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

[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

Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs

An important factor for the development of spinal degeneration, pain and the outcome of spinal surgery is known to be the balance of the spine. It must be analyzed in an upright, standing position to ensure physiological loading conditions and visualize load-dependent deformations. Despite the complex 3D shape of the spine, this analysis is currently performed using 2D radiographs, as all frequently used 3D imaging techniques require the patient to be scanned in a prone position. To overcome this limitation, we propose a deep neural network to reconstruct the 3D spinal pose in an upright standing position, loaded naturally. Specifically, we propose a novel neural network architecture, which takes orthogonal 2D radiographs and infers the spine’s 3D posture using vertebral shape priors. In this work, we define vertebral shape priors using an atlas and a spine shape prior, incorporating both into our proposed network architecture. We validate our architecture on digitally reconstructed radiographs, achieving a 3D reconstruction Dice of 0.95, indicating an almost perfect 2D-to-3D domain translation. Validating the reconstruction accuracy of a 3D standing spine on real data is infeasible due to the lack of a valid ground truth. Hence, we design a novel experiment for this purpose, using an orientation invariant distance metric, to evaluate our model’s ability to synthesize full-3D, upright, and patient-specific spine models. We compare the synthesized spine shapes from clinical upright standing radiographs to the same patient’s 3D spinal posture in the prone position from CT

CARACTERIZACIÓN CUANTITATIVA DE LA PATOLOGÍA DISCAL Y LUMBAR DEGENERATIVA MEDIANTE ANÁLISIS DE IMAGEN POR RESONANCIA MAGNÉTICA Y DETECCIÓN Y SEGMENTACIÓN DE LA COLUMNA VERTEBRAL EN PACIENTES ONCOLÓGICOS A PARTIR DEL ANÁLISIS DE IMAGEN EN TOMOGRAFÍA COMPUTARIZADA

