be considered as a very interesting edge detection algorithm on noisy medical images. (c) 2017 Sharif University of Technology. All rights reserved.This work was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and by FEDER funds under Grant BFU2015-64380-C2-2-R.Elaraby, A.; Moratal, D. (2017). A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection. Scientia Iranica. 24(6):3247-3256. https://doi.org/10.24200/sci.2017.43593247325624

OncoSpineSeg: A Software Tool for a Manual Segmentation of Computed Tomography of the Spine on Cancer Patients

The organ most commonly affected by metastatic cancer is the skeleton, and spine is the site where it causes the highest morbidity. Computer-aided diagnosis (CAD) for detecting and assessing metastatic disease in bone or other spine disorders can assist physicians to perform their decision-making tasks. A precise segmentation of the spine is important as a first stage in any automatic diagnosis task. However, it is a challenging problem to segment correctly an affected spine, and it is a crucial step to assess quantitatively the results of segmentation by comparing them with the results of a manual segmentation, reviewed by one experienced radiologist. This chapter presents the design of a MATLAB-based software for the manual segmentation of the spine. The software tool has a simple and easy to use interface, and it works with either computed tomography or magnetic resonance imaging (MRI). A typical workflow includes loading the image volume, creating multi-planar reconstructions, manually contouring the vertebrae, spinal lesions, intervertebral discs and spinal canal with availability of different segmentation tools, classification of the bone into healthy bone, osteolytic metastases, osteoblastic metastases or mixed lesions, being also possible to classify an object as a false-positive and a 3D reconstruction of the segmented objects

Texture Analysis in Magnetic Resonance Imaging: Review and Considerations for Future Applications

Texture analysis is a technique used for the quantification of image texture. It has been successfully used in many fields, and in the past years it has been applied in magnetic resonance imaging (MRI) as a computer-aided diagnostic tool. Quantification of the intrinsic heterogeneity of different tissues and lesions is necessary as they are usually imperceptible to the human eye. In the present chapter, we describe texture analysis as a process consisting of six steps: MRI acquisition, region of interest (ROI) definition, ROI preprocessing, feature extraction, feature selection, and classification. There is a great variety of methods and techniques to be chosen at each step and all of them can somehow affect the outcome of the texture analysis application. We reviewed the literature regarding texture analysis in clinical MRI focusing on the important considerations to be taken at each step of the process in order to obtain maximum benefits and to avoid misleading results

Retos COVID-19

El 15 de marzo del 2020 marcó un antes y un después en nuestras vidas con el coronavirus como gran protagonista a nivel internacional. Nos acostamos un domingo por la noche, después de haber disfrutado del fin de semana al aire libre, y amanecimos encerrados en nuestras casas durante un periodo de tres meses. La población andaba perdida y las fake news se expandían a mayor velocidad que la transmisión del virus. Este libro presenta un total de cincuenta y dos artículos que, recogidos a lo largo de un año de pandemia, ofrecen al lector un dibujo del panorama vivido y de las inquietudes por las que hemos pasado. Desde las primeras medidas de protección individual (mascarilla sí, mascarilla no; cierre total sí versus cierre parcial mejor) a los primeros datos sobre la sintomatología de los enfermos o los tratamientos que se empezaron a aplicar sin conocer exactamente sus bondades. Investigaciones sobre el origen del virus, las promesas de que las vacunas iban a estar listas en breve¿ tantos interrogantes planteados a lo largo de unos meses que han ido pasando, situándonos en una rápida desescalada y, casi sin darnos cuenta, en la segunda ola después del verano.Poco después llegó Navidad y, tras ella, sin apenas margen temporal, la tercera ola, la cuarta y la quinta al inicio del verano, cuando gran parte de la población estaba vacunada y pensábamos que el verano era nuestro. Pero después de estos contagios sucesivos se nos plantean muchas dudas distintas. Todos estos temas son los que hemos plasmado en Retos COVID-19 y que inspiran este libro, siendo la vía diseñada para canalizar con rigor la correcta información a través de webinars, una página específica dentro de la web de la Fundación QUAES y newsletters semanalesMoratal Pérez, D. (2022). Retos COVID-19. Editorial Universitat Politècnica de València. https://doi.org/10.4995/2022.669901EDITORIA

