Visual Search of Neuropil-Enriched RNAs from Brain In Situ Hybridization Data through the Image Analysis Pipeline Hippo-ATESC

International audienceMotivation: RNA molecules specifically enriched in the neuropil of neuronal cells and in particular in dendritic spines are of great interest for neurobiology in virtue of their involvement in synaptic structure and plasticity. The systematic recognition of such molecules is therefore a very important task. High resolution images of RNA in situ hybridization experiments contained in the Allen Brain Atlas (ABA) represent a very rich resource to identify them and have been so far exploited for this task through human-expert analysis. However, software tools that may automatically address the same objective are not very well developed. Results: In this study we describe an automatic method for exploring in situ hybridization data and discover neuropil-enriched RNAs in the mouse hippocampus. We called it Hippo-ATESC (Automatic Texture Extraction from the Hippocampal region using Soft Computing). Bioinformatic validation showed that the Hippo-ATESC is very efficient in the recognition of RNAs which are manually identified by expert curators as neuropil-enriched on the same image series. Moreover, we show that our method can also highlight genes revealed by microdissection-based methods but missed by human visual inspection. We experimentally validated our approach by identifying a non-coding transcript enriched in mouse synaptosomes. The code is freely available on the web at http://ibislab.ce.unipr.it/software/hippo/

Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images

Meta-optimization of Bio-inspired Techniques for Object Recognition

Il riconoscimento di oggetti consiste nel trovare automaticamente un oggetto all'interno di un'immagine o in una sequenza video. Questo compito è molto importante in molti campi quali diagnosi mediche, assistenza di guida avanzata, visione artificiale, sorveglianza, realtà aumentata. Tuttavia, questo compito può essere molto impegnativo a causa di artefatti (dovuti al sistema di acquisizione, all'ambiente o ad altri effetti ottici quali prospettiva, variazioni di illuminazione, etc.) che possono influenzare l'aspetto anche di oggetti facili da identificare e ben definiti . Una possibile tecnica per il riconoscimento di oggetti consiste nell'utilizzare approcci basati su modello: in questo scenario viene creato un modello che rappresenta le proprietà dell'oggetto da individuare; poi, vengono generate possibili ipotesi sul posizionamento dell'oggetto, e il modello viene trasformato di conseguenza, fino a trovare la migliore corrispondenza con l'aspetto reale dell'oggetto. Per generare queste ipotesi in maniera intelligente, è necessario un buon algoritmo di ottimizzazione. Gli algoritmi di tipo bio-ispirati sono metodi di ottimizzazione che si basano su proprietà osservate in natura (quali cooperazione, evoluzione, socialità). La loro efficacia è stata dimostrata in molte attività di ottimizzazione, soprattutto in problemi di difficile soluzione, multi-modali e multi-dimensionali quali, per l'appunto, il riconoscimento di oggetti. Anche se queste euristiche sono generalmente efficaci, esse dipendono da molti parametri che influenzano profondamente le loro prestazioni; pertanto, è spesso richiesto uno sforzo significativo per capire come farle esprimere al massimo delle loro potenzialità. Questa tesi descrive un metodo per (i) individuare automaticamente buoni parametri per tecniche bio-ispirate, sia per un problema specifico che più di uno alla volta, e (ii) acquisire maggior conoscenza sul ruolo di un parametro in questi algoritmi. Inoltre, viene mostrato come le tecniche bio-ispirate possono essere applicate con successo in diversi ambiti nel riconoscimento di oggetti, e come è possibile migliorare ulteriormente le loro prestazioni mediante il tuning automatico dei loro parametri.Object recognition is the task of automatically finding a given object in an image or in a video sequence. This task is very important in many fields such as medical diagnosis, advanced driving assistance, image understanding, surveillance, virtual reality. Nevertheless, this task can be very challenging because of artefacts (related with the acquisition system, the environment or other optical effects like perspective, illumination changes, etc.) which may affect the aspect even of easy-to-identify and well-defined objects. A possible way to achieve object recognition is using model-based approaches: in this scenario a model (also called template) representing the properties of the target object is created; then, hypotheses on the position of the object are generated, and the model is transformed accordingly, until the best match with the actual appearance of the object is found. To generate these hypotheses intelligently, a good optimization algorithm is required. Bio-inspired techniques are optimization methods whose foundations rely on properties observed in nature (such as cooperation, evolution, emergence). Their effectiveness has been proved in many optimization tasks, especially in multi-modal, multi-dimensional hard problems like object recognition. Although these heuristics are generally effective, they depend on many parameters that strongly affect their performances; therefore, a significant effort must be spent to understand how to let them express their full potentialities. This thesis describes a method to (i) automatically find good parameters for bio-inspired techniques, both for a specific problem and for more than one at the same time, and (ii) acquire more knowledge of a parameter's role in such algorithms. Then, it shows how bio-inspired techniques can be successfully applied to different object recognition tasks, and how it is possible to further improve their performances by means of automatic parameter tuning

Computer assisted enhanced volumetric segmentation magnetic imaging data using a mixture of artificial neural networks