[EN] Over the last 20 years health system has been revolutionized by imaging technology so diagnostic imaging has become the mainstay of the management of patients. Nowadays, degeneration of the intervertebral discs, herniation and spinal stenosis are very common entities that affect millions of people and cause back pain. The development of computer-aided diagnosis (CAD) methods for classifying and quantifying these pathologies has increased in the past decade as a way to assist radiologists in the diagnosis task. So, the main objective of the first part of this Doctoral Thesis is the development of a CAD software for the classification and quantification of spine disease by means of Magnetic Resonance image analysis. To this end, two different groups of patients have been used, one as training group (14 patients) and the other as testing group (53 patients). To classify disc degeneration according to the gold standard, Pfirrmann classification, a method mainly based on the measurement of disc signal intensity and structure has been developed. The method developed to detect disc herniations has been focused on disc segmentation and its approximation by an ellipse, in this way it is possible to extract disc shape features for detecting contour abnormalities. The method developed to detect spinal stenosis, based on signal intensity, has been developed to extract the spinal canal and, by applying different techniques, to detect spinal stenosis at every intervertebral disc level and quantify the severity of the pathology. The results have shown a segmentation inaccuracy below 1%. Regarding reproducibility, it has been obtained an almost perfect agreement (measured by the k and ICC statistics) for all the analysed pathologies. The results have shown that the developed methods can assist radiologists to perform their decision-making tasks, providing support for enhanced reproducibility of MRI reports and achieving greater objectivity. However, not only the intervertebral discs are susceptible to suffer several pathologies. The vertebral bodies are also subject to a wide variety of diseases because of different circumstances. So, prior to any diagnosis task, an accurate detection and segmentation of the vertebral bodies are the first crucial steps. Therefore, the main objective of the second part of this Doctoral Thesis is the development of an automatic method for the detection and segmentation of the spine in Computed Tomography imaging. Performing an automatic and robust segmentation is a very challenging task due to the difficulty discriminating between the ribs and the vertebral bodies. To overcome this problem, two different segmentation methods have been combined: the first method uses a Level-Set method to perform an initial segmentation; the second method uses a probabilistic atlas to refine the initial segmentation with a special focus on ribs suppression. So a 3D volume indicating the probability of each voxel of belonging to the spine has been developed, by means of a set of images, corresponding to 14 patients (training group), manually segmented by an expert. The generated probability map has been deformed and adapted to each testing case. To evaluate the segmentation results and the improvement obtained after applying the atlas to the initial segmentation, the Dice similarity coefficient (DSC) and the Hausdorff distance (HD) have been used. The results have shown up an average of 11 mm of improvement in segmentation accuracy in terms of HD, obtaining an overall final average of 14,98 ± 1,32 mm. A refinement of 1,3 % has been obtained in terms of DSC, with a global value of 91,75 ± 1,20 %. The study has demonstrated that the atlas is able to detect and appropriately eliminate the ribs while improving the segmentation accuracy.[ES] En los últimos 20 años el sistema sanitario se ha visto revolucionado por la tecnología de la imagen, por lo que el diagnóstico por imagen se ha convertido en un pilar fundamental en el manejo de los pacientes. Hoy en día la degeneración de los discos intervertebrales, la hernia discal y la estenosis del canal vertebral, son tres patologías que afectan a millones de personas y causan dolor de espalda. El desarrollo de sistemas CAD para clasificar y cuantificar estas patologías se ha incrementado en la última década como una forma de ayuda al radiólogo en el diagnóstico. Por tanto, la primera parte de esta Tesis Doctoral tiene como objetivo el desarrollo de un sistema CAD para la clasificación y cuantificación de la patología discal por medio del análisis de Imagen por Resonancia Magnética. Con este fin se han utilizado dos grupos de pacientes, uno como grupo de entrenamiento (14 pacientes) y el otro como grupo de prueba (53 pacientes). Para la clasificación de la degeneración discal se ha desarrollado un método basado en el cálculo de la estructura del disco y de su señal de intensidad. El método de detección de herniaciones se ha centrado en la segmentación del disco y su aproximación por una elipse, para extraer así información sobre la forma del disco. El método de detección de estenosis, basado en la señal de intensidad, ha sido desarrollado para extraer el canal vertebral y, con la aplicación de diferentes técnicas, detectar estrechamientos a la altura de los discos y cuantificar la gravedad de los mismos. Los resultados han demostrado una alta precisión en la segmentación, con un error inferior al 1 %. En cuanto a la reproducibilidad, se ha obtenido un acuerdo casi perfecto (medido con los coeficientes CCI y k) para todas las patologías analizadas. Los resultados obtenidos demuestran que los métodos desarrollados pueden servir de ayuda al radiólogo en el diagnóstico, mejorando la reproducibilidad y logrando una mayor objetividad. Sin embargo, no sólo los discos intervertebrales son susceptibles de sufrir alguna patología. Los cuerpos vertebrales también pueden sufrir lesiones por diversas circunstancias. No obstante, antes de realizar cualquier tarea de diagnóstico, llevar