scholarly journals Reliability of Using Retinal Vascular Fractal Dimension as a Biomarker in the Diabetic Retinopathy Detection

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Fan Huang ◽  
Behdad Dashtbozorg ◽  
Jiong Zhang ◽  
Erik Bekkers ◽  
Samaneh Abbasi-Sureshjani ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Automatic Segmentation ◽  
Clinical Applications ◽  
Regions Of Interest ◽  
Branching Pattern ◽  
Pattern Complexity ◽  
Potential Biomarker ◽  
Segmentation Methods ◽  
Relative Errors ◽  
The Stability

The retinal fractal dimension (FD) is a measure of vasculature branching pattern complexity. FD has been considered as a potential biomarker for the detection of several diseases like diabetes and hypertension. However, conflicting findings were found in the reported literature regarding the association between this biomarker and diseases. In this paper, we examine the stability of the FD measurement with respect to (1) different vessel annotations obtained from human observers, (2) automatic segmentation methods, (3) various regions of interest, (4) accuracy of vessel segmentation methods, and (5) different imaging modalities. Our results demonstrate that the relative errors for the measurement of FD are significant and FD varies considerably according to the image quality, modality, and the technique used for measuring it. Automated and semiautomated methods for the measurement of FD are not stable enough, which makes FD a deceptive biomarker in quantitative clinical applications.

scholarly journals Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics

2020 ◽  
Vol 24 (01) ◽  
pp. 50-64 ◽  
Author(s):  
Meritxell Bach Cuadra ◽  
Julien Favre ◽  
Patrick Omoumi
Keyword(s):  
Machine Learning ◽  
Clinical Practice ◽  
Quantitative Analysis ◽  
Predictive Models ◽  
Signal Intensity ◽  
Computational Analysis ◽  
Automatic Segmentation ◽  
Regions Of Interest ◽  
Image Texture ◽  
Segmentation Methods

AbstractAlthough still limited in clinical practice, quantitative analysis is expected to increase the value of musculoskeletal (MSK) imaging. Segmentation aims at isolating the tissues and/or regions of interest in the image and is crucial to the extraction of quantitative features such as size, signal intensity, or image texture. These features may serve to support the diagnosis and monitoring of disease. Radiomics refers to the process of extracting large amounts of features from radiologic images and combining them with clinical, biological, genetic, or any other type of complementary data to build diagnostic, prognostic, or predictive models. The advent of machine learning offers promising prospects for automatic segmentation and integration of large amounts of data. We present commonly used segmentation methods and describe the radiomics pipeline, highlighting the challenges to overcome for adoption in clinical practice. We provide some examples of applications from the MSK literature.

Performance of Electronic Structure Methods for the Description of Hückel-Möbius Interconversions in Extended π-Systems

2019 ◽  
Author(s):  
Tatiana Woller ◽  
Ambar Banerjee ◽  
Nitai Sylvetsky ◽  
Xavier Deraet ◽  
Frank De Proft ◽  
...  
Keyword(s):  
Electronic Structure ◽  
Basis Set ◽  
Dft Methods ◽  
Molecular Switching ◽  
Wide Range ◽  
Electronic Structure Methods ◽  
Explicitly Correlated ◽  
Relative Errors ◽  
The Stability ◽  
Basis Set Expansion