Link-Level Functional Connectivity Neuroalterations in Autism Spectrum Disorder: A Developmental Resting-State fMRI Study

[EN] Autism spectrum disorder (ASD) is a neurological and developmental disorder whose late diagnosis is based on subjective tests. In seeking for earlier diagnosis, we aimed to find objective biomarkers via analysis of resting-state functional MRI (rs-fMRI) images obtained from the Autism Brain Image Data Exchange (ABIDE) database. Thus, we estimated brain functional connectivity (FC) between pairs of regions as the statistical dependence between their neural-related blood-oxygen-level-dependent (BOLD) signals. We compared FC of individuals with ASD and healthy controls, matched by age and intelligence quotient (IQ), and split into three age groups (50 children, 98 adolescents, and 32 adults), from a developmental perspective. After estimating the correlation, we observed hypoconnectivities in children and adolescents with ASD between regions belonging to the default mode network (DMN). Concretely, in children, FC decreased between the left middle temporal gyrus and right frontal pole (p = 0.0080), and between the left orbitofrontal cortex and right superior frontal gyrus (p = 0.0144). In adolescents, this decrease was observed between bilateral postcentral gyri (p = 0.0012), and between the right precuneus and right middle temporal gyrus (p = 0.0236). These results help to gain a better understanding of the involved regions on autism and its connection with the affected superior cognitive brain functions.This research was partially funded by the Ministerio de Economia y Competitividad (MINECO), through the project BFU2015-64380-C2-2-R. U.P.-R. is funded by the Spanish Ministerio de Educacion, Cultura y Deporte (MECD) under grant FPU13/03537. We are thankful to the initiative ABIDE to provide the huge public release and open sharing autism database what has made possible to carry out this study in better conditions and improve the results obtained significantly.Borràs-Ferrís, L.; Pérez-Ramírez, MÚ.; Moratal, D. (2019). Link-Level Functional Connectivity Neuroalterations in Autism Spectrum Disorder: A Developmental Resting-State fMRI Study. Diagnostics. 9(1):1-10. https://doi.org/10.3390/diagnostics9010032S11091Hull, J. V., Dokovna, L. B., Jacokes, Z. J., Torgerson, C. M., Irimia, A., & Van Horn, J. D. (2017). Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review. Frontiers in Psychiatry, 7. doi:10.3389/fpsyt.2016.00205Mertz, L. (2017). Sharing Data to Solve the Autism Riddle: An Interview with Adriana Di Martino and Michael Milham of ABIDE. IEEE Pulse, 8(6), 6-9. doi:10.1109/mpul.2017.2750819Cociu, B. A., Das, S., Billeci, L., Jamal, W., Maharatna, K., Calderoni, S., … Muratori, F. (2018). Multimodal Functional and Structural Brain Connectivity Analysis in Autism: A Preliminary Integrated Approach With EEG, fMRI, and DTI. IEEE Transactions on Cognitive and Developmental Systems, 10(2), 213-226. doi:10.1109/tcds.2017.2680408Dekhil, O., Hajjdiab, H., Shalaby, A., Ali, M. T., Ayinde, B., Switala, A., … El-Baz, A. (2018). Using resting state functional MRI to build a personalized autism diagnosis system. PLOS ONE, 13(10), e0206351. doi:10.1371/journal.pone.0206351Rogers, B. P., Morgan, V. L., Newton, A. T., & Gore, J. C. (2007). Assessing functional connectivity in the human brain by fMRI. Magnetic Resonance Imaging, 25(10), 1347-1357. doi:10.1016/j.mri.2007.03.007Cheng, W., Rolls, E. T., Gu, H., Zhang, J., & Feng, J. (2015). Autism: reduced connectivity between cortical areas involved in face expression, theory of mind, and the sense of self. Brain, 138(5), 1382-1393. doi:10.1093/brain/awv051Lynch, C. J., Uddin, L. Q., Supekar, K., Khouzam, A., Phillips, J., & Menon, V. (2013). Default Mode Network in Childhood Autism: Posteromedial Cortex Heterogeneity and Relationship with Social Deficits. Biological Psychiatry, 74(3), 212-219. doi:10.1016/j.biopsych.2012.12.013Uddin, L. Q., Supekar, K., Lynch, C. J., Khouzam, A., Phillips, J., Feinstein, C., … Menon, V. (2013). Salience Network–Based Classification and Prediction of Symptom Severity in Children With Autism. JAMA Psychiatry, 70(8), 869. doi:10.1001/jamapsychiatry.2013.104Di Martino, A., Kelly, C., Grzadzinski, R., Zuo, X.