a cabo una detección y segmentación precisa de los cuerpos vertebrales es un primer paso crucial. Así pues, la segunda parte de esta Tesis Doctoral tiene como objetivo el desarrollo de un método automático para la detección y segmentación de la columna vertebral por medio del análisis de Tomografía Computarizada. Llevar a cabo una segmentación automática y precisa es una tarea complicada debido principalmente a la gran dificultad para distinguir entre los cuerpos vertebrales y las costillas. Para solucionar este problema se han combinado dos métodos de segmentación diferentes: el primero utiliza un método Level-Set para llevar a cabo una segmentación inicial; el segundo utiliza un atlas probabilístico, para refinar la segmentación inicial, con un enfoque especial en la supresión de las costillas. Por tanto, se ha obtenido un volumen 3D indicando la probabilidad de cada voxel de pertenecer o no a la columna vertebral, por medio de un conjunto de imágenes correspondientes a 14 pacientes segmentadas manualmente por un experto. El mapa de probabilidad generado ha sido deformado y adaptado a cada uno de los 7 pacientes del grupo de prueba. Para evaluar los resultados de la segmentación y la mejora obtenida después de aplicar el atlas a la segmentación inicial, se ha utilizado el coeficiente Dice (DSC) y la distancia Hausdorff (HD). Los resultados han demostrado una mejora en la precisión de la segmentación de 11 mm de media en términos de HD, con una media global de 14,98 ± 1,32 mm. En términos de DSC se ha obtenido una mejora de un 1,3 % , con una media global de 91,75 ± 1,20 %. El estudio ha demostrado que el atlas es capaz de detectar y eliminar apropiadamente las estructuras costales[CA] En els últims 20 anys el sistema sanitari s'ha vist revolucionat per la tecnologia de la imatge, per la qual cosa el diagnòstic per imatge s'ha convertit en un pilar fonamental en el maneig dels pacients. Hui en dia la degeneració dels discos intervertebrals, l'hèrnia discal i l'estenosi del canal vertebral, són tres patologies molt comunes que afecten milions de persones i causen dolor d'esquena. El desenvolupament de sistemes CAD per a classificar i quantificar estes patologies s'ha incrementat en l'última dècada com una forma d'ajuda al radiòleg en el diagnòstic. Per tant, la primera part d'aquesta Tesi Doctoral té com a objectiu el desenvolupament d'un sistema CAD per a la classificació i quantificació de la patologia discal per mitjà de l'anàlisi d'Imatge per Ressonància Magnètica. Amb aquest fi s'han utilitzat dos grups de pacients distints, un com a grup d'entrenament (14 pacients) i l'altre com a grup de prova (53 pacients). Per a la classificació de la degeneració discal, s'ha desenvolupat un mètode basat en el càlcul de l'estructura del disc i del seu senyal d'intensitat. El mètode de detecció d'herniacions s'ha centrat en la segmentació del disc i la seua aproximació per una el·lipse, per a extraure així informació sobre la forma del disc. El mètode de detecció d'estenosi, basat en el senyal d'intensitat, ha sigut desenvolupat per a extraure el canal vertebral i amb l'aplicació de diferents tècniques detectar estrenyiments a l'altura dels discos i quantificar la gravetat dels mateixos. Els resultats han demostrat una alta precisió en la segmentació, amb un error inferior a l'1 %. En quant a la reproduïbilitat, s'ha obtingut un acord quasi perfecte (mesurat amb els coeficients CCI i k) per a totes les patologies analitzades. Els resultats obtinguts demostren que els mètodes desenvolupats poden servir d'ajuda al radiòleg en el diagnòstic, millorant la reproduïbilitat i aconseguint una major objectivitat. No obstant això, no sols els discos intervertebrals són susceptibles de patir alguna patologia. Els cossos vertebrals també poden patir lesions per diverses circumstàncies. Per tant, abans de realitzar qualsevol tasca de diagnòstic, dur a terme una detecció i segmentació precisa dels cossos vertebrals és un primer pas crucial. Així, doncs, la segona part d'aquesta Tesi Doctoral té com a objectiu el desenvolupament d'un mètode automàtic per a la detecció i segmentació de la columna vertebral per mitjà de l'anàlisi de Tomografia Computada. Dur a terme una segmentació automàtica i precisa és una tasca complicada degut principalment a la gran dificultat per a distingir entre els cossos vertebrals i les costelles. Per a solucionar aquest problema s'han combinat dos mètodes de segmentació diferents: el primer utilitza un mètode Level-Set per a dur a terme una segmentació inicial; el segon utilitza un atles probabilístic, per a refinar la segmentació inicial amb un enfocament especial en la supressió de les costelles. Per tant, s'ha obtingut un volum 3D indicant la probabilitat de cada voxel de pertànyer o no a la columna vertebral, per mitjà d'un conjunt d'imatges corresponents a 14 pacients (grup d'entrenament) segmentades manualment per un expert. El mapa de probabilitat generat ha sigut deformat i adaptat a cadascun dels 7 pacients del grup de prova. Per a avaluar els resultats de la segmentació i la millora obtinguda després d'aplicar l'atles a la segmentació inicial, s'ha utilitzat el coeficient Dice (DSC) i la distància Hausdorff (HD). Els resultats han demostrat una millora en la precisió de la segmentació d'11 mm de mitja en termes de HD, amb una mitja global de 14,98 ± 1,32 mm. S'ha obtingut una millora d'un 1,3 % en termes de DSC, amb una mitja global de 91,75 ± 1,20 %. L'estudi ha demostrat que l'atles és capaç de detectar i eliminar apropiadament les estructures costals alhora que millora la precisió de la segmentació.Ruiz España, S. (2016). CARACTERIZACIÓN CUANTITATIVA DE LA PATOLOGÍA DISCAL Y LUMBAR DEGENERATIVA MEDIANTE ANÁLISIS DE IMAGEN POR RESONANCIA MAGNÉTICA Y DETECCIÓN Y SEGMENTACIÓN DE LA COLUMNA VERTEBRAL EN PACIENTES ONCOLÓGICOS A PARTIR DEL ANÁLISIS DE IMAGEN EN TOMOGRAFÍA COMPUTARIZADA [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/68485TESI

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes