DoctorEye: A clinically driven multifunctional platform, for accurate processing of tumors in medical images

Copyright @ Skounakis et al.This paper presents a novel, open access interactive platform for 3D medical image analysis, simulation and visualization, focusing in oncology images. The platform was developed through constant interaction and feedback from expert clinicians integrating a thorough analysis of their requirements while having an ultimate goal of assisting in accurately delineating tumors. It allows clinicians not only to work with a large number of 3D tomographic datasets but also to efficiently annotate multiple regions of interest in the same session. Manual and semi-automatic segmentation techniques combined with integrated correction tools assist in the quick and refined delineation of tumors while different users can add different components related to oncology such as tumor growth and simulation algorithms for improving therapy planning. The platform has been tested by different users and over large number of heterogeneous tomographic datasets to ensure stability, usability, extensibility and robustness with promising results. AVAILABILITY: THE PLATFORM, A MANUAL AND TUTORIAL VIDEOS ARE AVAILABLE AT: http://biomodeling.ics.forth.gr. It is free to use under the GNU General Public License

Μοντελοποίηση ανάπτυξης γλοιοβλαστώματος

4th-grade glioma (Glioblastoma multiforme) is the most aggressive type of brain cancer. Several mathematical models have been developed towards identifying the mechanism of tumor growth. In this thesis we try to figure out the main mathematic and implementation issues concerning a 3-dimensional (3D) model for appropriately simulating glioma growth in brain. A brief review on the models that have been proposed during the last decades for simulating glioma growth is initially provided. Afterwards a deep study is carried in the mathematical body of a 3D diffusive model, which exploits local tissue anisotropy and heterogeneity in brain with differentiated cancer cell proliferation schemes. Therefore, a virtual controllable case is presented for evaluating the accuracy, simulation time and storage & computational consistency of the various numerical schemes that have been implemented. Continuing, radiotherapy is introduced into the model and some first experimental results are presented. Concluding, we show a model for exploiting statistical tissue information and diffusion tensors extracted from atlases of healthy brain tissue. Lastly, after introducing the gross tumor volume, we present the proliferation – invasion – hypoxia – necrotic – angiogenesis (PIHNA) model and some in vivo experiments in mice.Το γλοίωμα 4ου βαθμού (πολύμορφο γλοιοβλάστωμα) είναι η πιο επιθετική μορφή εγκεφαλικού όγκου. Διάφορα μαθηματικά μοντέλα έχουν αναπτυχθεί προς τον εντοπισμό του μηχανισμού της ανάπτυξης του καρκίνου. Σε αυτή τη διατριβή προσπαθούμε να παρουσιάσουμε το μαθηματικό υπόβαθρο για ένα τρισδιάστατο μοντέλο που προσομοιώνει ικανοποιητικά την ανάπτυξη γλοιοβλαστώματος στον εγκέφαλο. Αφού κάνουμε μια ανασκόπηση των διαφόρων μοντέλων που έχουν προταθεί τις τελευταίες δεκαετίες για την ανάπτυξη του γλοιώματος, παραθέτουμε μια εις βάθος μελέτη των μαθηματικών στοιχείων ενός τρισδιάστατου μοντέλου διάχυσης, που εκμεταλλεύεται πληροφορία για την ανισοτροπική και ετερογενή διάχυση. Προς την κατεύθυνση αυτή, ένας εικονικός όγκος χρησιμοποιείται για την αξιολόγηση της ακρίβειας, του χρόνου εκτέλεσης και της υπολογιστικής πολυπλοκότητας που εμπεριέχουν διαφορετικές τεχνικές προσομοίωσης. Στη συνέχεια το μοντέλο προσαρμόζεται ώστε να μπορεί να προσομοιώσει ακτινοθεραπεία και παρουσιάζονται κάποια πειράματα σε αληθινά δεδομένα ασθενών. Στη συνέχεια παρουσιάζουμε ένα μοντέλο που εκμεταλλεύεται άτλαντες από υγιή εγκέφαλο προς τον εντοπισμό των περιοχών της φαιάς και της λευκής ουσίας (δεδομένα που χρειάζεται το μοντέλο). Τέλος, παρουσιάζεται το μοντέλο PIHNA και κάποια αποτελέσματα in vivo πειραμάτων σε ποντίκια

Comparing Finite Elements and Finite Differences for developing Diffusive Models of glioma growth

Glioma is the most aggressive type of brain tumor. Several mathematical models have been developed during the last two decades, towards simulating the mechanisms that govern the development of glioma. The most common models use the diffusion-reaction equation (DRE) for simulating the spatiotemporal variation of tumor cell concentration. The proposed diffusive models have mainly used finite differences (FDs) or finite elements (FEs) for the approximation of the solution of the partial differential DRE. This paper presents experimental results on the comparison of the FEs and FDs, especially focused on the glioma model case. It is studied how the different meshes of brain can affect computational consistency, simulation time and efficiency of the model. The experiments have been studied on a test case, for which there is a known algebraic expression of the solution. Thus, it is possible to calculate the error that the different models yield

