2,173 research outputs found
Karakterizacija predkliničnega tumorskega ksenograftnega modela z uporabo multiparametrične MR
Introduction: In small animal studies multiple imaging modalities can be combined to complement each other in providing information on anatomical structure and function. Non-invasive imaging studies on animal models are used to monitor progressive tumor development. This helps to better understand the efficacy of new medicines and prediction of the clinical outcome. The aim was to construct a framework based on longitudinal multi-modal parametric in vivo imaging approach to perform tumor tissue characterization in mice. Materials and Methods: Multi-parametric in vivo MRI dataset consisted of T1-, T2-, diffusion and perfusion weighted images. Image set of mice (n=3) imaged weekly for 6 weeks was used. Multimodal image registration was performed based on maximizing mutual information. Tumor region of interested was delineated in weeks 2 to 6. These regions were stacked together, and all modalities combined were used in unsupervised segmentation. Clustering methods, such as K-means and Fuzzy C-means together with blind source separation technique of non-negative matrix factorization were tested. Results were visually compared with histopathological findings. Results: Clusters obtained with K-means and Fuzzy C-means algorithm coincided with T2 and ADC maps per levels of intensity observed. Fuzzy C-means clusters and NMF abundance maps reported most promising results compared to histological findings and seem as a complementary way to asses tumor microenvironment. Conclusions: A workflow for multimodal MR parametric map generation, image registration and unsupervised tumor segmentation was constructed. Good segmentation results were achieved, but need further extensive histological validation.Uvod
Eden izmed pomembnih stebrov znanstvenih raziskav v medicinski diagnostiki predstavljajo eksperimenti na živalih v sklopu predkliničnih študij. V teh študijah so eksperimenti izvedeni za namene odkrivanja in preskušanja novih terapevtskih metod za zdravljenje človeških bolezni. Rak jajčnikov je eden izmed glavnih vzrokov smrti kot posledica rakavih obolenj. Potreben je razvoj novih, učinkovitejših metod, da bi lahko uspešneje kljubovali tej bolezni. Časovno okno aplikacije novih terapevtikov je ključni dejavnik uspeha raziskovane terapije. Tumorska fiziologija se namreč razvija med napredovanjem bolezni. Eden izmed ciljev predkliničnih študij je spremljanje razvoja tumorskega mikro-okolja in tako določiti optimalno časovno okno za apliciranje razvitega terapevtika z namenom doseganja maksimalne učinkovitosti.
Slikovne modalitete so kot raziskovalno orodje postale izjemno popularne v biomedicinskih in farmakoloških raziskavah zaradi svoje neinvazivne narave. Predklinične slikovne modalitete imajo nemalo prednosti pred tradicionalnim pristopom. Skladno z raziskovalno regulativo, tako za spremljanje razvoja tumorja skozi daljši čas ni potrebno žrtvovati živali v vmesnih časovnih točkah. Sočasno lahko namreč s svojim nedestruktivnim in neinvazivnim pristopom poleg anatomskih informacij podajo tudi molekularni in funkcionalni opis preučevanega subjekta. Za dosego slednjega so običajno uporabljene različne slikovne modalitete. Pogosto se uporablja kombinacija več slikovnih modalitet, saj so medsebojno komplementarne v podajanju željenih informacij. V sklopu te naloge je predstavljeno ogrodje za procesiranje različnih modalitet magnetno resonančnih predkliničnih modelov z namenom karakterizacije tumorskega tkiva.
Metodologija
V študiji Belderbos, Govaerts, Croitor Sava in sod. [1] so z uporabo magnetne resonance preučevali določitev optimalnega časovnega okna za uspešno aplikacijo novo razvitega terapevtika. Poleg konvencionalnih magnetno resonančnih slikovnih metod (T1 in T2 uteženo slikanje) sta bili uporabljeni tudi perfuzijsko in difuzijsko uteženi tehniki. Zajem slik je potekal tedensko v obdobju šest tednov. Podatkovni seti, uporabljeni v predstavljenem delu, so bili pridobljeni v sklopu omenjene raziskave. Ogrodje za procesiranje je narejeno v okolju Matlab (MathWorks, verzija R2019b) in omogoča tako samodejno kot ročno procesiranje slikovnih podatkov.
