scholarly journals Smart Region-Growing: a novel algorithm for the segmentation of 3D clarified confocal image stacks

2018 ◽  
Author(s):  
Alejandro Luis Callara ◽  
Chiara Magliaro ◽  
Arti Ahluwalia ◽  
Nicola Vanello
Keyword(s):  
Region Growing ◽  
Ground Truth ◽  
Complete Information ◽  
Confocal Imaging ◽  
Supplementary Information ◽  
Manual Segmentation ◽  
Confocal Image ◽  
Pixel Intensity ◽  
The Brain ◽  
Novel Algorithm

AbstractMotivationAccurately mapping the brain at the micro-scale is still a challenge in cellular neuroscience. While notable success has been reached in the field of tissue clarification and confocal imaging to obtain high-fidelity maps of three-dimensional neuron organization, neuron segmentation is still far away of ground-truth and manual segmentation performed by experts may be needed. The need of an expert is in part related to the limited success of the algorithms and tools performing single-neuron segmentation from 3D microscopic image data available in the State of Art, in part to the non-complete information given by these methods, which typically perform neuron tracing and thus limit the interpret-ability of results.ResultsIn this paper, a novel algorithm for segmenting single neurons in their own arrangement within the brain is presented. The algorithm performs a region growing procedure with local thresholds based on the pixel intensity statistics typical of confocal acquisitions of biological samples and described by a mixture model. The algorithm is developed and tested on 3D confocal datasets obtained from clarified tissues. We compare the result of our algorithm with those obtained by manual segmentation performed by 6 different experts in terms of neuron surface area, volume and Sholl profiles. Statistical analysis performed on morphologic features extracted from the segmented structures confirms the feasibility of our approach.AvailabilityThe Smart Region Growing (SmRG) algorithm used in the analysis along with test confocal image stacks is available on request to the [email protected] informationSupplementary data are available on request to the authors.

ADAPTIVE REGION GROWING FOR SKULL, BRAIN, AND SCALP SEGMENTATION FROM 3D MRI

2019 ◽  
Vol 31 (05) ◽  
pp. 1950033
Author(s):  
Tran Anh Tuan ◽  
Jin Young Kim ◽  
Pham The Bao
Keyword(s):  
Nervous System ◽  
Automatic Segmentation ◽  
Region Growing ◽  
Ground Truth ◽  
Resonance Imaging ◽  
3D Mri ◽  
Histogram Method ◽  
Ground Truth Data ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Many of the diseases that are related to the nervous system are serious and can lead to pain, headaches, and many other symptoms. Magnetic resonance imaging (MRI), which produces detailed medical images for the diagnosis of nervous system-related diseases, offers an advanced approach to the study of these diseases. In this paper, a method for the automatic segmentation of the skull, scalp, and brain of 3D MRI imagery is proposed to help doctors diagnose numerous injuries and diseases. For the proposed method, the 3D MRI dataset was first transformed to 2D axial slices, which were grouped based on the skull shape. Second, the histogram method was used to compute the fitting rectangle boundary around the object. Third, the segmentation of the skull was performed using the proposed adaptive region growing method, depending on the slice groups. Finally, the brain and the scalp were segmented. The proposed method was tested and validated on 20 subjects from the BrainWeb database and seven adults from the Neurodevelopmental MRI database with the use of the tissues for the ground truth data. The performance of the proposed method was evaluated using the Dice percentage and a comparison with the other methods.

