Effect of optimization framework on Rigid and Non-rigid Multimodal Image Registration

The process of transforming or aligning two images is known as image registration. In the present era, image registration is one of the most popular transformation tools in case of, for example, satellite as well as medical imaging analysis. Images captured by difference devices that can be processed under same registration model are called multimodal images. In this work, we present a multimodal image registration framework, upon which ant colony optimization (ACO) and flower pollination algorithms (FPA), which are two meta heuristics algorithms, are applied in order to improve the performance of a proposed rigid and non-rigid multimodal registration framework and decrease its processing time. The results of the ACO and FPA based framework were compared against particle swarm optimization and Genetic algorithm-based framework's results and seem to be promising

Convolutional Neural Networks and their Application in Cancer Diagnosis based on RNA-Sequencing

Η έκφραση γονιδίων αποτελεί τη μελέτη της λειτουργίας της γονιδιακής μεταγραφής, κατά την οποία συνθέτονται γονιδιακά προϊόντα, είδη RNA ή πρωτεΐνες. Η μελέτη της παρέχει την κατανόηση των κυτταρικών λειτουργιών, όπως η κυτταρική διαφοροποίηση και οι μη φυσιολογικές παθολογικές λειτουργίες. Ο καρκίνος αποτελεί μία γενετική ασθένεια όπου γενετικές παραλλαγές προκαλούν μη φυσιολογικές λειτουργίες στα γονίδια και τροποποιούν την έκφραση τους. Οι πρωτεΐνες, οι οποίες αποτελούν το τελικό αποτέλεσμα της έκφρασης γονιδίων, καθορίζουν τους φαινοτύπους και τις βιολογικές λειτουργίες. Συνεπώς, η ανίχνευση των επιπέδων έκφρασης γονιδίων δύναται να χρησιμοποιηθεί στη διάγνωση, την πρόγνωση, ακόμα και την επιλογή της θεραπείας του καρκίνου. Σε αυτή την πτυχιακή θα αναλυθεί η θεωρία και οι εφαρμογές της Βαθειάς Μάθησης. Στη συνέχεια, θα εφαρμοστεί η Βαθειά Μάθηση και πιο συγκεκριμένα ένα Συνελικτικό Νευρωνικό Δίκτυο, ως μέσο για τη διάγνωση πολλαπλών τύπων καρκίνου (κατηγοριοποίηση καρκίνων) χρησιμοποιώντας δεδομένα έκφρασης γονιδίων, και πιο συγκεκριμένα αλληλουχίες RNA. Τα δεδομένα του «The Cancer Genome Atlas» (TCGA) αποτελούνται από αλληλουχίες RNA. Θα επεξεργαστούν σε πρώτο επίπεδο και μετά θα μετατραπούν σε πολλαπλές δισδιάστατες εικόνες. Οι εικόνες αυτές θα εισαχθούν σε ένα Συνελικτικό Νευρωνικό Δίκτυο, το οποίο θα τις κατηγοριοποιήσει σε 33 τύπους καρκίνου, αποσκοπώντας στην διάγνωση με τη μέγιστη δυνατή ακρίβεια.Gene expression analysis is the study of the way genes are transcribed to synthesize functional gene products, functional RNA species, or protein products. Its study can provide insights of cellular processes, such as cellular differentiation and abnormal pathological processes. Cancer is a genetic disease where genetic variations cause abnormally functioning genes that appear to alter expression. Proteins, being the final products of gene expression, define the phenotypes and biological processes. Therefore, detecting gene expression levels can be used for cancer diagnosis, prognosis, and even treatment prediction. This thesis will be analyzing the theory and applications of Deep Learning. It will then apply Deep Learning (DL) and in particular a Convolutional Neural Network (CNN) as a means for the diagnosis of multiple cancer types (pan-cancer classification) using gene expression data and specifically RNA-sequencing. The Cancer Genome Atlas (TCGA) data, which consists of RNA-sequencing, will be preprocessed and then embedded into multiple two-dimensional (2D) images. These images will then be applied to a Convolutional Neural Network which will classify them into 33 types of cancer, in an attempt to achieve the highest possible diagnosis accuracy

ADVANCED MOTION MODELS FOR RIGID AND DEFORMABLE REGISTRATION IN IMAGE-GUIDED INTERVENTIONS

Image-guided surgery (IGS) has been a major area of interest in recent decades that continues to transform surgical interventions and enable safer, less invasive procedures. In the preoperative contexts, diagnostic imaging, including computed tomography (CT) and magnetic resonance (MR) imaging, offers a basis for surgical planning (e.g., definition of target, adjacent anatomy, and the surgical path or trajectory to the target). At the intraoperative stage, such preoperative images and the associated planning information are registered to intraoperative coordinates via a navigation system to enable visualization of (tracked) instrumentation relative to preoperative images. A major limitation to such an approach is that motions during surgery, either rigid motions of bones manipulated during orthopaedic surgery or brain soft-tissue deformation in neurosurgery, are not captured, diminishing the accuracy of navigation systems. This dissertation seeks to use intraoperative images (e.g., x-ray fluoroscopy and cone-beam CT) to provide more up-to-date anatomical context that properly reflects the state of the patient during interventions to improve the performance of IGS. Advanced motion models for inter-modality image registration are developed to improve the accuracy of both preoperative planning and intraoperative guidance for applications in orthopaedic pelvic trauma surgery and minimally invasive intracranial neurosurgery. Image registration algorithms are developed with increasing complexity of motion that can be accommodated (single-body rigid, multi-body rigid, and deformable) and increasing complexity of registration models (statistical models, physics-based models, and deep learning-based models). For orthopaedic pelvic trauma surgery, the dissertation includes work encompassing: (i) a series of statistical models to model shape and pose variations of one or more pelvic bones and an atlas of trajectory annotations; (ii) frameworks for automatic segmentation via registration of the statistical models to preoperative CT and planning of fixation trajectories and dislocation / fracture reduction; and (iii) 3D-2D guidance using intraoperative fluoroscopy. For intracranial neurosurgery, the dissertation includes three inter-modality deformable registrations using physic-based Demons and deep learning models for CT-guided and CBCT-guided procedures