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

Probe-based visual analysis of geospatial simulations

This work documents the design, development, refinement, and evaluation of probes as an interaction technique for expanding both the usefulness and usability of geospatial visualizations, specifically those of simulations. Existing applications that allow the visualization of, and interaction with, geospatial simulations and their results generally present views of the data that restrict the user to a single perspective. When zoomed out, local trends and anomalies become suppressed and lost; when zoomed in, spatial awareness and comparison between regions become limited. The probe-based interaction model integrates coordinated visualizations within individual probe interfaces, which depict the local data in user-defined regions-of-interest. It is especially useful when dealing with complex simulations or analyses where behavior in various localities differs from other localities and from the system as a whole. The technique has been incorporated into a number of geospatial simulations and visualization tools. In each of these applications, and in general, probe-based interaction enhances spatial awareness, improves inspection and comparison capabilities, expands the range of scopes, and facilitates collaboration among multiple users. The great freedom afforded to users in defining regions-of-interest can cause modifiable areal unit problems to affect the reliability of analyses without the user’s knowledge, leading to misleading results. However, by automatically alerting the user to these potential issues, and providing them tools to help adjust their selections, these unforeseen problems can be revealed, and even corrected

Towards Automated Analysis of Urban Infrastructure after Natural Disasters using Remote Sensing

Natural disasters, such as earthquakes and hurricanes, are an unpreventable component of the complex and changing environment we live in. Continued research and advancement in disaster mitigation through prediction of and preparation for impacts have undoubtedly saved many lives and prevented significant amounts of damage, but it is inevitable that some events will cause destruction and loss of life due to their sheer magnitude and proximity to built-up areas. Consequently, development of effective and efficient disaster response methodologies is a research topic of great interest. A successful emergency response is dependent on a comprehensive understanding of the scenario at hand. It is crucial to assess the state of the infrastructure and transportation network, so that resources can be allocated efficiently. Obstructions to the roadways are one of the biggest inhibitors to effective emergency response. To this end, airborne and satellite remote sensing platforms have been used extensively to collect overhead imagery and other types of data in the event of a natural disaster. The ability of these platforms to rapidly probe large areas is ideal in a situation where a timely response could result in saving lives. Typically, imagery is delivered to emergency management officials who then visually inspect it to determine where roads are obstructed and buildings have collapsed. Manual interpretation of imagery is a slow process and is limited by the quality of the imagery and what the human eye can perceive. In order to overcome the time and resource limitations of manual interpretation, this dissertation inves- tigated the feasibility of performing fully automated post-disaster analysis of roadways and buildings using airborne remote sensing data. First, a novel algorithm for detecting roadway debris piles from airborne light detection and ranging (lidar) point clouds and estimating their volumes is presented. Next, a method for detecting roadway flooding in aerial imagery and estimating the depth of the water using digital elevation models (DEMs) is introduced. Finally, a technique for assessing building damage from airborne lidar point clouds is presented. All three methods are demonstrated using remotely sensed data that were collected in the wake of recent natural disasters. The research presented in this dissertation builds a case for the use of automatic, algorithmic analysis of road networks and buildings after a disaster. By reducing the latency between the disaster and the delivery of damage maps needed to make executive decisions about resource allocation and performing search and rescue missions, significant loss reductions could be achieved

Similarity Assessment and Retrieval of CAD Models

Ph.DDOCTOR OF PHILOSOPH

Evaluation and implementation of an auto-encoder for compression of satellite images in the ScOSA project

The thesis evaluates the efficiency of various autoencoder neural networks for image compression regarding satellite imagery. The results highlight the evaluation and implementation of autoencoder architectures and the procedures required to deploy neural networks to reliable embedded devices. The developed autoencoders evaluated, targeting a ZYNQ 7020 FPGA (Field Programmable Gate Array) and a ZU7EV FPGA

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010