Manual Quantification Research Articles

DALMATIAN: An Algorithm for Automatic Cell Detection and Counting in 3D.

Current 3D imaging methods, including optical projection tomography, light-sheet microscopy, block-face imaging, and serial two photon tomography enable visualization of large samples of biological tissue. Large volumes of data obtained at high resolution require development of automatic image processing techniques, such as algorithms for automatic cell detection or, more generally, point-like object detection. Current approaches to automated cell detection suffer from difficulties originating from detection of particular cell types, cell populations of different brightness, non-uniformly stained, and overlapping cells. In this study, we present a set of algorithms for robust automatic cell detection in 3D. Our algorithms are suitable for, but not limited to, whole brain regions and individual brain sections. We used watershed procedure to split regional maxima representing overlapping cells. We developed a bootstrap Gaussian fit procedure to evaluate the statistical significance of detected cells. We compared cell detection quality of our algorithm and other software using 42 samples, representing 6 staining and imaging techniques. The results provided by our algorithm matched manual expert quantification with signal-to-noise dependent confidence, including samples with cells of different brightness, non-uniformly stained, and overlapping cells for whole brain regions and individual tissue sections. Our algorithm provided the best cell detection quality among tested free and commercial software.

Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning

Development and validation of a fully automated method to detect and quantify macular fluid in conventional OCT images. Development of a diagnostic modality. The clinical dataset for fluid detection consisted of 1200 OCT volumes of patients with neovascular age-related macular degeneration (AMD, n= 400), diabetic macular edema (DME, n= 400), or retinal vein occlusion (RVO, n= 400) acquired with Zeiss Cirrus (Carl Zeiss Meditec, Dublin, CA) (n= 600) or Heidelberg Spectralis (Heidelberg Engineering, Heidelberg, Germany) (n= 600) OCT devices. A method based on deep learning to automatically detect and quantify intraretinal cystoid fluid (IRC) and subretinal fluid (SRF) was developed. The performance of the algorithm in accurately identifying fluid localization and extent was evaluated against a manual consensus reading of 2 masked reading center graders. Performance of a fully automated method to accurately detect, differentiate, and quantify intraretinal and SRF using area under the receiver operating characteristics curves, precision, and recall. The newly designed, fully automated diagnostic method based on deep learning achieved optimal accuracy for the detection and quantification of IRC for all 3 macular pathologies with a mean accuracy (AUC) of 0.94 (range, 0.91-0.97), a mean precision of 0.91, and a mean recall of 0.84. The detection and measurement of SRF were also highly accurate with an AUC of 0.92 (range, 0.86-0.98), a mean precision of 0.61, and a mean recall of 0.81, with superior performance in neovascular AMD and RVO compared with DME, which was represented rarely in the population studied. High linear correlation was confirmed between automated and manual fluid localization and quantification, yielding an average Pearson's correlation coefficient of 0.90 for IRC and of 0.96 for SRF. Deep learning in retinal image analysis achieves excellent accuracy for the differential detection of retinal fluid types across the most prevalent exudative macular diseases and OCT devices. Furthermore, quantification of fluid achieves a high level of concordance with manual expert assessment. Fully automated analysis of retinal OCT images from clinical routine provides a promising horizon in improving accuracy and reliability of retinal diagnosis for research and clinical practice in ophthalmology.

MyoVision: software for automated high-content analysis of skeletal muscle immunohistochemistry.

Analysis of skeletal muscle cross sections is an important experimental technique in muscle biology. Many aspects of immunohistochemistry and fluorescence microscopy can now be automated, but most image quantification techniques still require extensive human input, slowing progress and introducing the possibility of user bias. MyoVision is a new software package that was developed to overcome these limitations. The software improves upon previously reported automatic techniques and analyzes images without requiring significant human input and correction. When compared with data derived by manual quantification, MyoVision achieves an accuracy of ≥94% for basic measurements such as fiber number, fiber type distribution, fiber cross-sectional area, and myonuclear number. Scientists can download the software free from www.MyoVision.org and use it to automate the analysis of their own experimental data. This will improve the efficiency and consistency of the analysis of muscle cross sections and help to reduce the burden of routine image quantification in muscle biology. NEW & NOTEWORTHY Scientists currently analyze images of immunofluorescently labeled skeletal muscle using time-consuming techniques that require sustained human supervision. As well as being inefficient, these techniques can increase variability in studies that quantify morphological adaptations of skeletal muscle at the cellular level. MyoVision is new software that overcomes these limitations by performing high-content analysis of muscle cross sections with minimal manual input. It is open source and freely available.