-N., Mennes, M., Mairena, M. A., … Milham, M. P. (2011). Aberrant Striatal Functional Connectivity in Children with Autism. Biological Psychiatry, 69(9), 847-856. doi:10.1016/j.biopsych.2010.10.029Washington, S. D., Gordon, E. M., Brar, J., Warburton, S., Sawyer, A. T., Wolfe, A., … VanMeter, J. W. (2013). Dysmaturation of the default mode network in autism. Human Brain Mapping, 35(4), 1284-1296. doi:10.1002/hbm.22252Assaf, M., Jagannathan, K., Calhoun, V. D., Miller, L., Stevens, M. C., Sahl, R., … Pearlson, G. D. (2010). Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients. NeuroImage, 53(1), 247-256. doi:10.1016/j.neuroimage.2010.05.067Weng, S.-J., Wiggins, J. L., Peltier, S. J., Carrasco, M., Risi, S., Lord, C., & Monk, C. S. (2010). Alterations of resting state functional connectivity in the default network in adolescents with autism spectrum disorders. Brain Research, 1313, 202-214. doi:10.1016/j.brainres.2009.11.057Kennedy, D. P., & Courchesne, E. (2008). The intrinsic functional organization of the brain is altered in autism. NeuroImage, 39(4), 1877-1885. doi:10.1016/j.neuroimage.2007.10.052Mueller, S., Keeser, D., Samson, A. C., Kirsch, V., Blautzik, J., Grothe, M., … Meindl, T. (2013). Convergent Findings of Altered Functional and Structural Brain Connectivity in Individuals with High Functioning Autism: A Multimodal MRI Study. PLoS ONE, 8(6), e67329. doi:10.1371/journal.pone.0067329Von dem Hagen, E. A. H., Stoyanova, R. S., Baron-Cohen, S., & Calder, A. J. (2012). Reduced functional connectivity within and between ‘social’ resting state networks in autism spectrum conditions. Social Cognitive and Affective Neuroscience, 8(6), 694-701. doi:10.1093/scan/nss053Uddin, L. Q., Supekar, K., & Menon, V. (2013). Reconceptualizing functional brain connectivity in autism from a developmental perspective. Frontiers in Human Neuroscience, 7. doi:10.3389/fnhum.2013.00458Kana, R. K., Uddin, L. Q., Kenet, T., Chugani, D., & Müller, R.-A. (2014). Brain connectivity in autism. Frontiers in Human Neuroscience, 8. doi:10.3389/fnhum.2014.00349Beckmann, C. F., & Smith, S. M. (2004). Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging. IEEE Transactions on Medical Imaging, 23(2), 137-152. doi:10.1109/tmi.2003.822821Cole. (2010). Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Frontiers in Systems Neuroscience. doi:10.3389/fnsys.2010.00008Etzel, J. A., Gazzola, V., & Keysers, C. (2009). An introduction to anatomical ROI-based fMRI classification analysis. Brain Research, 1282, 114-125. doi:10.1016/j.brainres.2009.05.090Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., … Milham, M. P. (2013). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659-667. doi:10.1038/mp.2013.78Cameron, C., Yassine, B., Carlton, C., Francois, C., Alan, E., András, J., … Pierre, B. (2013). The Neuro Bureau Preprocessing Initiative: open sharing of preprocessed neuroimaging data and derivatives. Frontiers in Neuroinformatics, 7. doi:10.3389/conf.fninf.2013.09.00041Xu, T., Yang, Z., Jiang, L., Xing, X.-X., & Zuo, X.-N. (2015). A Connectome Computation System for discovery science of brain. Science Bulletin, 60(1), 86-95. doi:10.1007/s11434-014-0698-3Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., … Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968-980. doi:10.1016/j.neuroimage.2006.01.021Cole, M. W., Yang, G. J., Murray, J. D., Repovš, G., & Anticevic, A. (2016). Functional connectivity change as shared signal dynamics. Journal of Neuroscience Methods, 259, 22-39. doi:10.1016/j.jneumeth.2015.11.011Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., … Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23, S208-S219. doi:10.1016/j.neuroimage.2004.07.051Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012). FSL. NeuroImage, 62(2), 782-790. doi:10.1016/j.neuroimage.2011.09.015Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. NeuroImage, 92, 381-397. doi:10.1016/j.neuroimage.2014.01.060Bhaumik, R., Pradhan, A., Das, S., & Bhaumik, D. K. (2018). Predicting Autism Spectrum Disorder Using Domain-Adaptive Cross-Site Evaluation. Neuroinformatics, 16(2), 197-205. doi:10.1007/s12021-018-9366-0Cavanna, A. E., & Trimble, M. R. (2006). The precuneus: a review of its functional anatomy and behavioural correlates. Brain, 129(3), 564-583. doi:10.1093/brain/awl004Rolls, E. T. (2004). The functions of the orbitofrontal cortex. Brain and Cognition, 55(1), 11-29. doi:10.1016/s0278-2626(03)00277-xBeer, J. S., John, O. P., Scabini, D., & Knight, R. T. (2006). Orbitofrontal Cortex and Social Behavior: Integrating Self-monitoring and Emotion-Cognition Interactions. 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Structural and functional, empirical and modeled connectivity in the cerebral cortex of the rat

