Convolutional neural networks for the segmentation of small rodent brain MRI

Image segmentation is a common step in the analysis of preclinical brain MRI, often performed manually. This is a time-consuming procedure subject to inter- and intra- rater variability. A possible alternative is the use of automated, registration-based segmentation, which suffers from a bias owed to the limited capacity of registration to adapt to pathological conditions such as Traumatic Brain Injury (TBI). In this work a novel method is developed for the segmentation of small rodent brain MRI based on Convolutional Neural Networks (CNNs). The experiments here presented show how CNNs provide a fast, robust and accurate alternative to both manual and registration-based methods. This is demonstrated by accurately segmenting three large datasets of MRI scans of healthy and Huntington disease model mice, as well as TBI rats. MU-Net and MU-Net-R, the CCNs here presented, achieve human-level accuracy while eliminating intra-rater variability, alleviating the biases of registration-based segmentation, and with an inference time of less than one second per scan. Using these segmentation masks I designed a geometric construction to extract 39 parameters describing the position and orientation of the hippocampus, and later used them to classify epileptic vs. non-epileptic rats with a balanced accuracy of 0.80, five months after TBI. This clinically transferable geometric approach detects subjects at high-risk of post-traumatic epilepsy, paving the way towards subject stratification for antiepileptogenesis studies

A survey on artificial intelligence in histopathology image analysis

The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning-based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field

Diffusion Tensor Imaging Biomarkers of Brain Development and Disease

<p>Understanding the structure of the brain has been a major goal of neuroscience research over the past century, driven in part by the understanding that brain structure closely follows function. Normative brain maps, or atlases, can be used to understand normal brain structure, and to identify structural differences resulting from disease. Recently, diffusion tensor magnetic resonance imaging has emerged as a powerful tool for brain atlasing; however, its utility is hindered by image resolution and signal limitations. These limitations can be overcome by imaging fixed ex-vivo specimens stained with MRI contrast agents, a technique known as diffusion tensor magnetic resonance histology (DT-MRH). DT-MRH represents a unique, quantitative tool for mapping the brain with unprecedented structural detail. This technique has engendered a new generation of 3D, digital brain atlases, capable of representing complex dynamic processes such as neurodevelopment. This dissertation explores the use of DT-MRH for quantitative brain atlasing in an animal model and initial work in the human brain. </p><p>Chapter 1 describes the advantages of the DT-MRH technique, and the motivations for generating a quantitative atlas of rat postnatal neurodevelopment. The second chapter covers optimization of the DT-MRH hardware and pulse sequence design for imaging the developing rat brain. Chapter 3 details the acquisition and curation of rat neurodevelopmental atlas data. Chapter 4 describes the creation and implementation of an ontology-based segmentation scheme for tracking changes in the developing brain. Chapters 5 and 6 pertain to analyses of volumetric changes and diffusion tensor parameter changes throughout rat postnatal neurodevelopment, respectively. Together, the first six chapters demonstrate many of the unique and scientifically valuable features of DT-MRH brain atlases in a popular animal model.</p><p>The final two chapters are concerned with translating the DT-MRH technique for use in human and non-human primate brain atlasing. Chapter 7 explores the validity of assumptions imposed by DT-MRH in the primate brain. Specifically, it analyzes computer models and experimental data to determine the extent to which intravoxel diffusion complexity exists in the rhesus macaque brain, a close model for the human brain. Finally, Chapter 8 presents conclusions and future directions for DT-MRH brain atlasing, and includes initial work in creating DT-MRH atlases of the human brain. In conclusion, this work demonstrates the utility of a DT-MRH brain atlasing with an atlas of rat postnatal neurodevelopment, and lays the foundation for creating a DT-MRH atlas of the human brain.</p>Dissertatio

Whole-brain cortical parcellation: A hierarchical method based on dMRI tractography

