Measurement of functional microcirculatory geometry and velocity distributions using automated image analysis.
Ontology highlight
ABSTRACT:
SUBMITTER: Dobbe JG
PROVIDER: S-EPMC2441502 | biostudies-other | 2008 Jul
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Microcirculation plays a crucial role in delivering oxygen and nutrients to living tissues and in removing metabolic wastes from the human body. Monitoring the velocity of blood flow in microcirculation is essential for assessing various diseases, such as diabetes, cancer, and critical illnesses. Because of the complex morphological pattern of the capillaries, both In-vivo capillary identification and blood flow velocity measurement by conventional optical capillaroscopy are challenging. Thus, we focused on developing an In-vivo optical microscope for capillary imaging, and we propose an In-vivo full-field flow velocity measurement method based on intelligent object identification. The proposed method realizes full-field blood flow velocity measurements in microcirculation by employing a deep neural network to automatically identify and distinguish capillaries from images. In addition, a spatiotemporal diagram analysis is used for flow velocity calculation. In-vivo experiments were conducted, and the images and videos of capillaries were collected for analysis. We demonstrated that the proposed method is highly accurate in performing full-field blood flow velocity measurements in microcirculation. Further, because this method is simple and inexpensive, it can be effectively employed in clinics.
| S-EPMC6975132 | biostudies-literature
Project description:Objective:Full-thickness macular holes (MH) are classified principally by size, which is one of the strongest predictors of anatomical and visual success. Using a three-dimensional (3D) automated image processing algorithm, we analysed optical coherence tomography (OCT) images of 104 MH of patients, comparing MH dimensions and morphology with clinician-acquired two-dimensional measurements. Methods and Analysis:All patients underwent a high-density central horizontal scanning OCT protocol. Two independent clinicians measured the minimum linear diameter (MLD) and maximum base diameter. OCT images were also analysed using an automated 3D segmentation algorithm which produced key parameters including the respective maximum and minimum diameter of the minimum area (MA) of the MH, as well as volume and surface area. Results:Using the algorithm-derived values, MH were found to have significant asymmetry in all dimensions. The minima of the MA were typically approximately 90° to the horizontal, and differed from their maxima by 55 ?m. The minima of the MA differed from the human-measured MLD by a mean of nearly 50 ?m, with significant interobserver variability. The resultant differences led to reclassification using the International Vitreomacular Traction Study Group classification in a quarter of the patients (p=0.07). Conclusion:MH are complex shapes with significant asymmetry in all dimensions. We have shown how 3D automated analysis of MH describes their dimensions more accurately and repeatably than human assessment. This could be used in future studies investigating hole progression and outcome to help guide optimum treatments.
| S-EPMC7430427 | biostudies-literature
Project description:PurposeTo propose a rapid multi-slice T1 measurement method using time-resolved imaging of contrast kinetics (TRICKS) and a semi-automated image processing algorithm for comprehensive assessment murine kidney function using dynamic contrast-enhanced MRI (DCE-MRI).MethodsA multi-slice TRICKS sampling scheme was implemented in an established rapid T1 measurement method. A semi-automated image-processing scheme employing basic image processing techniques and machine learning was developed to facilitate image analysis. Reliability of the multi-slice technique in measuring renal perfusion and glomerular filtration rate (GFR) was tested in normal mice (n = 7 for both techniques) by comparing to the validated single-slice technique. Utility of this method was demonstrated on mice after either sham surgery (n = 7) or induction of unilateral renal artery stenosis (RAS, n = 8). Renal functional parameters were extracted using a validated bi-compartment model.ResultsThe TRICKS sampling scheme achieved an acceleration factor of 2.7, allowing imaging of eight axial slices at 1.23 s/scan. With the aid of the semi-automated scheme, image analysis required under 15-min for both kidneys per mouse. The multi-slice technique yielded renal perfusion and GFR values comparable to the single-slice technique. Model-fitted renal parameters successfully differentiated control and stenotic mouse kidneys, including renal perfusion (706.5 ± 164.0 vs. 375.9 ± 277.9 mL/100 g/min, P = 0.002), blood flow (1.6 ± 0.4 vs. 0.7 ± 0.7 mL/min, P < 0.001), and GFR (142.9 ± 17.9 vs. 58.0 ± 42.8 μL/min, P < 0.001).ConclusionThe multi-slice TRICKS-based DCE-MRI technique, with a semi-automated image processing scheme, allows rapid and comprehensive measurement of murine kidney function.