[EN] Connectomics data from animal models provide an invaluable opportunity to reveal the complex interplay between structure and function in the mammalian brain. In this work, we investigate the relationship between structural and functional connectivity in the rat brain cortex using a directed anatomical network generated from a carefully curated meta-analysis of published tracing data, along with resting-state functional MRI data obtained from a group of 14 anesthetized Wistar rats. We found a high correspondence between the strength of functional connections, measured as blood oxygen level dependent (BOLD) signal correlations between cortical regions, and the weight of the corresponding anatomical links in the connectome graph (maximum Spearman rank-order correlation rho = 0.48). At the network-level, regions belonging to the same functionally defined community tend to form more mutual weighted connections between each other compared to regions located in different communities. We further found that functional communities in resting-state networks are enriched in densely connected anatomical motifs. Importantly, these higher-order structural subgraphs cannot be explained by lower-order topological properties, suggesting that dense structural patterns support functional associations in the resting brain. Simulations of brain-wide resting-state activity based on neural mass models implemented on the empirical rat anatomical connectome demonstrated high correlation between the simulated and the measured functional connectivity (maximum Pearson correlation rho = 0: 53), further suggesting that the topology of structural connections plays an important role in shaping functional cortical networks.This work was supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under grants BFU2015-64380-C2-1-R (S.C) and BFU2015-64380-C2-2-R (D.M.) and EU Horizon 2020 Program 668863-SyBil-AA grant (S.C.). S.C. acknowledges financial support from the Spanish State Research Agency, through the "Severo Ochoa" Programme for Centres of Excellence in R&D (ref. SEV-2013-0317). A. D.-P., was supported by grant FPU13/01475 from the Spanish Ministerio de Educacion, Cultura y Deporte (MECD). O.S. acknowledges support by the J.S. McDonnell Foundation (#220020387) and the National Institutes of Health (NIH R01 AT009036-01). We are also grateful to Andrea Avena-Koenigsberger and Begona Fernandez for their technical support.Díaz-Parra, A.; Osborn, Z.; Canals Gamoneda, S.; Moratal, D.; Sporns, O. (2017). Structural and functional, empirical and modeled connectivity in the cerebral cortex of the rat. NeuroImage. 159:170-184. https://doi.org/10.1016/j.neuroimage.2017.07.046S17018415