Brain FDG PET for the Etiological Diagnosis of Clinically Uncertain Cognitive Impairment During Delirium in Remission

2020 ◽  
Vol 77 (4) ◽  
pp. 1609-1622
Author(s):  
Franziska Mathies ◽  
Catharina Lange ◽  
Anja Mäurer ◽  
Ivayla Apostolova ◽  
Susanne Klutmann ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Fdg Pet ◽  
False Negative ◽  
Ground Truth ◽  
Etiological Diagnosis ◽  
Geriatric Unit ◽  
Positron Emission ◽  
The Brain ◽  
Hospitalized Geriatric Patients

Background: Positron emission tomography (PET) of the brain with 2-[F-18]-fluoro-2-deoxy-D-glucose (FDG) is widely used for the etiological diagnosis of clinically uncertain cognitive impairment (CUCI). Acute full-blown delirium can cause reversible alterations of FDG uptake that mimic neurodegenerative disease. Objective: This study tested whether delirium in remission affects the performance of FDG PET for differentiation between neurodegenerative and non-neurodegenerative etiology of CUCI. Methods: The study included 88 patients (82.0±5.7 y) with newly detected CUCI during hospitalization in a geriatric unit. Twenty-seven (31%) of the patients were diagnosed with delirium during their current hospital stay, which, however, at time of enrollment was in remission so that delirium was not considered the primary cause of the CUCI. Cases were categorized as neurodegenerative or non-neurodegenerative etiology based on visual inspection of FDG PET. The diagnosis at clinical follow-up after ≥12 months served as ground truth to evaluate the diagnostic performance of FDG PET. Results: FDG PET was categorized as neurodegenerative in 51 (58%) of the patients. Follow-up after 16±3 months was obtained in 68 (77%) of the patients. The clinical follow-up diagnosis confirmed the FDG PET-based categorization in 60 patients (88%, 4 false negative and 4 false positive cases with respect to detection of neurodegeneration). The fraction of correct PET-based categorization did not differ between patients with delirium in remission and patients without delirium (86% versus 89%, p = 0.666). Conclusion: Brain FDG PET is useful for the etiological diagnosis of CUCI in hospitalized geriatric patients, as well as in patients with delirium in remission.

scholarly journals Is There a Brain Microbiome?

2021 ◽  
Vol 16 ◽  
pp. 263310552110187
Author(s):  
Christopher D Link
Keyword(s):  
Animal Models ◽  
Human Brain ◽  
Neurodegenerative Diseases ◽  
Ground Truth ◽  
Healthy Human ◽  
Strong Motivation ◽  
The Many ◽  
The Brain

Numerous studies have identified microbial sequences or epitopes in pathological and non-pathological human brain samples. It has not been resolved if these observations are artifactual, or truly represent population of the brain by microbes. Given the tempting speculation that resident microbes could play a role in the many neuropsychiatric and neurodegenerative diseases that currently lack clear etiologies, there is a strong motivation to determine the “ground truth” of microbial existence in living brains. Here I argue that the evidence for the presence of microbes in diseased brains is quite strong, but a compelling demonstration of resident microbes in the healthy human brain remains to be done. Dedicated animal models studies may be required to determine if there is indeed a “brain microbiome.”

Open Source Software Implementation of Anatomical Segmentation

2018 ◽  
Author(s):  
Maggie Hess
Keyword(s):  
Large Scale ◽  
Focused Ultrasound ◽  
Region Growing ◽  
Ground Truth ◽  
Clot Lysis ◽  
Focused Ultrasound Surgery ◽  
Manual Methods ◽  
Areas Of Interest ◽  
Seed Points ◽  
Viable Method

Purpose: Intraventricular hemorrhage (IVH) affects nearly 15% of preterm infants. It can lead to ventricular dilation and cognitive impairment. To ablate IVH clots, MR-guided focused ultrasound surgery (MRgFUS) is investigated. This procedure requires accurate, fast and consistent quantification of ventricle and clot volumes. Methods: We developed a semi-autonomous segmentation (SAS) algorithm for measuring changes in the ventricle and clot volumes. Images are normalized, and then ventricle and clot masks are registered to the images. Voxels of the registered masks and voxels obtained by thresholding the normalized images are used as seed points for competitive region growing, which provides the final segmentation. The user selects the areas of interest for correspondence after thresholding and these selections are the final seeds for region growing. SAS was evaluated on an IVH porcine model.  Results: SAS was compared to ground truth manual segmentation (MS) for accuracy, efficiency, and consistency. Accuracy was determined by comparing clot and ventricle volumes produced by SAS and MS. In Two-One-Sided Test, SAS and MS were found to be significantly equivalent (p < 0.01). SAS on average was found to be 15 times faster than MS (p < 0.01). Consistency was determined by repeated segmentation of the same image by both SAS and manual methods, SAS being significantly more consistent than MS (p < 0.05).  Conclusion: SAS is a viable method to quantify the IVH clot and the lateral brain ventricles and it is serving in a large- scale porcine study of MRgFUS treatment of IVH clot lysis.