| S-EPMC6861697 | biostudies-literature
Project description:A nonintrusive digital imaging process was developed to study particle size distributions created through flocculation and sedimentation. Quantification of particle size distributions under different operating conditions can be of use in the understanding of aggregation mechanisms. This process was calibrated by measuring standardized polystyrene particles of known size and was utilized to count and measure individual kaolin clay particles as well as aggregates formed by coagulation with polyaluminum chloride and flocculation. Identification of out-of-focus flocs was automated with LabVIEW and used to remove them from the database that was analyzed. The particle diameter of the test suspension of kaolinite clay was measured to be 7.7?±?3.8??m and a linear relationship was obtained between turbidity and the concentration of clay particles determined by imaging. The analysis technique was applied to characterize flocs and floc particle size distribution as a function of coagulant dose. Removal of flocs by sedimentation was characterized by imaging, and the negative logarithm of the fraction of turbidity remaining after settling had a linear relationship with the logarithm of aluminum dose. The maximum floc size observed in the settled water was less than 120??m, which was in accordance with the value predicted by a model for the capture velocity of the experimental tube settler of 0.21?mm/s.
| S-EPMC4752185 | biostudies-literature
Project description:Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper, we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate. Although the method we present is general and can be applied to quantitative image data from any application, in this paper we focus on image-based proteomic data. We apply our method to an animal study investigating the effects of opiate addiction on the brain proteome. Our image-based functional mixed model approach finds results that are missed with conventional spot-based analysis approaches. In particular, we find that the significant regions of the image identified by the proposed method frequently correspond to subregions of visible spots that may represent post-translational modifications or co-migrating proteins that cannot be visually resolved from adjacent, more abundant proteins on the gel image. Thus, it is possible that this image-based approach may actually improve the realized resolution of the gel, revealing differentially expressed proteins that would not have even been detected as spots by modern spot-based analyses.
| S-EPMC3298181 | biostudies-literature
Project description:Various noninvasive microscopic camera technologies have been used to visualize the sublingual microcirculation in patients. We describe a comprehensive approach to bedside in vivo sublingual microcirculation video image capture and analysis techniques in the human clinical setting. We present a user perspective and guide suitable for clinical researchers and developers interested in the capture and analysis of sublingual microcirculatory flow videos. We review basic differences in the cameras, optics, light sources, operation, and digital image capture. We describe common techniques for image acquisition and discuss aspects of video data management, including data transfer, metadata, and database design and utilization to facilitate the image analysis pipeline. We outline image analysis techniques and reporting including video preprocessing and image quality evaluation. Finally, we propose a framework for future directions in the field of microcirculatory flow videomicroscopy acquisition and analysis. Although automated scoring systems have not been sufficiently robust for widespread clinical or research use to date, we discuss promising innovations that are driving new development.
| S-EPMC4748457 | biostudies-literature
Project description:ObjectiveTo determine how long it takes for neural impulses to travel along peripheral nerve fibers in living humans.MethodsA collision test was performed to measure the conduction velocity distribution of the ulnar nerve. Two stimuli at the distal and proximal sites were used to produce the collision. Compound muscle or nerve action potentials were recorded to perform the measurements on the motor or mixed nerve, respectively. Interstimulus interval was set at 1-5 ms. A quadri-pulse technique was used to measure the refractory period and calibrate the conduction time.ResultsCompound muscle action potential produced by the proximal stimulation started to emerge at the interstimulus interval of about 1.5 ms and increased with the increment in interstimulus interval. Two groups of motor nerve fibers with different conduction velocities were identified. The mixed nerve showed a wider conduction velocity distribution with identification of more subgroups of nerve fibers than the motor nerve.ConclusionsThe conduction velocity distributions in high resolution on a peripheral motor and mixed nerve are different and this can be measured with the collision test.SignificanceWe provided ground truth data to verify the neuroimaging pipelines for the measurements of latency connectome in the peripheral nervous system.
