Variational segmentation of vector-valued images with gradient vector flow

International audienceIn this paper, we extend the gradient vector flow field for robust variational segmentation of vector-valued images. Rather than using scalar edge information, we define a vectorial edge map derived from a weighted local structure tensor of the image that enables the diffusion of the gradient vectors in accurate directions through the 4DGVF equation. To reduce the contribution of noise in the structure tensor, image channels are weighted according to a blind estimator of contrast. The method is applied to biological volume delineation in dynamic PET imaging, and validated on realistic Monte Carlo simulations of numerical phantoms as well as on real images

Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review

[EN] Purpose: To systematically review evidence regarding the association of multi-parametric biomarkers with clinical outcomes and their capacity to explain relevant subcompartments of gliomas. Materials and Methods: Scopus database was searched for original journal papers from January 1st, 2007 to February 20th , 2017 according to PRISMA. Four hundred forty-nine abstracts of papers were reviewed and scored independently by two out of six authors. Based on those papers we analyzed associations between biomarkers, subcompartments within the tumor lesion, and clinical outcomes. From all the articles analyzed, the twenty-seven papers with the highest scores were highlighted to represent the evidence about MR imaging biomarkers associated with clinical outcomes. Similarly, eighteen studies defining subcompartments within the tumor region were also highlighted to represent the evidence of MR imaging biomarkers. Their reports were critically appraised according to the QUADAS-2 criteria. Results: It has been demonstrated that multi-parametric biomarkers are prepared for surrogating diagnosis, grading, segmentation, overall survival, progression-free survival, recurrence, molecular profiling and response to treatment in gliomas. Quantifications and radiomics features obtained from morphological exams (T1, T2, FLAIR, T1c), PWI (including DSC and DCE), diffusion (DWI, DTI) and chemical shift imaging (CSI) are the preferred MR biomarkers associated to clinical outcomes. Subcompartments relative to the peritumoral region, invasion, infiltration, proliferation, mass effect and pseudo flush, relapse compartments, gross tumor volumes, and high-risk regions have been defined to characterize the heterogeneity. For the majority of pairwise cooccurrences, we found no evidence to assert that observed co-occurrences were significantly different from their expected co-occurrences (Binomial test with False Discovery Rate correction, alpha=0.05). The co-occurrence among terms in the studied papers was found to be driven by their individual prevalence and trends in the literature. Conclusion: Combinations of MR imaging biomarkers from morphological, PWI, DWI and CSI exams have demonstrated their capability to predict clinical outcomes in different management moments of gliomas. Whereas morphologic-derived compartments have been mostly studied during the last ten years, new multi-parametric MRI approaches have also been proposed to discover specific subcompartments of the tumors. MR biomarkers from those subcompartments show the local behavior within the heterogeneous tumor and may quantify the prognosis and response to treatment of gliomas.This work was supported by the Spanish Ministry for Investigation, Development and Innovation project with identification number DPI2016-80054-R.Oltra-Sastre, M.; Fuster García, E.; Juan -Albarracín, J.; Sáez Silvestre, C.; Perez-Girbes, A.; Sanz-Requena, R.; Revert-Ventura, A.... (2019). 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Magn Reson Imaging 2014,32(7),854-859Van Cauter S.; De Keyzer F.; Sima D.M.; Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas. Neuro-oncol 2014,16(7),1010-1021Seeger A.; Braun C.; Skardelly M.; Comparison of three different MR perfusion techniques and MR spectroscopy for multiparametric assessment in distinguishing recurrent high-grade gliomas from stable disease. Acad Radiol 2013,20(12),1557-1565Chawalparit O.; Sangruchi T.; Witthiwej T.; Diagnostic performance of advanced mri in differentiating high-grade from low-grade gliomas in a setting of routine service. J Med Assoc Thai 2013,96(10),1365-1373Li Y.; Lupo J.M.; Parvataneni R.; Survival analysis in patients with newly diagnosed glioblastoma using pre- and postradiotherapy MR spectroscopic imaging. 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Computer-Aided Diagnosis in Neuroimaging

This chapter is intended to provide an overview to the most used methods for computer-aided diagnosis in neuroimaging and its application to neurodegenerative diseases. The fundamental preprocessing steps, and how they are applied to different image modalities, will be thoroughly presented. We introduce a number of widely used neuroimaging analysis algorithms, together with a wide overview on the recent advances in brain imaging processing. Finally, we provide a general conclusion on the state of the art in brain imaging processing and possible future developments

Generative-Discriminative Low Rank Decomposition for Medical Imaging Applications

In this thesis, we propose a method that can be used to extract biomarkers from medical images toward early diagnosis of abnormalities. Surge of demand for biomarkers and availability of medical images in the recent years call for accurate, repeatable, and interpretable approaches for extracting meaningful imaging features. However, extracting such information from medical images is a challenging task because the number of pixels (voxels) in a typical image is in order of millions while even a large sample-size in medical image dataset does not usually exceed a few hundred. Nevertheless, depending on the nature of an abnormality, only a parsimonious subset of voxels is typically relevant to the disease; therefore various notions of sparsity are exploited in this thesis to improve the generalization performance of the prediction task. We propose a novel discriminative dimensionality reduction method that yields good classification performance on various datasets without compromising the clinical interpretability of the results. This is achieved by combining the modelling strength of generative learning framework and the classification performance of discriminative learning paradigm. Clinical interpretability can be viewed as an additional measure of evaluation and is also helpful in designing methods that account for the clinical prior such as association of certain areas in a brain to a particular cognitive task or connectivity of some brain regions via neural fibres. We formulate our method as a large-scale optimization problem to solve a constrained matrix factorization. Finding an optimal solution of the large-scale matrix factorization renders off-the-shelf solver computationally prohibitive; therefore, we designed an efficient algorithm based on the proximal method to address the computational bottle-neck of the optimization problem. Our formulation is readily extended for different scenarios such as cases where a large cohort of subjects has uncertain or no class labels (semi-supervised learning) or a case where each subject has a battery of imaging channels (multi-channel), \etc. We show that by using various notions of sparsity as feasible sets of the optimization problem, we can encode different forms of prior knowledge ranging from brain parcellation to brain connectivity

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

A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term

Development and Application of Semi-automated ITK Tools Development and Application of Semi-automated ITK Tools for the Segmentation of Brain MR Images

Image segmentation is a process to identify regions of interest from digital images. Image segmentation plays an important role in medical image processing which enables a variety of clinical applications. It is also a tool to facilitate the detection of abnormalities such as cancerous lesions in the brain. Although numerous efforts in recent years have advanced this technique, no single approach solves the problem of segmentation for the large variety of image modalities existing today. Consequently, brain MRI segmentation remains a challenging task. The purpose of this thesis is to demonstrate brain MRI segmentation for delineation of tumors, ventricles and other anatomical structures using Insight Segmentation and Registration Toolkit (ITK) routines as the foundation. ITK is an open-source software system to support the Visible Human Project. Visible Human Project is the creation of complete, anatomically detailed, three-dimensional representations of the normal male and female human bodies. Currently under active development, ITK employs leading-edge segmentation and registration algorithms in two, three, and more dimensions. A goal of this thesis is to implement those algorithms to facilitate brain segmentation for a brain cancer research scientist