<p>Expanded porphyrins provide a versatile route to molecular switching devices due to their ability to shift between several π-conjugation topologies encoding distinct properties. Taking into account its size and huge conformational flexibility, DFT remains the workhorse for modeling such extended macrocycles. Nevertheless, the stability of Hückel and Möbius conformers depends on a complex interplay of different factors, such as hydrogen bonding, p···p stacking, steric effects, ring strain and electron delocalization. As a consequence, the selection of an exchange-correlation functional for describing the energy profile of topological switches is very difficult. For these reasons, we have examined the performance of a variety of wavefunction methods and density functionals for describing the thermochemistry and kinetics of topology interconversions across a wide range of macrocycles. Especially for hexa- and heptaphyrins, the Möbius structures have a pronouncedly stronger degree of static correlation than the Hückel and figure-eight structures, and as a result the relative energies of singly-twisted structures are a challenging test for electronic structure methods. Comparison of limited orbital space full CI calculations with CCSD(T) calculations within the same active spaces shows that post-CCSD(T) correlation contributions to relative energies are very minor. At the same time, relative energies are weakly sensitive to further basis set expansion, as proven by the minor energy differences between MP2/cc-pVDZ and explicitly correlated MP2-F12/cc-pVDZ-F12 calculations. Hence, our CCSD(T) reference values are reasonably well-converged in both 1-particle and n-particle spaces. While conventional MP2 and MP3 yield very poor results, SCS-MP2 and particularly SOS-MP2 and SCS-MP3 agree to better than 1 kcal mol<sup>-1</sup> with the CCSD(T) relative energies. Regarding DFT methods, only M06-2X provides relative errors close to chemical accuracy with a RMSD of 1.2 kcal mol<sup>-1</sup>. While the original DSD-PBEP86 double hybrid performs fairly poorly for these extended p-systems, the errors drop down to 2 kcal mol<sup>-1</sup> for the revised revDSD-PBEP86-NL, again showing that same-spin MP2-like correlation has a detrimental impact on performance like the SOS-MP2 results. </p>

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

scholarly journals Exosome-derived noncoding RNAs in gastric cancer: functions and clinical applications

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Xiao-Huan Tang ◽  
Ting Guo ◽  
Xiang-Yu Gao ◽  
Xiao-Long Wu ◽  
Xiao-Fang Xing ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Noncoding Rnas ◽  
Tumour Microenvironment ◽  
Clinical Applications ◽  
Biological Molecules ◽  
Biological Functions ◽  
Membrane Structures ◽  
Chemical Substances ◽  
Expression Levels ◽  
The Stability

AbstractExosomes are a subpopulation of the tumour microenvironment (TME) that transmit various biological molecules to promote intercellular communication. Exosomes are derived from nearly all types of cells and exist in all body fluids. Noncoding RNAs (ncRNAs) are among the most abundant contents in exosomes, and some ncRNAs with biological functions are specifically packaged into exosomes. Recent studies have revealed that exosome-derived ncRNAs play crucial roles in the tumorigenesis, progression and drug resistance of gastric cancer (GC). In addition, regulating the expression levels of exosomal ncRNAs can promote or suppress GC progression. Moreover, the membrane structures of exosomes protect ncRNAs from degradation by enzymes and other chemical substances, significantly increasing the stability of exosomal ncRNAs. Specific hallmarks within exosomes that can be used for exosome identification, and specific contents can be used to determine their origin. Therefore, exosomal ncRNAs are suitable for use as diagnostic and prognostic biomarkers or therapeutic targets. Regulating the biogenesis of exosomes and the expression levels of exosomal ncRNAs may represent a new way to block or eradicate GC. In this review, we summarized the origins and characteristics of exosomes and analysed the association between exosomal ncRNAs and GC development.

scholarly journals A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning

2021 ◽  
Author(s):  
Wing Keung Cheung ◽  
Robert Bell ◽  
Arjun Nair ◽  
Leon Menezies ◽  
Riyaz Patel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Arteries ◽  
Automatic Segmentation ◽  
Three Dimensional ◽  
Regions Of Interest ◽  
Dice Similarity Coefficient ◽  
Processing Unit ◽  
Two Dimensional ◽  
Computed Tomography Images ◽  
Graphical Processing

AbstractA fully automatic two-dimensional Unet model is proposed to segment aorta and coronary arteries in computed tomography images. Two models are trained to segment two regions of interest, (1) the aorta and the coronary arteries or (2) the coronary arteries alone. Our method achieves 91.20% and 88.80% dice similarity coefficient accuracy on regions of interest 1 and 2 respectively. Compared with a semi-automatic segmentation method, our model performs better when segmenting the coronary arteries alone. The performance of the proposed method is comparable to existing published two-dimensional or three-dimensional deep learning models. Furthermore, the algorithmic and graphical processing unit memory efficiencies are maintained such that the model can be deployed within hospital computer networks where graphical processing units are typically not available.