In den modernen Neurowissenschaften ist allgemein anerkannt, dass die Gehirnfunktionen auf dem Zusammenwirken von verschiedenen Regionen in Netzwerken beruhen und die strukturelle Konnektivität daher großer Bedeutung ist. Daher kann die Abgrenzung funktioneller Hirnbereiche auf der Grundlage der Diffusions-Magnet-Resonanz-Tomographie (dMRT) und der Traktografie zu wertvollen Hirnkarten führen.Existierende Verfahren versuchen eine fest vorgegebene Anzahl von Regionen zu finden und/oder sind auf kleine Bereiche der grauen Substanz beschränkt. Im Allgemeinen ist es jedoch unwahrscheinlich, dass eine einzelne Parzellierung des Kortex, eine ausreichende Darstellung der funktio- anatomischen Organisation des Gehirns erlaubt. In dieser Arbeit schlagen wir eine hierarchische Clusteranalyse vor um diese Einschränkungen zu überwinden und das gesamte Gehirn zu parzellieren. Wir zeigen, dass dieses Verfahren die Eigenschaften der zugrundeliegenden Struktur auf allen Granularitätstufen des hierarchischen Baums (Dendrogramm) kodieren kann. Weiterhin entwickeln wir eine optimale Verarbeitungspipeline zur Erstellung dieses Baums, die dessen Komplexität mit minimalem Informationsverlust reduziert. Wir zeigen wie diese Datenstrukturen verwendet werden können um die Ähnlichkeitstruktur von verschiedenen Probanden oder Messungen zu vergleichen und wie man daraus verschiedene Parzellierungen des Gehirns erhalten kann.Unser neuer Ansatz liefert eine ausführlichere Analyse der anatomischen Strukturen und bietet eine Methode zur Parzellierung des ganzen Gehirns.In modern neuroscience there is general agreement that brain function relies on networks and that connectivity is therefore of paramount importance for brain function. Accordingly, the delineation of functional brain areas on the basis of diffusion magnetic resonance imaging (dMRI) and tractography may lead to highly relevant brain maps.Existing methods typically aim to find a predefined number of areas and/or are limited to small regions of grey matter. However, it is in general not likely that a single parcellation dividing the brain into a finite number of areas is an adequate representation of the function-anatomical organization of the brain. In this work, we propose hierarchical clustering as a solution to overcome these limitations and achieve whole-brain parcellation. We demonstrate that this method encodes the information of the underlying structure at all granularity levels in a hierarchical tree or dendrogram. We develop an optimal tree building and processing pipeline that reduces the complexity of the tree with minimal information loss. We show how these trees can be used to compare the similarity structure of different subjects or recordings and how to extract parcellations from them.Our novel approach yields a more exhaustive representation of the real underlying structure and successfully tackles the challenge of whole-brain parcellation

Libro de actas. XXXV Congreso Anual de la Sociedad Española de Ingeniería Biomédica

596 p.CASEIB2017 vuelve a ser el foro de referencia a nivel nacional para el intercambio científico de conocimiento, experiencias y promoción de la I D i en Ingeniería Biomédica. Un punto de encuentro de científicos, profesionales de la industria, ingenieros biomédicos y profesionales clínicos interesados en las últimas novedades en investigación, educación y aplicación industrial y clínica de la ingeniería biomédica. En la presente edición, más de 160 trabajos de alto nivel científico serán presentados en áreas relevantes de la ingeniería biomédica, tales como: procesado de señal e imagen, instrumentación biomédica, telemedicina, modelado de sistemas biomédicos, sistemas inteligentes y sensores, robótica, planificación y simulación quirúrgica, biofotónica y biomateriales. Cabe destacar las sesiones dedicadas a la competición por el Premio José María Ferrero Corral, y la sesión de competición de alumnos de Grado en Ingeniería biomédica, que persiguen fomentar la participación de jóvenes estudiantes e investigadores

[¹⁸F]fluorination of biorelevant arylboronic acid pinacol ester scaffolds synthesized by convergence techniques

Aim: The development of small molecules through convergent multicomponent reactions (MCR) has been boosted during the last decade due to the ability to synthesize, virtually without any side-products, numerous small drug-like molecules with several degrees of structural diversity.(1) The association of positron emission tomography (PET) labeling techniques in line with the “one-pot” development of biologically active compounds has the potential to become relevant not only for the evaluation and characterization of those MCR products through molecular imaging, but also to increase the library of radiotracers available. Therefore, since the [18F]fluorination of arylboronic acid pinacol ester derivatives tolerates electron-poor and electro-rich arenes and various functional groups,(2) the main goal of this research work was to achieve the 18F-radiolabeling of several different molecules synthesized through MCR. Materials and Methods: [18F]Fluorination of boronic acid pinacol esters was first extensively optimized using a benzaldehyde derivative in relation to the ideal amount of Cu(II) catalyst and precursor to be used, as well as the reaction solvent. Radiochemical conversion (RCC) yields were assessed by TLC-SG. The optimized radiolabeling conditions were subsequently applied to several structurally different MCR scaffolds comprising biologically relevant pharmacophores (e.g. β-lactam, morpholine, tetrazole, oxazole) that were synthesized to specifically contain a boronic acid pinacol ester group. Results: Radiolabeling with fluorine-18 was achieved with volumes (800 μl) and activities (≤ 2 GBq) compatible with most radiochemistry techniques and modules. In summary, an increase in the quantities of precursor or Cu(II) catalyst lead to higher conversion yields. An optimal amount of precursor (0.06 mmol) and Cu(OTf)2(py)4 (0.04 mmol) was defined for further reactions, with DMA being a preferential solvent over DMF. RCC yields from 15% to 76%, depending on the scaffold, were reproducibly achieved. Interestingly, it was noticed that the structure of the scaffolds, beyond the arylboronic acid, exerts some influence in the final RCC, with electron-withdrawing groups in the para position apparently enhancing the radiolabeling yield. Conclusion: The developed method with high RCC and reproducibility has the potential to be applied in line with MCR and also has a possibility to be incorporated in a later stage of this convergent “one-pot” synthesis strategy. Further studies are currently ongoing to apply this radiolabeling concept to fluorine-containing approved drugs whose boronic acid pinacol ester precursors can be synthesized through MCR (e.g. atorvastatin)