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

Video annotation for studying the brain in naturalistic settings

Aivojen tutkiminen luonnollisissa asetelmissa on viimeaikainen suunta aivotutkimuksessa. Perinteisesti aivotutkimuksessa on käytetty hyvin yksinkertaistettuja ja keinotekoisia ärsykkeitä, mutta viime aikoina on alettu tutkia ihmisaivoja yhä luonnollisimmissa asetelmissa. Näissä kokeissa on käytetty elokuvaa luonnollisena ärsykkeenä. Elokuvan monimutkaisuudesta johtuen tarvitaan siitä yksinkertaistettu malli laskennallisen käsittely mahdollistamiseksi. Tämä malli tuotetaan annotoimalla; keräämällä elokuvan keskeisistä ärsykepiirteistä dataa tietorakenteen muodostamiseksi. Tätä dataa verrataan aivojen aikariippuvaiseen aktivaatioon etsittäessä mahdollisia korrelaatioita. Kaikkia elokuvan ominaisuuksia ei pystytä annotoimaan automaattisesti; ihmiselle merkitykselliset ominaisuudet on annotoitava käsin, joka on joissain tapauksissa ongelmallista johtuen elokuvan käyttämistä useista viestintämuodoista. Ymmärrys näistä viestinnän muodoista auttaa analysoimaan ja annotoimaan elokuvia. Elokuvaa Tulitikkutehtaan Tyttö (Aki Kaurismäki, 1990) käytettiin ärsykkeenä aivojen tutkimiseksi luonnollisissa asetelmissa. Kokeista saadun datan analysoinnin helpottamiseksi annotoitiin elokuvan keskeiset visuaaliset ärsykepiirteet. Tässä työssä tutkittiin annotointiin käytettävissä olevia eri lähestymistapoja ja teknologioita. Annotointi auttaa informaation organisoinnissa, mistä syystä annotointia ilmestyy nykyään kaikkialla. Erilaisia annotaatiotyökaluja ja -teknologioita kehitetään jatkuvasti. Lisäksi videoanalyysimenetelmät ovat alkaneet mahdollistaa yhä merkityksellisemmän informaation automaattisen annotoinnin tulevaisuudessa.Studying the brain in naturalistic settings is a recent trend in neuroscience. Traditional brain imaging experiments have relied on using highly simplified and artificial stimuli, but recently efforts have been put into studying the human brain in conditions closer to real-life. The methodology used in these studies involve imitating naturalistic stimuli with a movie. Because of the complexity of the naturalistic stimulus, a simplified model of it is needed to handle it computationally. This model is obtained by making annotations; collecting information of salient features of the movie to form a data structure. This data is compared with the brain activity evolving in time to search for possible correlations. All the features of a movie cannot be reliably annotated automatically: semantic features of a movie require manual annotations, which is in some occasions problematic due to the various cinematic techniques adopted. Understanding these methods helps analyzing and annotating movies. The movie Match Factory Girl (Aki Kaurismäki, 1990) was used as a stimulus in studying the brain in naturalistic settings. To help the analysis of the acquired data the salient visual features of the movie were annotated. In this work existing annotation approaches and available technologies for annotation were reviewed. Annotations help organizing information, therefore they are nowadays found everywhere. Different tools and technologies are being developed constantly. Furthermore, development of automatic video analysis methods are going to provide more meaningful annotations in the future

Development of advanced 3D medical analysis tools for clinical training, diagnosis and treatment

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The objective of this PhD research was the development of novel 3D interactive medical platforms for medical image analysis, simulation and visualisation, with a focus on oncology images to support clinicians in managing the increasing amount of data provided by several medical image modalities. DoctorEye and Automatic Tumour Detector platforms were developed through constant interaction and feedback from expert clinicians, integrating a number of innovations in algorithms and methods, concerning image handling, segmentation, annotation, visualisation and plug-in technologies. DoctorEye is already being used in a related tumour modelling EC project (ContraCancrum) and offers several robust algorithms and tools for fast annotation, 3D visualisation and measurements to assist the clinician in better understanding the pathology of the brain area and define the treatment. It is free to use upon request and offers a user friendly environment for clinicians as it simplifies the implementation of complex algorithms and methods. It integrates a sophisticated, simple-to-use plug-in technology allowing researchers to add algorithms and methods (e.g. tumour growth and simulation algorithms for improving therapy planning) and interactively check the results. Apart from diagnostic and research purposes, it supports clinical training as it allows an expert clinician to evaluate a clinical delineation by different clinical users. The Automatic Tumour Detector focuses on abdominal images, which are more complex than those of the brain. It supports full automatic 3D detection of kidney pathology in real-time as well as 3D advanced visualisation and measurements. This is achieved through an innovative method implementing Templates. They contain rules and parameters for the Automatic Recognition Framework defined interactively by engineers based on clinicians’ 3D Golden Standard models. The Templates enable the automatic detection of kidneys and their possible abnormalities (tumours, stones and cysts). The system also supports the transmission of these Templates to another expert for a second opinion. Future versions of the proposed platforms could integrate even more sophisticated algorithms and tools and offer fully computer-aided identification of a variety of other organs and their dysfunctions