Diffusive modelling of glioma evolution: a review

Summarization: Gliomas, the most aggressive form of brain cancer, are known for their widespread invasion into the tis-sue near the tumor lesion. Exponential models, which have been widely used in other types of cancers, can-not be used for the simulation of tumor growth, due to the diffusive behavior of glioma. Diffusive models that have been proposed in the last two decades seem to better approximate the expansion of gliomas. This paper covers the history of glioma diffusive model-ling, starting from the simplified initial model in 90s and describing how this have been enriched to take into account heterogenous brain tissue, anisotropic migration of glioma cells and adjustable proliferation rates. Especially, adjustable proliferation rates are very important for modelling therapy plans and per-sonalising therapy to different patientsΠαρουσιάστηκε στο: Journal of Biomedical Science and Engineerin

Depression Assessment by Fusing High and Low Level Features from Audio, Video, and Text

International audienceDepression is a major cause of disability world-wide. The present paper reports on the results of our participation to the depression sub-challenge of the sixth Audio/Visual Emotion Challenge (AVEC 2016), which was designed to compare feature modalities ( audio, visual, interview transcript-based) in gender-based and gender-independent modes using a variety of classification algorithms. In our approach, both high and low level features were assessed in each modality. Audio features were extracted from the low-level descriptors provided by the challenge organizers. Several visual features were extracted and assessed including dynamic characteristics of facial elements (using Landmark Motion History Histograms and Landmark Motion Magnitude), global head motion, and eye blinks. These features were combined with statistically derived features from pre-extracted features ( emotions, action units, gaze, and pose). Both speech rate and word-level semantic content were also evaluated. Classification results are reported using four different classification schemes: i) gender-based models for each individual modality, ii) the feature fusion model, ii) the decision fusion model, and iv) the posterior probability classification model. Proposed approaches outperforming the reference classification accuracy include the one utilizing statistical descriptors of low-level audio features. This approach achieved f1-scores of 0.59 for identifying depressed and 0.87 for identifying notdepressed individuals on the development set and 0.52/0.81, respectively for the test set

In-depth analysis and evaluation of diffusive glioma models

Summarization: Glioma is one of the most aggressive types of brain tumor. Several mathematical models have been developed during the past two decades, toward simulating the mechanisms that govern the development of glioma. The most common models use the diffusion-reaction equation (DRE) for simulating the spatiotemporal variation of tumor cell concentration. Nevertheless, despite the applications presented, there has been little work on studying the details of the mathematical solution and implementation of the 3-D diffusion model and presenting a qualitative analysis of the algorithmic results. This paper presents a complete mathematical framework on the solution of the DRE using different numerical schemes. This framework takes into account all characteristics of the latest models, such as brain tissue heterogeneity, anisotropic tumor cell migration, chemotherapy, and resection modeling. The different numerical schemes presented have been evaluated based upon the degree to which the DRE exact solution is approximated. Experiments have been conducted both on real datasets and a test case for which there is a known algebraic expression of the solution. Thus, it is possible to calculate the accuracy of the different models.Presented on: IEEE Transactions on Information Technology in Biomedicin

A complete mathematical study of a 3D model of heterogeneous and anisotropic glioma evolution

Summarization: Glioma is the most aggressive type of brain cancer. Several mathematical models have been developed towards identifying the mechanism of tumor growth. The most successful models have used variations of the diffusion-reaction equation, with the recent ones taking into account brain tissue heterogeneity and anisotropy. However, to the best of our knowledge, there hasn't been any work studying in detail the mathematical solution and implementation of the 3D diffusion model, addressing related heterogeneity and anisotropy issues. To this end, this paper introduces a complete mathematical framework on how to derive the solution of the equation using different numerical approximation of finite differences. It indicates how different proliferation rate schemes can be incorporated in this solution and presents a comparative study of different numerical approaches.Presented on

The mathematical path to develop a heterogeneous, anisotropic and 3-dimensional glioma model using finite differences

Summarization: Several mathematical models have been developed to express glioma growth behavior. The most successful models have used the diffusion-reaction equation, with the most recent ones taking into account spatial heterogeneity and anisotropy. However, to the best of our knowledge, there hasn't been any work studying in detail the mathematical solution and implementation of the 3D diffusion model, addressing all related heterogeneity and anisotropy issues. This paper presents a complete mathematical framework on how to derive the solution of the equation using different numerical schemes of finite differences. Moreover, the derived mathematics can be customized to incorporate various cell proliferation schemes. Lastly, a comparative study of the numerical scheme helps us select the best of them and then apply it to real clinical data.Presented on