An accurate computer-assisted method able to perform regional segmentation on 3D single modality images and measure its volume is designed using a mixture of unsupervised and supervised artificial neural networks. Firstly, an unsupervised artificial neural network is used to estimate representative textures that appear in the images. The region of interest of the resultant images is selected by means of a multi-layer perceptron after a training using a single sample slice, which contains a central portion of the 3D region of interest. The method was applied to magnetic resonance imaging data collected from an experimental acute inflammatory model (T(2) weighted) and from a clinical study of human Alzheimer's disease (T(1) weighted) to evaluate the proposed method. In the first case, a high correlation and parallelism was registered between the volumetric measurements, of the injured and healthy tissue, by the proposed method with respect to the manual measurements (r = 0.82 and p < 0.05) and to the histopathological studies (r = 0.87 and p < 0.05). The method was also applied to the clinical studies, and similar results were derived of the manual and semi-automatic volumetric measurement of both hippocampus and the corpus callosum (0.95 and 0.88

Image similarity in medical images

Recent experiments have indicated a strong influence of the substrate grain orientation on the self-ordering in anodic porous alumina. Anodic porous alumina with straight pore channels grown in a stable, self-ordered manner is formed on (001) oriented Al grain, while disordered porous pattern is formed on (101) oriented Al grain with tilted pore channels growing in an unstable manner. In this work, numerical simulation of the pore growth process is carried out to understand this phenomenon. The rate-determining step of the oxide growth is assumed to be the Cabrera-Mott barrier at the oxide/electrolyte (o/e) interface, while the substrate is assumed to determine the ratio β between the ionization and oxidation reactions at the metal/oxide (m/o) interface. By numerically solving the electric field inside a growing porous alumina during anodization, the migration rates of the ions and hence the evolution of the o/e and m/o interfaces are computed. The simulated results show that pore growth is more stable when β is higher. A higher β corresponds to more Al ionized and migrating away from the m/o interface rather than being oxidized, and hence a higher retained O:Al ratio in the oxide. Experimentally measured oxygen content in the self-ordered porous alumina on (001) Al is indeed found to be about 3% higher than that in the disordered alumina on (101) Al, in agreement with the theoretical prediction. The results, therefore, suggest that ionization on (001) Al substrate is relatively easier than on (101) Al, and this leads to the more stable growth of the pore channels on (001) Al

Computational Unfolding of the Human Hippocampus

The hippocampal subfields are defined by their unique cytoarchitectures, which many recent studies have tried to map to human in-vivo MRI because of their promise to further our understanding of hippocampal function, or its dysfunction in disease. However, recent anatomical literature has highlighted broad inter-individual variability in hippocampal morphology and subfield locations, much of which can be attributed to different folding configurations within hippocampal (or archicortical) tissue. Inspired in part by analogous surface-based neocortical analysis methods, the current thesis aimed to develop a standardized coordinate framework, or surface-based method, that respects the topology of all hippocampal folding configurations. I developed such a coordinate framework in Chapter 2, which was initialized by detailed manual segmentations of hippocampal grey matter and high myelin laminae which are visible in 7-Tesla MRI and which separate different hippocampal folds. This framework was leveraged to i) computationally unfold the hippocampus which provided implicit topological inter-individual alignment, ii) delineate subfields with high reliability and validity, and iii) extract novel structural features of hippocampal grey matter. In Chapter 3, I applied this coordinate framework to the open source BigBrain 3D histology dataset. With this framework, I computationally extracted morphological and laminar features and showed that they are sufficient to derive hippocampal subfields in a data-driven manner. This underscores the sensitivity of these computational measures and the validity of the applied subfield definitions. Finally, the unfolding coordinate framework developed in Chapter 2 and extended in Chapter 3 requires manual detection of different tissue classes that separate folds in hippocampal grey matter. This is costly in the time and the expertise required. Thus, in Chapter 4, I applied state-of-the-art deep learning methods in the open source Human Connectome Project MRI dataset to automate this process. This allowed for scalable application of the methods described in Chapters 2, 3, and 4 to similar new datasets, with support for extensions to suit data of different modalities or resolutions. Overall, the projects presented here provide multifaceted evidence for the strengths of a surface-based approach to hippocampal analysis as developed in this thesis, and these methods are readily deployable in new neuroimaging work

Studying neuroanatomy using MRI

The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging, and disease. Developments in MRI acquisition, image processing, and data modelling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and inferring microstructural properties; we also describe key artefacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, though methods need to improve and caution is required in its interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works

A novel NMF-based DWI CAD framework for prostate cancer.

In this thesis, a computer aided diagnostic (CAD) framework for detecting prostate cancer in DWI data is proposed. The proposed CAD method consists of two frameworks that use nonnegative matrix factorization (NMF) to learn meaningful features from sets of high-dimensional data. The first technique, is a three dimensional (3D) level-set DWI prostate segmentation algorithm guided by a novel probabilistic speed function. This speed function is driven by the features learned by NMF from 3D appearance, shape, and spatial data. The second technique, is a probabilistic classifier that seeks to label a prostate segmented from DWI data as either alignat, contain cancer, or benign, containing no cancer. This approach uses a NMF-based feature fusion to create a feature space where data classes are clustered. In addition, using DWI data acquired at a wide range of b-values (i.e. magnetic field strengths) is investigated. Experimental analysis indicates that for both of these frameworks, using NMF producing more accurate segmentation and classification results, respectively, and that combining the information from DWI data at several b-values can assist in detecting prostate cancer

An Artificial Intelligence Approach to Tumor Volume Delineation

Postponed access: the file will be accessible after 2023-11-14Masteroppgave for radiograf/bioingeniørRABD395MAMD-HELS