Synaptic clef segmentation method based on fractal dimension for ATUM-SEM image of mouse cortex

2021 ◽  
pp. 2150038
Author(s):  
Chao Ma ◽  
Lijun Shen ◽  
Hao Deng ◽  
Jialin Li
Keyword(s):  
Fractal Dimension ◽  
Physical Structure ◽  
Noise Performance ◽  
Segmentation Method ◽  
Technical Advance ◽  
Image Stack ◽  
Sem Image ◽  
The Neural Network ◽  
Mouse Cortex ◽  
The Stability

It is well known that neurons communicate through synapses in the nervous system, and the size, morphology, and connectivity of synapses determine the functional properties of the neural network. Therefore, synapses have always been one of the key objects of neuroscience. Due to the technical advance in electron microscope (EM), the physical structure of synapses can be observed at high resolution. Nevbarheless, to date, the automatic analysis of the synapse in EM images is still a challenging task. In this paper, we proposed a fractal dimension-based segmentation method for synaptic clef of mouse cortex on EM image stack. Our method does not require a lot of groundtruth to train the model, and shows better adaptive anti-noise performance. That should be ascribed to the stability of segmentation-related key parameters in the data from same tissue. In this way, we only need to give initial values, and then gradually adjust these key parameters. Experiments reveal that our method achieves the desired results, and reduces the time in artificial annotating, so that researchers can focus more on the analysis of segmentation results.

scholarly journals A Study of the Spatial Form of Maling Village, Henan, China

2020 ◽  
Vol 12 (18) ◽  
pp. 7350
Author(s):  
Qindong Fan ◽  
Fengtian Du ◽  
Hu Li
Keyword(s):  
Land Use ◽  
Fractal Dimension ◽  
Fractal Theory ◽  
Stability Index ◽  
Spatial Form ◽  
Land Use Types ◽  
Fractal Characteristics ◽  
Spatial Shape ◽  
The Stability ◽  
The Village

In order to improve the study of the spatial form of villages, fractal theory is used to analyze the plane and facade of Maling Village, Changdai Town, Mengjin County, Luoyang City, Henan Province, China. The results show that the village facade and plane spatial shape of Maling Village have obvious fractal characteristics and the fractal dimension can be used as an important index to evaluate the plane and facade shape of the village. The fractal dimension of each land use type is between 1.2415 and 1.7443. The stability index of land use types in the village follows the order of village construction land > cultivated land > road > garden land > woodland > grassland. The research results can provide decision-making information for the rational use and planning of village land.

Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets

2012 ◽  
Vol 51 (05) ◽  
pp. 415-422 ◽  
Author(s):  
A. Schmidt-Richberg ◽  
J. Fiehler ◽  
T. Illies ◽  
D. Möller ◽  
H. Handels ◽  
...  
Keyword(s):  
Level Set ◽  
Automatic Segmentation ◽  
Time Of Flight ◽  
Medical Expert ◽  
Consistency Check ◽  
Major Drawback ◽  
Segmentation Methods ◽  
Initial Segmentation ◽  
Level Set Approach ◽  
Manual Correction

Summary Objectives: Exact cerebrovascular segmentations are required for several applications in today’s clinical routine. A major drawback of typical automatic segmentation methods is the occurrence of gaps within the segmentation. These gaps are typically located at small vessel structures exhibiting low intensities. Manual correction is very time-consuming and not suitable in clinical practice. This work presents a post-processing method for the automatic detection and closing of gaps in cerebrovascular segmentations. Methods: In this approach, the 3D centerline is calculated from an available vessel segmentation, which enables the detection of corresponding vessel endpoints. These endpoints are then used to detect possible connections to other 3D centerline voxels with a graph-based approach. After consistency check, reasonable detected paths are expanded to the vessel boundaries using a level set approach and combined with the initial segmentation. Results: For evaluation purposes, 100 gaps were artificially inserted at non-branching vessels and bifurcations in manual cerebrovascular segmentations derived from ten Time-of-Flight magnetic resonance angiography datasets. The results show that the presented method is capable of detecting 82% of the non-branching vessel gaps and 84% of the bifurcation gaps. The level set segmentation expands the detected connections with 0.42 mm accuracy compared to the initial segmentations. A further evaluation based on 10 real automatic segmentations from the same datasets shows that the proposed method detects 35 additional connections in average per dataset, whereas 92.7% were rated as correct by a medical expert. Conclusion: The presented approach can considerably improve the accuracy of cerebrovascular segmentations and of following analysis outcomes.

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