| S-EPMC8284026 | biostudies-literature
Project description:Background and aimEyelid position and contour abnormality could lead to various diseases, such as blepharoptosis, which is a common eyelid disease. Accurate assessment of eyelid morphology is important in the management of blepharoptosis. We aimed to proposed a novel deep learning-based image analysis to automatically measure eyelid morphological properties before and after blepharoptosis surgery.MethodsThis study included 135 ptotic eyes of 103 patients who underwent blepharoptosis surgery. Facial photographs were taken preoperatively and postoperatively. Margin reflex distance (MRD) 1 and 2 of the operated eyes were manually measured by a senior surgeon. Multiple eyelid morphological parameters, such as MRD1, MRD2, upper eyelid length and corneal area, were automatically measured by our deep learning-based image analysis. Agreement between manual and automated measurements, as well as two repeated automated measurements of MRDs were analysed. Preoperative and postoperative eyelid morphological parameters were compared. Postoperative eyelid contour symmetry was evaluated using multiple mid-pupil lid distances (MPLDs).ResultsThe intraclass correlation coefficients (ICCs) between manual and automated measurements of MRDs ranged from 0.934 to 0.971 (p < .001), and the bias ranged from 0.09 mm to 0.15 mm. The ICCs between two repeated automated measurements were up to 0.999 (p < .001), and the bias was no more than 0.002 mm. After surgery, MRD1 increased significantly from 0.31 ± 1.17 mm to 2.89 ± 1.06 mm, upper eyelid length from 19.94 ± 3.61 mm to 21.40 ± 2.40 mm, and corneal area from 52.72 ± 15.97 mm2 to 76.31 ± 11.31mm2 (all p < .001). Postoperative binocular MPLDs at different angles (from 0° to 180°) showed no significant differences in the patients.ConclusionThis technique had high accuracy and repeatability for automatically measuring eyelid morphology, which allows objective assessment of blepharoptosis surgical outcomes. Using only patients' photographs, this technique has great potential in diagnosis and management of other eyelid-related diseases.
| S-EPMC8805858 | biostudies-literature
Project description:The entorhinal cortex contains neurons that represent self-location, including grid cells that fire in periodic locations and velocity signals that encode running speed and head direction. Although the size and shape of the environment influence grid patterns, whether entorhinal velocity signals are equally influenced or provide a universal metric for self-motion across environments remains unknown. Here we report that speed cells rescale after changes to the size and shape of the environment. Moreover, head direction cells reorganize in an experience-dependent manner to align with the axis of environmental change. A knockout mouse model allows dissociation of the coordination between cell types, with grid and speed cells, but not head direction cells, responding in concert to environmental change. These results point to malleability in the coding features of multiple entorhinal cell types and have implications for which cell types contribute to the velocity signal used by computational models of grid cells.
| S-EPMC7007349 | biostudies-literature
Project description:Quantitative characterization of root system architecture and its development is important for the assessment of a complete plant phenotype. To enable high-throughput phenotyping of plant roots efficient solutions for automated image analysis are required. Since plants naturally grow in an opaque soil environment, automated analysis of optically heterogeneous and noisy soil-root images represents a challenging task. Here, we present a user-friendly GUI-based tool for semi-automated analysis of soil-root images which allows to perform an efficient image segmentation using a combination of adaptive thresholding and morphological filtering and to derive various quantitative descriptors of the root system architecture including total length, local width, projection area, volume, spatial distribution and orientation. The results of our semi-automated root image segmentation are in good conformity with the reference ground-truth data (mean dice coefficient?=?0.82) compared to IJ_Rhizo and GiAroots. Root biomass values calculated with our tool within a few seconds show a high correlation (Pearson coefficient?=?0.8) with the results obtained using conventional, pure manual segmentation approaches. Equipped with a number of adjustable parameters and optional correction tools our software is capable of significantly accelerating quantitative analysis and phenotyping of soil-, agar- and washed root images.
| S-EPMC6928233 | biostudies-literature