Improved detection of soma location and morphology in fluorescence microscopy images of neurons

Kayasandık, Cihan Bilge; Labate, Demetrio

doi:10.1016/j.jneumeth.2016.09.007

Cited by 26 publications

(24 citation statements)

References 31 publications

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“…To avoid this issue, in this paper we apply a new approach to automatically detect astrocytes based on the Directional Ratio, a multiscale geometric descriptor that estimates the probable location of cell bodies in an image by measuring local anisotropy over multiple scales 21,22 . This method is purely morphology-based, does not require any nucleus marker and was originally developed by the authors to detect somas in fluorescent images of neurons 23 .…”

Section: Methodsmentioning

confidence: 99%

A multistep deep learning framework for the automated detection and segmentation of astrocytes in fluorescent images of brain tissue

Kayasandık

Labate

2020

Sci Rep

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While astrocytes have been traditionally described as passive supportive cells, studies during the last decade have shown they are active players in many aspects of cnS physiology and function both in normal and disease states. However, the precise mechanisms regulating astrocytes function and interactions within the cnS are still poorly understood. this knowledge gap is due in large part to the limitations of current image analysis tools that cannot process astrocyte images efficiently and to the lack of methods capable of quantifying their complex morphological characteristics. to provide an unbiased and accurate framework for the quantitative analysis of fluorescent images of astrocytes, we introduce a new automated image processing pipeline whose main novelties include an innovative module for cell detection based on multiscale directional filters and a segmentation routine that leverages deep learning and sparse representations to reduce the need of training data and improve performance. extensive numerical tests show that our method performs very competitively with respect to state-of-the-art methods also in challenging images where astrocytes are clustered together. Our code is released open source and freely available to the scientific community.The human brain is a complex network of over 2 × 10 11 neural cells comprising neurons and glial cells. Neuroglia comprise astrocytes, oligodendrocytes, NG2 glia, microglia and all peripheral glia 1 . Astrocytes, a subtype of glial cells with a complex star-shaped morphology, are the most abundant cells in most part of the human brain 2 . Although they were long characterized as supportive cells only involved in maintaining neuron homeostasis and survival, a number of studies during the last decade have revealed that astrocytes play an active role in fundamental brain processes underlying neuronal development and function. Several studies have implicated astrocytes in controlling the development of the nervous system through axon guidance and synaptogenesis. During neural circuit development, astrocytes are responsible for the regularization of the neuronal network by pruning abnormal or dysfunctioning synapses 3 . In addition, together with the pre-and post-synaptic parts of two neuronal synapses, astrocytes can form a so-called "tripartite" synapse that helps modulate synaptic transmission via the release of neurotransmitters such as glutamate and ATP 4,5 . The critical roles of astrocytes in neuronal development and connectivity make them among the most promising targets for innovative therapies designed to treat a range of brain disorders or neurological injuries 6 . As a result, the attention on astrocytes has dramatically increased in recent years.Astrocytes have been shown to reflect their diverse abilities and functions on their special structural design, and alterations in astrocyte morphology are known to correlate to traumatic brain injury, infection, ischemia, autoimmune responses, and neurodegenerative diseases 5,7,8 . For instance, their intricate ar...

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Section: Methodsmentioning

confidence: 99%

A multistep deep learning framework for the automated detection and segmentation of astrocytes in fluorescent images of brain tissue

Kayasandık

Labate

2020

Sci Rep

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“…However this approach is not very effective when applied to fluorescent images since high intensity values are commonly found also outside somas. Therefore, in this paper, we apply a more sophisticated approach based on Directional Ratio, a multiscale geometric descriptor recently introduced by some of the authors in a prior work to overcome the limitations of conventional algorithms 26 , 29 , 30 . This method employs a collection of directional filters to compute, for each point in the image, a numerical score measuring the level of local anisotropy at a given scale.…”

Section: Methodsmentioning

confidence: 99%

“…This method employs a collection of directional filters to compute, for each point in the image, a numerical score measuring the level of local anisotropy at a given scale. As shown in 26 , 30 , the application of this method is extremely effective to detect soma locations and, used in combination with the level set method or the fast marching approach, allows one to accurately and efficiently separate somas from neurites.…”

Section: Methodsmentioning

confidence: 99%

Automated sorting of neuronal trees in fluorescent images of neuronal networks using NeuroTreeTracer

Kayasandık

Negi

Laezza

et al. 2018

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Fluorescence confocal microscopy has become increasingly more important in neuroscience due to its applications in image-based screening and profiling of neurons. Multispectral confocal imaging is useful to simultaneously probe for distribution of multiple analytes over networks of neurons. However, current automated image analysis algorithms are not designed to extract single-neuron arbors in images where neurons are not separated, hampering the ability map fluorescence signals at the single cell level. To overcome this limitation, we introduce NeuroTreeTracer – a novel image processing framework aimed at automatically extracting and sorting single-neuron traces in fluorescent images of multicellular neuronal networks. This method applies directional multiscale filters for automated segmentation of neurons and soma detection, and includes a novel tracing routine that sorts neuronal trees in the image by resolving network connectivity even when neurites appear to intersect. By extracting each neuronal tree, NeuroTreetracer enables to automatically quantify the spatial distribution of analytes of interest in the subcellular compartments of individual neurons. This software is released open-source and freely available with the goal to facilitate applications in neuron screening and profiling.

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“…Oschatz et al proposed a morphological analysis method for transmitting electron microscopy images combined with physisorption and Raman spectroscopy [20]. Moreover, Kayasandik and Labate improved detection of soma location and morphology in fluorescence microscopy images of neurons [21]. Furthermore, Non-Alcoholic Steatohepatitis (NASH) has been used as a common liver disorder for detecting morphological changes [22].…”

Section: Introductionmentioning

confidence: 99%

Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images

Wang

Shi

Cao

et al. 2019

CBIO

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Background: To reduce the intensity of the work of doctors, pre-classification work needs to be issued. In this paper, a novel and related liver microscopic image classification analysis method is proposed. Objective: For quantitative analysis, segmentation is carried out to extract the quantitative information of special organisms in the image for further diagnosis, lesion localization, learning and treating anatomical abnormalities and computer-guided surgery. </P><P> Methods: In the current work, entropy-based features of microscopic fibrosis mice’ liver images were analyzed using fuzzy c-cluster, k-means and watershed algorithms based on distance transformations and gradient. A morphological segmentation based on a local threshold was deployed to determine the fibrosis areas of images. Results: The segmented target region using the proposed method achieved high effective microscopy fibrosis images segmenting of mice liver in terms of the running time, dice ratio and precision. The image classification experiments were conducted using Gray Level Co-occurrence Matrix (GLCM). The best classification model derived from the established characteristics was GLCM which performed the highest accuracy of classification using a developed Support Vector Machine (SVM). The training model using 11 features was found to be accurate when only trained by 8 GLCMs. Conclusion: The research illustrated that the proposed method is a new feasible research approach for microscopy mice liver image segmentation and classification using intelligent image analysis techniques. It is also reported that the average computational time of the proposed approach was only 2.335 seconds, which outperformed other segmentation algorithms with 0.8125 dice ratio and 0.5253 precision.</P>

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Improved detection of soma location and morphology in fluorescence microscopy images of neurons

Cited by 26 publications

References 31 publications

A multistep deep learning framework for the automated detection and segmentation of astrocytes in fluorescent images of brain tissue

A multistep deep learning framework for the automated detection and segmentation of astrocytes in fluorescent images of brain tissue

Automated sorting of neuronal trees in fluorescent images of neuronal networks using NeuroTreeTracer

Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images

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