scholarly journals DeepMAsED: evaluating the quality of metagenomic assemblies

2020 ◽  
Vol 36 (10) ◽  
pp. 3011-3017 ◽  
Author(s):  
Olga Mineeva ◽  
Mateo Rojas-Carulla ◽  
Ruth E Ley ◽  
Bernhard Schölkopf ◽  
Nicholas D Youngblut
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Ground Truth ◽  
Supplementary Information ◽  
Learning Approach ◽  
Wide Range ◽  
Metagenome Assembly ◽  
Model Training ◽  
Reference Genomes

Abstract Motivation Methodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large-scale metagenome assemblies. Results We present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates a 1% contig misassembly rate in two recent large-scale metagenome assembly publications. Conclusions DeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modeling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects. Availability and implementation DeepMAsED is available from GitHub at https://github.com/leylabmpi/DeepMAsED. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience.

2020 ◽  
Author(s):  
Bahar Azari ◽  
Christiana Westlin ◽  
Ajay Satpute ◽  
J. Benjamin Hutchinson ◽  
Philip A. Kragel ◽  
...  
Keyword(s):  
Human Brain ◽  
Subjective Experience ◽  
A Priori ◽  
Ground Truth ◽  
Test Case ◽  
Physical Measurements ◽  
Supervised Classifiers ◽  
Emotional Episodes ◽  
Emotion Labels ◽  
The Brain

Machine learning methods provide powerful tools to map physical measurements to scientific categories. But are such methods suitable for discovering the ground truth about psychological categories? We use the science of emotion as a test case to explore this question. In studies of emotion, researchers use supervised classifiers, guided by emotion labels, to attempt to discover biomarkers in the brain or body for the corresponding emotion categories. This practice relies on the assumption that the labels refer to objective categories that can be discovered. Here, we critically examine this approach across three distinct datasets collected during emotional episodes- measuring the human brain, body, and subjective experience- and compare supervised classification studies with those from unsupervised clustering in which no a priori labels are assigned to the data. We conclude with a set of recommendations to guide researchers towards meaningful, data-driven discoveries in the science of emotion and beyond.

scholarly journals LUMINOUS database: lumbar multifidus muscle segmentation from ultrasound images

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Clyde J. Belasso ◽  
Bahareh Behboodi ◽  
Habib Benali ◽  
Mathieu Boily ◽  
Hassan Rivaz ◽  
...  
Keyword(s):  
Ground Truth ◽  
Ease Of Use ◽  
Automated Segmentation ◽  
Manual Segmentation ◽  
Clinical Implementation ◽  
Multifidus Muscle ◽  
Lumbar Multifidus ◽  
The Us ◽  
Segmentation Algorithms ◽  
And Function

Abstract Background Among the paraspinal muscles, the structure and function of the lumbar multifidus (LM) has become of great interest to researchers and clinicians involved in lower back pain and muscle rehabilitation. Ultrasound (US) imaging of the LM muscle is a useful clinical tool which can be used in the assessment of muscle morphology and function. US is widely used due to its portability, cost-effectiveness, and ease-of-use. In order to assess muscle function, quantitative information of the LM must be extracted from the US image by means of manual segmentation. However, manual segmentation requires a higher level of training and experience and is characterized by a level of difficulty and subjectivity associated with image interpretation. Thus, the development of automated segmentation methods is warranted and would strongly benefit clinicians and researchers. The aim of this study is to provide a database which will contribute to the development of automated segmentation algorithms of the LM. Construction and content This database provides the US ground truth of the left and right LM muscles at the L5 level (in prone and standing positions) of 109 young athletic adults involved in Concordia University’s varsity teams. The LUMINOUS database contains the US images with their corresponding manually segmented binary masks, serving as the ground truth. The purpose of the database is to enable development and validation of deep learning algorithms used for automatic segmentation tasks related to the assessment of the LM cross-sectional area (CSA) and echo intensity (EI). The LUMINOUS database is publicly available at http://data.sonography.ai. Conclusion The development of automated segmentation algorithms based on this database will promote the standardization of LM measurements and facilitate comparison among studies. Moreover, it can accelerate the clinical implementation of quantitative muscle assessment in clinical and research settings.

scholarly journals MONACO: accurate biological network alignment through optimal neighborhood matching between focal nodes

2020 ◽  
Author(s):  
Hyun-Myung Woo ◽  
Byung-Jun Yoon
Keyword(s):  
Biological Network ◽  
Protein Complexes ◽  
Ground Truth ◽  
Network Alignment ◽  
Supplementary Information ◽  
Alignment Algorithm ◽  
Computationally Efficient ◽  
Topological Similarity ◽  
Alignment Algorithms ◽  
Multiple Network

Abstract Motivation Alignment of protein–protein interaction networks can be used for the unsupervised prediction of functional modules, such as protein complexes and signaling pathways, that are conserved across different species. To date, various algorithms have been proposed for biological network alignment, many of which attempt to incorporate topological similarity between the networks into the alignment process with the goal of constructing accurate and biologically meaningful alignments. Especially, random walk models have been shown to be effective for quantifying the global topological relatedness between nodes that belong to different networks by diffusing node-level similarity along the interaction edges. However, these schemes are not ideal for capturing the local topological similarity between nodes. Results In this article, we propose MONACO, a novel and versatile network alignment algorithm that finds highly accurate pairwise and multiple network alignments through the iterative optimal matching of ‘local’ neighborhoods around focal nodes. Extensive performance assessment based on real networks as well as synthetic networks, for which the ground truth is known, demonstrates that MONACO clearly and consistently outperforms all other state-of-the-art network alignment algorithms that we have tested, in terms of accuracy, coherence and topological quality of the aligned network regions. Furthermore, despite the sharply enhanced alignment accuracy, MONACO remains computationally efficient and it scales well with increasing size and number of networks. Availability and implementation Matlab implementation is freely available at https://github.com/bjyoontamu/MONACO. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Efficient implementation of convolutional neural networks in the data processing of two-photon in vivo imaging

2019 ◽  
Vol 35 (17) ◽  
pp. 3208-3210 ◽  
Author(s):  
Yangzhen Wang ◽  
Feng Su ◽  
Shanshan Wang ◽  
Chaojuan Yang ◽  
Yonglu Tian ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Detection Methods ◽  
Supplementary Information ◽  
Imaging Data ◽  
Two Photon ◽  
Processing Power ◽  
Fluctuation Method ◽  
The Brain

Abstract Motivation Functional imaging at single-neuron resolution offers a highly efficient tool for studying the functional connectomics in the brain. However, mainstream neuron-detection methods focus on either the morphologies or activities of neurons, which may lead to the extraction of incomplete information and which may heavily rely on the experience of the experimenters. Results We developed a convolutional neural networks and fluctuation method-based toolbox (ImageCN) to increase the processing power of calcium imaging data. To evaluate the performance of ImageCN, nine different imaging datasets were recorded from awake mouse brains. ImageCN demonstrated superior neuron-detection performance when compared with other algorithms. Furthermore, ImageCN does not require sophisticated training for users. Availability and implementation ImageCN is implemented in MATLAB. The source code and documentation are available at https://github.com/ZhangChenLab/ImageCN. Supplementary information Supplementary data are available at Bioinformatics online.

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