A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma

© 2017 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Brain metastases are occasionally detected before diagnosing their primary site of origin. In these cases, simple visual examination of medical images of the metastases is not enough to identify the primary cancer, so an extensive evaluation is needed. To avoid this procedure, a radiomics approach on magnetic resonance (MR) images of the metastatic lesions is proposed to classify two of the most frequent origins (lung cancer and melanoma). In this study, 50 T1-weighted MR images of brain metastases from 30 patients were analyzed: 27 of lung cancer and 23 of melanoma origin. A total of 43 statistical texture features were extracted from the segmented lesions in 2D and 3D. Five predictive models were evaluated using a nested cross-validation scheme. The best classification results were achieved using 3D texture features for all the models, obtaining an average AUC > 0.9 in all cases and an AUC = 0.947 +/- 0.067 when using the best model (naive Bayes).Research supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under Grant BFU2015-64380-C2-2-R.Ortiz-Ramón, R.; Larroza-Santacruz, A.; Arana Fernandez De Moya, E.; Moratal, D. (2017). A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma. Proceedings Intenational Anual Conference of IEEE Engineering in Medicine and Biology Society. 493-496. https://doi.org/10.1109/EMBC.2017.8036869S49349

Role of Surface Chemistry in Protein Remodeling at the Cell-Material Interface

Background: The cell-material interaction is a complex bi-directional and dynamic process that mimics to a certain extent the natural interactions of cells with the extracellular matrix. Cells tend to adhere and rearrange adsorbed extracellular matrix (ECM) proteins on the material surface in a fibril-like pattern. Afterwards, the ECM undergoes proteolytic degradation, which is a mechanism for the removal of the excess ECM usually approximated with remodeling. ECM remodeling is a dynamic process that consists of two opposite events: assembly and degradation. Methodology/Principal Findings: This work investigates matrix protein dynamics on mixed self-assembled monolayers (SAMs) of –OH and –CH3 terminated alkanethiols. SAMs assembled on gold are highly ordered organic surfaces able to provide different chemical functionalities and well-controlled surface properties. Fibronectin (FN) was adsorbed on the different surfaces and quantified in terms of the adsorbed surface density, distribution and conformation. Initial cell adhesion and signaling on FN-coated SAMs were characterized via the formation of focal adhesions, integrin expression and phosphorylation of FAKs. Afterwards, the reorganization and secretion of FN was assessed. Finally, matrix degradation was followed via the expression of matrix metalloproteinases MMP2 and MMP9 and correlated with Runx2 levels. We show that matrix degradation at the cell material interface depends on surface chemistry in MMP-dependent way. Conclusions/Significance: This work provides a broad overview of matrix remodeling at the cell-material interface, establishing correlations between surface chemistry, FN adsorption, cell adhesion and signaling, matrix reorganization and degradation. The reported findings improve our understanding of the role of surface chemistry as a key parameter in the design of new biomaterials. It demonstrates the ability of surface chemistry to direct proteolytic routes at the cell-material interface, which gains a distinct bioengineering interest as a new tool to trigger matrix degradation in different biomedical applications

RATT: RFID Assisted Tracking Tile. Preliminary results

© 2017 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Behavior is one of the most important aspects of animal life. This behavior depends on the link between animals, their nervous systems and their environment. In order to study the behavior of laboratory animals several tools are needed, but a tracking tool is essential to perform a thorough behavioral study. Currently, several visual tracking tools are available. However, they have some drawbacks. For instance, when an animal is inside a cave, or is close to other animals, the tracking cameras cannot always detect the location or movement of this animal. This paper presents RFID Assisted Tracking Tile (RATT), a tracking system based on passive Radio Frequency Identification (RFID) technology in high frequency band according to ISO/IEC 15693. The RATT system is composed of electronic tiles that have nine active RFID antennas attached; in addition, it contains several overlapping passive coils to improve the magnetic field characteristics. Using several tiles, a large surface can be built on which the animals can move, allowing identification and tracking of their movements. This system, that could also be combined with a visual tracking system, paves the way for complete behavioral studies.Research supported in part by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under grants BFU2015-64380-C2-2-R and BFU2015-64380-C2-1-R. Santiago Canals acknowledges financial support from the Spanish State Research Agency, through the "Severo Ochoa" Programme for Centres of Excellence in R&D (ref. SEV-2013-0317). Dario R. Quinones is supported by grant Ayudas para la formacion de personal investigador (FPI) from Universitat Politecnica de Valencia.Quiñones, DR.; Cuevas-López, A.; Cambra-Enguix J.; Canals-Gamoneda, S.; Moratal, D. (2017). RATT: RFID Assisted Tracking Tile. Preliminary results. Proceedings Intenational Anual Conference of IEEE Engineering in Medicine and Biology Society. 4114-4117. https://doi.org/10.1109/EMBC.2017.8037761S4114411