V prvem koraku je pred generiranjem parametričnih map uporabljenih modalitet, potrebno izluščiti parametre uporabljenih protokolov iz priloženih tekstovnih datotek in zajete slike pravilno razvrstiti glede na podano anatomijo. Na tem mestu so slike tudi filtrirane in maskirane. Filtriranje je koristno za izboljšanje razmerja med koristnim signalom (slikanim živalskim modelom) in ozadjem, saj je skener za zajem slik navadno podvržen različnim izvorom slikovnega šuma. Uporabljen je bil filter ne-lokalnih povprečij Matlab knjižnice za procesiranje slik. Prednost maskiranja se potrdi v naslednjem koraku pri generiranju parametričnih map, saj se ob primerno maskiranem subjektu postopek bistveno pospeši z mapiranjem le na želenem področju.
Za izdelavo parametričnih map je uporabljena metoda nelinearnih najmanjših kvadratov. Z modeliranjem fizikalnih pojavov uporabljenih modalitet tako predstavimo preiskovan živalski model z biološkimi parametri. Le-ti se komplementarno dopolnjujejo v opisu fizioloških lastnosti preučevanega modela na ravni posameznih slikovnih elementov.
Ključen gradnik v uspešnem dopolnjevanju informacij posameznih modalitet je ustrezna poravnava parametričnih map. Posamezne modalitete so zajete zaporedno, ob različnih časih. Skeniranje vseh modalitet posamezne živali skupno traja več kot eno uro. Med zajemom slik tako navkljub uporabi anestetikov prihaja do majhnih premikov živali. V kolikor ti premiki niso pravilno upoštevani, prihaja do napačnih interpretacij skupnih informacij večih modalitet. Premiki živali znotraj modalitet so bili modelirani kot toge, med različnimi modalitetami pa kot afine preslikave. Poravnava slik je izvedena z lastnimi Matlab funkcijami ali z uporabo funkcij iz odprtokodnega ogrodja za procesiranje slik Elastix.
Z namenom karakterizacije tumorskega tkiva so bile uporabljene metode nenadzorovanega razčlenjevanja. Bistvo razčlenjevanja je v združevanju posameznih slikovnih elementov v segmente. Elementi si morajo biti po izbranem kriteriju dovolj medsebojno podobni in se hkrati razlikovati od elementov drugih segmentov. Za razgradnjo so bile izbrane tri metode: metoda K-tih povprečij, kot ena izmed enostavnejšihmetoda mehkih C-tih povprečij, s prednostjo mehke razčlenitvein kot zadnja, nenegativna matrična faktorizacija. Slednja ponuja pogled na razčlenitev tkiva kot produkt tipičnih več-modalnih značilk in njihove obilice za vsak posamezni slikovni element. Za potrditev izvedenega razčlenjevanja z omenjenimi metodami je bila izvedena vizualna primerjava z rezultati histopatološke analize.
Rezultati
Na ustvarjene parametrične mape je imela poravnava slik znotraj posameznih modalitet velik vpliv. Zaradi dolgotrajnega zajema T1 uteženih slik nemalokrat prihaja do premikov živali, kar brez pravilne poravnave slik negativno vpliva na mapiranje modalitet in kasnejšo segmentacijo slik. Generirane mape imajo majhno odstopanje od tistih, narejenih s standardno uporabljenimi odprtokodnimi programi. Klastri pridobljeni z metodama K-tih in mehkih C-tih povprečij dobro sovpadajo z razčlenbami glede na njihovo inteziteto pri T2 in ADC mapah. Najobetavnejše rezultate po primerjavi s histološkimi izsledki podajata metoda mehkih C-povprečij in nenegativna matrična faktorizacija. Njuni segmentaciji se dopolnjujeta v razlagi tumorskega mikro-okolja.
Zaključek
Z izgradnjo ogrodja za procesiranje slik magnetne resonance in segmentacijo tumorskega tkiva je bil cilj magistrske naloge dosežen. Zasnova ogrodja omogoča poljubno dodajanje drugih modalitet in uporabo drugih živalskih modelov. Rezultati razčlenitve tumorskega tkiva so obetavni, vendar je potrebna nadaljna primerjava z rezultati histopatološke analize. Možna nadgradnja je izboljšanje robustnosti poravnave slik z uporabo modela netoge (elastične) preslikave. Prav tako je smiselno preizkusiti dodatne metode nenadzorovane segmentacije in dobljene rezultate primerjati s tukaj predstavljenimi
Multi-atlas label fusion by using supervised local weighting for brain image segmentation
La segmentación automática de estructuras de interés en imágenes de resonancia magnética cerebral requiere esfuerzos significantes, debido a las formas complicadas, el bajo contraste y la variabilidad anatómica. Un aspecto que reduce el desempeño de la segmentación basada en múltiples atlas es la suposición de correspondencias uno-a-uno entre los voxeles objetivo y los del atlas. Para mejorar el desempeño de la segmentación, las metodologías de fusión de etiquetas incluyen información espacial y de intensidad a través de estrategias de votación ponderada a nivel de voxel. Aunque los pesos se calculan para un conjunto de atlas predefinido, estos no son muy eficientes en etiquetar estructuras intrincadas, ya que la mayoría de las formas de los tejidos no se distribuyen uniformemente en las imágenes. Este artículo propone una metodología de extracción de características a nivel de voxel basado en la combinación lineal de las intensidades de un parche. Hasta el momento, este es el primer intento de extraer características locales maximizando la función de alineamiento de kernel centralizado, buscando construir representaciones discriminativas, superar la complejidad de las estructuras, y reducir la influencia de los artefactos. Para validar los resultados, la estrategia de segmentación propuesta se compara contra la segmentación Bayesiana y la fusión de etiquetas basada en parches en tres bases de datos diferentes. Respecto del índice de similitud Dice, nuestra propuesta alcanza el más alto acierto (90.3% en promedio) con suficiente robusticidad ante los artefactos y respetabilidad apropiada.The automatic segmentation of interest structures is devoted to the morphological analysis of brain magnetic resonance imaging volumes. It demands significant efforts due to its complicated shapes and since it lacks contrast between tissues and intersubject anatomical variability. One aspect that reduces the accuracy of the multi-atlasbased segmentation is the label fusion assumption of one-to-one correspondences between targets and atlas voxels. To improve the performance of brain image segmentation, label fusion approaches include spatial and intensity information by using voxel-wise weighted voting strategies. Although the weights are assessed for a predefined atlas set, they are not very efficient for labeling intricate structures since most tissue shapes are not uniformly distributed in the images. This paper proposes a methodology of voxel-wise feature extraction based on the linear combination of patch intensities. As far as we are concerned, this is the first attempt to locally learn the features by maximizing the centered kernel alignment function. Our methodology aims to build discriminative representations, deal with complex structures, and reduce the image artifacts. The result is an enhanced patch-based segmentation of brain images. For validation, the proposed brain image segmentation approach is compared against Bayesian-based and patch-wise label fusion on three different brain image datasets. In terms of the determined Dice similarity index, our proposal shows the highest segmentation accuracy (90.3% on average); it presents sufficient artifact robustness, and provides suitable repeatability of the segmentation results
Multiscale, multimodal analysis of tumor heterogeneity in IDH1 mutant vs wild-type diffuse gliomas.
Glioma is recognized to be a highly heterogeneous CNS malignancy, whose diverse cellular composition and cellular interactions have not been well characterized. To gain new clinical- and biological-insights into the genetically-bifurcated IDH1 mutant (mt) vs wildtype (wt) forms of glioma, we integrated data from protein, genomic and MR imaging from 20 treatment-naïve glioma cases and 16 recurrent GBM cases. Multiplexed immunofluorescence (MxIF) was used to generate single cell data for 43 protein markers representing all cancer hallmarks, Genomic sequencing (exome and RNA (normal and tumor) and magnetic resonance imaging (MRI) quantitative features (protocols were T1-post, FLAIR and ADC) from whole tumor, peritumoral edema and enhancing core vs equivalent normal region were also collected from patients. Based on MxIF analysis, 85,767 cells (glioma cases) and 56,304 cells (GBM cases) were used to generate cell-level data for 24 biomarkers. K-means clustering was used to generate 7 distinct groups of cells with divergent biomarker profiles and deconvolution was used to assign RNA data into three classes. Spatial and molecular heterogeneity metrics were generated for the cell data. All features were compared between IDH mt and IDHwt patients and were finally combined to provide a holistic/integrated comparison. Protein expression by hallmark was generally lower in the IDHmt vs wt patients. Molecular and spatial heterogeneity scores for angiogenesis and cell invasion also differed between IDHmt and wt gliomas irrespective of prior treatment and tumor grade; these differences also persisted in the MR imaging features of peritumoral edema and contrast enhancement volumes. A coherent picture of enhanced angiogenesis in IDHwt tumors was derived from multiple platforms (genomic, proteomic and imaging) and scales from individual proteins to cell clusters and heterogeneity, as well as bulk tumor RNA and imaging features. Longer overall survival for IDH1mt glioma patients may reflect mutation-driven alterations in cellular, molecular, and spatial heterogeneity which manifest in discernable radiological manifestations
Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature
[ES] El futuro de la imagen médica está ligado a la inteligencia artificial. El análisis manual de imágenes médicas es hoy en día una tarea ardua, propensa a errores y a menudo inasequible para los humanos, que ha llamado la atención de la comunidad de Aprendizaje Automático (AA). La Imagen por Resonancia Magnética (IRM) nos proporciona una rica variedad de representaciones de la morfología y el comportamiento de lesiones inaccesibles sin una intervención invasiva arriesgada. Sin embargo, explotar la potente pero a menudo latente información contenida en la IRM es una tarea muy complicada, que requiere técnicas de análisis computacional inteligente.
Los tumores del sistema nervioso central son una de las enfermedades más críticas estudiadas a través de IRM. Específicamente, el glioblastoma representa un gran desafío, ya que, hasta la fecha, continua siendo un cáncer letal que carece de una terapia satisfactoria. Del conjunto de características que hacen del glioblastoma un tumor tan agresivo, un aspecto particular que ha sido ampliamente estudiado es su heterogeneidad vascular. La fuerte proliferación vascular del glioblastoma, así como su robusta angiogénesis han sido consideradas responsables de la alta letalidad de esta neoplasia.
Esta tesis se centra en la investigación y desarrollo del método Hemodynamic Tissue Signature (HTS): un método de AA no supervisado para describir la heterogeneidad vascular de los glioblastomas mediante el análisis de perfusión por IRM. El método HTS se basa en el concepto de hábitat, que se define como una subregión de la lesión con un perfil de IRM que describe un comportamiento fisiológico concreto. El método HTS delinea cuatro hábitats en el glioblastoma: el hábitat HAT, como la región más perfundida del tumor con captación de contraste; el hábitat LAT, como la región del tumor con un perfil angiogénico más bajo; el hábitat IPE, como la región adyacente al tumor con índices de perfusión elevados; y el hábitat VPE, como el edema restante de la lesión con el perfil de perfusión más bajo. La investigación y desarrollo de este método ha originado una serie de contribuciones enmarcadas en esta tesis.
Primero, para verificar la fiabilidad de los métodos de AA no supervisados en la extracción de patrones de IRM, se realizó una comparativa para la tarea de segmentación de gliomas de grado alto. Segundo, se propuso un algoritmo de AA no supervisado dentro de la familia de los Spatially Varying Finite Mixture Models. El algoritmo propone una densidad a priori basada en un Markov Random Field combinado con la función probabilística Non-Local Means, para codificar la idea de que píxeles vecinos tienden a pertenecer al mismo objeto. Tercero, se presenta el método HTS para describir la heterogeneidad vascular del glioblastoma. El método se ha aplicado a casos reales en una cohorte local de un solo centro y en una cohorte internacional de más de 180 pacientes de 7 centros europeos. Se llevó a cabo una evaluación exhaustiva del método para medir el potencial pronóstico de los hábitats HTS. Finalmente, la tecnología desarrollada en la tesis se ha integrado en la plataforma online ONCOhabitats (https://www.oncohabitats.upv.es). La plataforma ofrece dos servicios: 1) segmentación de tejidos de glioblastoma, y 2) evaluación de la heterogeneidad vascular del tumor mediante el método HTS.
Los resultados de esta tesis han sido publicados en diez contribuciones científicas, incluyendo revistas y conferencias de alto impacto en las áreas de Informática Médica, Estadística y Probabilidad, Radiología y Medicina Nuclear y Aprendizaje Automático. También se emitió una patente industrial registrada en España, Europa y EEUU. Finalmente, las ideas originales concebidas en esta tesis dieron lugar a la creación de ONCOANALYTICS CDX, una empresa enmarcada en el modelo de negocio de los companion diagnostics de compuestos farmacéuticos.[EN] The future of medical imaging is linked to Artificial Intelligence (AI). The manual analysis of medical images is nowadays an arduous, error-prone and often unaffordable task for humans, which has caught the attention of the Machine Learning (ML) community. Magnetic Resonance Imaging (MRI) provides us with a wide variety of rich representations of the morphology and behavior of lesions completely inaccessible without a risky invasive intervention. Nevertheless, harnessing the powerful but often latent information contained in MRI acquisitions is a very complicated task, which requires computational intelligent analysis techniques.
Central nervous system tumors are one of the most critical diseases studied through MRI. Specifically, glioblastoma represents a major challenge, as it remains a lethal cancer that, to date, lacks a satisfactory therapy. Of the entire set of characteristics that make glioblastoma so aggressive, a particular aspect that has been widely studied is its vascular heterogeneity. The strong vascular proliferation of glioblastomas, as well as their robust angiogenesis and extensive microvasculature heterogeneity have been claimed responsible for the high lethality of the neoplasm.
This thesis focuses on the research and development of the Hemodynamic Tissue Signature (HTS) method: an unsupervised ML approach to describe the vascular heterogeneity of glioblastomas by means of perfusion MRI analysis. The HTS builds on the concept of habitats. A habitat is defined as a sub-region of the lesion with a particular MRI profile describing a specific physiological behavior. The HTS method delineates four habitats within the glioblastoma: the HAT habitat, as the most perfused region of the enhancing tumor; the LAT habitat, as the region of the enhancing tumor with a lower angiogenic profile; the potentially IPE habitat, as the non-enhancing region adjacent to the tumor with elevated perfusion indexes; and the VPE habitat, as the remaining edema of the lesion with the lowest perfusion profile. The research and development of the HTS method has generated a number of contributions to this thesis.
First, in order to verify that unsupervised learning methods are reliable to extract MRI patterns to describe the heterogeneity of a lesion, a comparison among several unsupervised learning methods was conducted for the task of high grade glioma segmentation. Second, a Bayesian unsupervised learning algorithm from the family of Spatially Varying Finite Mixture Models is proposed. The algorithm integrates a Markov Random Field prior density weighted by the probabilistic Non-Local Means function, to codify the idea that neighboring pixels tend to belong to the same semantic object. Third, the HTS method to describe the vascular heterogeneity of glioblastomas is presented. The HTS method has been applied to real cases, both in a local single-center cohort of patients, and in an international retrospective cohort of more than 180 patients from 7 European centers. A comprehensive evaluation of the method was conducted to measure the prognostic potential of the HTS habitats. Finally, the technology developed in this thesis has been integrated into an online open-access platform for its academic use. The ONCOhabitats platform is hosted at https://www.oncohabitats.upv.es, and provides two main services: 1) glioblastoma tissue segmentation, and 2) vascular heterogeneity assessment of glioblastomas by means of the HTS method.
The results of this thesis have been published in ten scientific contributions, including top-ranked journals and conferences in the areas of Medical Informatics, Statistics and Probability, Radiology & Nuclear Medicine and Machine Learning. An industrial patent registered in Spain, Europe and EEUU was also issued. Finally, the original ideas conceived in this thesis led to the foundation of ONCOANALYTICS CDX, a company framed into the business model of companion diagnostics for pharmaceutical compounds.[CA] El futur de la imatge mèdica està lligat a la intel·ligència artificial. L'anàlisi manual d'imatges mèdiques és hui dia una tasca àrdua, propensa a errors i sovint inassequible per als humans, que ha cridat l'atenció de la comunitat d'Aprenentatge Automàtic (AA). La Imatge per Ressonància Magnètica (IRM) ens proporciona una àmplia varietat de representacions de la morfologia i el comportament de lesions inaccessibles sense una intervenció invasiva arriscada. Tanmateix, explotar la potent però sovint latent informació continguda a les adquisicions de IRM esdevé una tasca molt complicada, que requereix tècniques d'anàlisi computacional intel·ligent.
Els tumors del sistema nerviós central són una de les malalties més crítiques estudiades a través de IRM. Específicament, el glioblastoma representa un gran repte, ja que, fins hui, continua siguent un càncer letal que manca d'una teràpia satisfactòria. Del conjunt de característiques que fan del glioblastoma un tumor tan agressiu, un aspecte particular que ha sigut àmpliament estudiat és la seua heterogeneïtat vascular. La forta proliferació vascular dels glioblastomes, així com la seua robusta angiogènesi han sigut considerades responsables de l'alta letalitat d'aquesta neoplàsia.
Aquesta tesi es centra en la recerca i desenvolupament del mètode Hemodynamic Tissue Signature (HTS): un mètode d'AA no supervisat per descriure l'heterogeneïtat vascular dels glioblastomas mitjançant l'anàlisi de perfusió per IRM. El mètode HTS es basa en el concepte d'hàbitat, que es defineix com una subregió de la lesió amb un perfil particular d'IRM, que descriu un comportament fisiològic concret. El mètode HTS delinea quatre hàbitats dins del glioblastoma: l'hàbitat HAT, com la regió més perfosa del tumor amb captació de contrast; l'hàbitat LAT, com la regió del tumor amb un perfil angiogènic més baix; l'hàbitat IPE, com la regió adjacent al tumor amb índexs de perfusió elevats, i l'hàbitat VPE, com l'edema restant de la lesió amb el perfil de perfusió més baix. La recerca i desenvolupament del mètode HTS ha originat una sèrie de contribucions emmarcades a aquesta tesi.
Primer, per verificar la fiabilitat dels mètodes d'AA no supervisats en l'extracció de patrons d'IRM, es va realitzar una comparativa en la tasca de segmentació de gliomes de grau alt. Segon, s'ha proposat un algorisme d'AA no supervisat dintre de la família dels Spatially Varying Finite Mixture Models. L'algorisme proposa un densitat a priori basada en un Markov Random Field combinat amb la funció probabilística Non-Local Means, per a codificar la idea que els píxels veïns tendeixen a pertànyer al mateix objecte semàntic. Tercer, es presenta el mètode HTS per descriure l'heterogeneïtat vascular dels glioblastomas. El mètode HTS s'ha aplicat a casos reals en una cohort local d'un sol centre i en una cohort internacional de més de 180 pacients de 7 centres europeus. Es va dur a terme una avaluació exhaustiva del mètode per mesurar el potencial pronòstic dels hàbitats HTS. Finalment, la tecnologia desenvolupada en aquesta tesi s'ha integrat en una plataforma online ONCOhabitats (https://www.oncohabitats.upv.es). La plataforma ofereix dos serveis: 1) segmentació dels teixits del glioblastoma, i 2) avaluació de l'heterogeneïtat vascular dels glioblastomes mitjançant el mètode HTS.
Els resultats d'aquesta tesi han sigut publicats en deu contribucions científiques, incloent revistes i conferències de primer nivell a les àrees d'Informàtica Mèdica, Estadística i Probabilitat, Radiologia i Medicina Nuclear i Aprenentatge Automàtic. També es va emetre una patent industrial registrada a Espanya, Europa i els EEUU. Finalment, les idees originals concebudes en aquesta tesi van donar lloc a la creació d'ONCOANALYTICS CDX, una empresa emmarcada en el model de negoci dels companion diagnostics de compostos farmacèutics.En este sentido quiero agradecer a las diferentes instituciones y estructuras de financiación de investigación que han contribuido al desarrollo de esta tesis. En especial quiero agradecer a la Universitat Politècnica de València, donde he desarrollado toda mi carrera acadèmica y científica, así como al Ministerio de Ciencia e Innovación, al Ministerio de Economía y Competitividad, a la Comisión Europea, al EIT Health Programme y a la fundación Caixa ImpulseJuan Albarracín, J. (2020). Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/149560TESI
-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing
This paper presents a generic probabilistic framework for estimating the
statistical dependency and finding the anatomical correspondences among an
arbitrary number of medical images. The method builds on a novel formulation of
the -dimensional joint intensity distribution by representing the common
anatomy as latent variables and estimating the appearance model with
nonparametric estimators. Through connection to maximum likelihood and the
expectation-maximization algorithm, an information\hyp{}theoretic metric called
-metric and a co-registration algorithm named -CoReg
are induced, allowing groupwise registration of the observed images with
computational complexity of . Moreover, the method naturally
extends for a weakly-supervised scenario where anatomical labels of certain
images are provided. This leads to a combined\hyp{}computing framework
implemented with deep learning, which performs registration and segmentation
simultaneously and collaboratively in an end-to-end fashion. Extensive
experiments were conducted to demonstrate the versatility and applicability of
our model, including multimodal groupwise registration, motion correction for
dynamic contrast enhanced magnetic resonance images, and deep combined
computing for multimodal medical images. Results show the superiority of our
method in various applications in terms of both accuracy and efficiency,
highlighting the advantage of the proposed representation of the imaging
process
Magnetic Resonance Image segmentation using Pulse Coupled Neural Networks
The Pulse Couple Neural Network (PCNN) was developed by Eckhorn to model the observed synchronization of neural assemblies in the visual cortex of small mammals such as a cat. In this dissertation, three novel PCNN based automatic segmentation algorithms were developed to segment Magnetic Resonance Imaging (MRI) data: (a) PCNN image \u27signature\u27 based single region cropping; (b) PCNN - Kittler Illingworth minimum error thresholding and (c) PCNN -Gaussian Mixture Model - Expectation Maximization (GMM-EM) based multiple material segmentation. Among other control tests, the proposed algorithms were tested on three T2 weighted acquisition configurations comprising a total of 42 rat brain volumes, 20 T1 weighted MR human brain volumes from Harvard\u27s Internet Brain Segmentation Repository and 5 human MR breast volumes. The results were compared against manually segmented gold standards, Brain Extraction Tool (BET) V2.1 results, published results and single threshold methods. The Jaccard similarity index was used for numerical evaluation of the proposed algorithms. Our quantitative results demonstrate conclusively that PCNN based multiple material segmentation strategies can approach a human eye\u27s intensity delineation capability in grayscale image segmentation tasks
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