Search

Automated screening systems in conjunction with machine learning-based methods are becoming an essential part of the healthcare systems for assisting in disease diagnosis. Moreover, manually annotating data and hand-crafting features for training purposes are impractical and time-consuming. We propose a segmentation and classification-based approach for assembling an automated screening system for the analysis of calcium imaging. The method was developed and verified using the effects of disease IgGs (from Amyotrophic Lateral Sclerosis patients) on calcium (Ca2+) homeostasis. From 33 imaging videos we analyzed, 21 belonged to the disease and 12 to the control experimental groups. The method consists of three main steps: projection, segmentation, and classification. The entire Ca2+ time-lapse image recordings (videos) were projected into a single image using different projection methods. Segmentation was performed by using a multi-level thresholding (MLT) step and the Regions of Interest (ROIs) that encompassed cell somas were detected. A mean value of the pixels within these boundaries was collected at each time point to obtain the Ca2+ traces (time-series). Finally, a new matrix called feature image was generated from those traces and used for assessing the classification accuracy of various classifiers (control vs. disease). The mean value of the segmentation F-score for all the data was above 0.80 throughout the tested threshold levels for all projection methods, namely maximum intensity, standard deviation, and standard deviation with linear scaling projection. Although the classification accuracy reached up to 90.14%, interestingly, we observed that achieving better scores in segmentation results did not necessarily correspond to an increase in classification performance. Our method takes the advantage of the multi-level thresholding and of a classification procedure based on the feature images, thus it does not have to rely on hand-crafted training parameters of each event. It thus provides a semi-autonomous tool for assessing segmentation parameters which allows for the best classification accuracy. [ABSTRACT FROM AUTHOR]

Copyright of ITU Journal Series D: Engineering is the property of Istanbul Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents two shape descriptors which could be applied to both binary and grayscale images. The proposed algorithm utilizes gradient based features which are extracted along the object boundaries. We use two-dimensional steerable G-Filters (IEEE Trans Pattern Anal Mach Intell 19(6):545–563, 1997) to obtain gradient information at different orientations and scales, and then aggregate the gradients into a shape signature. The signature derived from the rotated object is circularly shifted version of the signature derived from the original object. This property is called the circular-shifting rule (Affine-invariant gradient based shape descriptor. Lecture notes in computer science. International workshop on multimedia contents Representation, Classification and Security, pp 514–521, 2006). The shape descriptor is defined as the Fourier transform of the signature. We also provide a distance measure for the proposed descriptor by taking the circular-shifting rule into account. The performance of the proposed descriptor is evaluated over two databases; one containing digits taken from vehicle license plates and the other containing MPEG-7 Core Experiment and Kimia shape data set. The experiments show that the devised method outperforms other well-known Fourier-based shape descriptors such as centroid distance and boundary curvature. [ABSTRACT FROM AUTHOR]

Objective: Myelin is essential for a healthy nervous system. Myelin formed by oligodendrocytes, accelerates impulse propagation and supports neuronal survival. Demyelination leads to neurodegeneration. In multiple sclerosis (MS) immune attack results in demyelination. Our goal is to dissect interactions among oligodendrocytes, neurons, and immune cells to improve our understanding of myelination and the demyelinating diseases. We aim to discover new targets for remyelination therapies. Methods: We are building tools for analyzing protein-protein and cell-to-cell interactions. To identify genes involved in myelination and MS, we developed a bioinformatics-based strategy, interactome analysis, which combines proteome and gene expression methodologies. Identified genes are evaluated in peripheral blood of MS patients and in mouse models. To accelerate drug discovery, 23 machine learning-based methodologies were assessed for myelin identification in fluorescent microscopy images. Results: Interactome analysis identified hundreds of proteinprotein interactions between pairs of oligodendrocytes, neurons, macrophages, microglia, and T cells. Most significant interactions are further analyzed in vivo and in vitro. Our customizedconvolutional neural network and Boosted Tree methods segmented myelin at over 98% accuracy. Identified myelin can be quantified and visualized in 3D. Conclusion: The interactome analysis yielded novel genes that are likely to be linked to MS. Machine learning-based methodologies are very effective in accelerating myelin quantification and thus, drug screens against demyelinating diseases such as MS. Overall, our innovative analysis strategies employing computer assistance produced novel avenues of exploration for myelination and demyelinating diseases. This study was supported TUBITAK (218S495,316S026), Istanbul Medipol University (BAP2018/06), and Turkish Academy of Sciences (GEBIP). [ABSTRACT FROM AUTHOR]