Spatial description-based approach towards integration of biomedical atlases

Biomedical imaging has become ubiquitous in both basic research and the clinical sciences. As technology advances the resulting multitude of imaging modalities has led to a sharp rise in the quantity and quality of such images. Whether for epi- demiological studies, educational uses, clinical monitoring, or translational science purposes, the ability to integrate and compare such image-based data has become in- creasingly critical in the life sciences and eHealth domain. Ontology-based solutions often lack spatial precision. Image processing-based solutions may have di culties when the underlying morphologies are too di erent. This thesis proposes a compro- mise solution which captures location in biomedical images via spatial descriptions. Three approaches of spatial descriptions have been explored. These include: (1) spatial descriptions based on spatial relationships between segmented regions; (2) spatial descriptions based on ducial points and a set of spatial relations; and (3) spatial descriptions based on ducial points and a set of spatial relations, integrated with spatial relations between segmented regions. Evaluation, particularly in the context of mouse gene expression data, a good representative of spatio-temporal bi- ological data, suggests that the spatial description-based solution can provide good spatial precision. This dissertation discusses the need for biomedical image data in- tegration, the shortcomings of existing solutions and proposes new algorithms based on spatial descriptions of anatomical details in the image. Evaluation studies, par- ticularly in the context of gene expression data analysis, were carried out to study the performance of the new algorithms

Connectomics across development:towards mapping brain structure from birth to childhood

The brain is probably the most complex system of the human body, composed of numerous neural units interconnected at dierent scales. This highly structured architecture provides the ability to communicate, synthesize information and perform the analytical tasks of human beings. Its development starts during the transition between the embryonic and fetal periods, from a simple tubular to a highly complex folded structure. It is globally organized as early as birth. This developing process is highly vulnerable to antenatal adverse conditions. Indeed, extreme prematurity and intra uterine growth restriction are major risk factors for long-term morbidities, including developmental ailments such as cerebral palsy, mental retardation and a wide spectrum of learning disabilities and behavior disorders. In this context, the characterization of the brainâs normative wiring pattern is crucial for our understanding of its architecture and workings, as the origin of many neurological and neurobehavioral disorders is found in early structural brain development. Diusion magnetic resonance imaging (dMRI) allows the in vivo assessment of biological tissues at the microstructural level. It has emerged as a powerful tool to study brain connectivity and analyse the underlying substrate of the human brain, comprising its structurally integrated and functionally specialized architecture. dMRI has been widely used in adult studies. Nevertheless, due to technical constraints, this mapping at earlier stages of development has not yet been accomplished. Yet, this time period is of extreme importance to comprehend the structural and functional integrity of the brain. This thesis is motivated by this shortfall, and intends to fill the gap between the clinical and neuroscience demands and the methodological developments needed to fulfill them. In our work, we comprehensibly study the brain structural connectivity of children born extremely prematurely and/or with additional prenatal restriction at school-age. We provide evidence that brain systems that mature early in development are the most vulnerable to antenatal insults. Interestingly, the alterations highlighted in these systems correlate with the neurobehavioral and cognitive impairments seen in these children at school-age. The overall brain organization appear also altered after preterm birth and prenatal restriction. Indeed, these children show dierent brain network modular topology, with a reduction in the overall network capacity. What remains unclear is whether the alterations seen at school age are already present at birth and, if yes, to what extent. In this thesis we set the technical basis to enable the connectome analysis as early as at birth. This task is challenging when dealing with neonatal data. Indeed, most of the assumptions used in adult data processing methods do not hold, due to the inverted image contrast and other MRI artefacts such as motion, partial volume and intensity inhomogeneities. Here, we propose a novel technique for surface reconstruction, and provide a fully-automatic procedure to delineate the newborn cortical surface, opening the way to establish the newborn connectome

A Hybrid System for the Semantic Annotation of Sulco-Gyral Anatomy in MRI Images.

International audienceThis paper presents an interactive system for the annotation of brain anatomical structures in Magnetic Resonance Images. The system is based on hybrid knowledge and techniques. First, it exploits both numerical knowledge from atlases and symbolic knowledge from a ruleextended ontology represented in OWL, the Web ontology language, and combines them with graphical data about cortical sulci, automatically extracted from the images. Second, the annotations of the parts of gyri and of sulci located in a region of interest are obtained with different reasoning techniques: Constraint Satisfaction Solving and Description Logics techniques. Preliminary experiments have been achieved on normal and also pathological data. The results obtained so far are very promising