Duchenne and Becker Muscular Dystrophies: A Review of Animal Models, Clinical End Points, and Biomarker Quantification.

Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) are neuromuscular disorders that primarily affect boys due to an X-linked mutation in the DMD gene, resulting in reduced to near absence of dystrophin or expression of truncated forms of dystrophin. Some newer therapeutic interventions aim to increase sarcolemmal dystrophin expression, and accurate dystrophin quantification is critical for demonstrating pharmacodynamic relationships in preclinical studies and clinical trials. Current challenges with measuring dystrophin include the variation in protein expression within individual muscle fibers and across whole muscle samples, the presence of preexisting dystrophin-positive revertant fibers, and trace amounts of residual dystrophin. Immunofluorescence quantification of dystrophin can overcome many of these challenges, but manual quantification of protein expression may be complicated by variations in the collection of images, reproducible scoring of fluorescent intensity, and bias introduced by manual scoring of typically only a few high-power fields. This review highlights the pathology of DMD and BMD, discusses animal models of DMD and BMD, and describes dystrophin biomarker quantitation in DMD and BMD, with several image analysis approaches, including a new automated method that evaluates protein expression of individual muscle fibers.

Composite Pheochromocytoma/Paraganglioma-Ganglioneuroma: A Clinicopathologic Study of Eight Cases with Analysis of Succinate Dehydrogenase.

Ganglioneuromas represent the most well-differentiated spectrum of neoplasia arising from the sympathetic nervous system, while neuroblastomas represent the most poorly differentiated counterpart, and ganglioneuroblastomas represent intermediate stages of differentiation. Small series of cases have documented the co-occurrence of ganglioneuroma with a pheochromocytoma (Pheo)/paraganglioma (PGL) component. We report the clinicopathologic features of eight such cases, diagnosed between 2003 and 2015 with a mean follow-up of 22months (1-47), which were evaluated for syndrome associations, SDHB expression, and clinical outcome. Mutations of the succinate dehydrogenase (SDH) complex subunits (A, B, C, D, and SDHAF2) have been implicated in predicting metastatic behavior and in identifying possible paraganglioma syndromes. The proliferative index was calculated by manual quantification of Ki-67-positive cells at selected hot-spots using ImageJ (NIH). In our series, composite Pheo/PGL-ganglioneuromas predominantly involved the adrenal gland (Pheo 7, PGL 1). The cases had an equal gender distribution (males 4, females 4), with a mean age at diagnosis of 67years (range 53 to 86years), an average size of 5.2cm (range 2 to 8.2cm), an average weight of 49.3g (7.8 to 144.7g, n=6), and the majority were functionally active (7 of 8, 88%). The mean Ki67 proliferation rate was 2% (range 0.3 to 3%), and all cases retained SDHB expression (8/8, 100%). No patient (0/8, 0%) developed metastatic disease on follow-up. One patient had a retroperitoneal composite PGL-ganglioneuroma in the setting of neurofibromatosis type 1. No recurrent disease or other associations were identified. In our study, composite Pheo/PGL-ganglioneuromas predominantly affected the adrenal gland in older patients, showed no loss of SDHB, and no disease recurrence was identified.

Quantitative real-time PCR (qPCR) for the detection and quantification of dactylogyrid parasites infecting Lutjanus guttatus.

Severe infections of the spotted rose snapper Lutjanus guttatus resulting from dactylogyrid monogeneans present a risk to aquaculture. Currently, the diagnosis of this infection requires the morphological identification and manual quantification of parasites. Based on the characterization of the 28S rRNA gene of dactylogyrid species present in L. guttatus, specific primers were designed for real-time polymerase chain reaction (qPCR) using EvaGreen® chemistry. The standard curve method estimated the number of dactylogyrids accurately. A total of 85 gill samples from cage-cultured fish infected with dactylogyrids were analysed. The estimated number of dactylogyrids using this molecular method was very similar to the manual count that was performed initially. The standardized qPCR approach will be helpful as a complementary method for the early routine monitoring of dactylogyrid infections and for epidemiological studies in which a high number of fish must be studied.

The soundscape of Arctic Charr spawning grounds in lotic and lentic environments: can passive acoustic monitoring be used to detect spawning activities?

The aims of this study were to (i) assess the efficacy of passive acoustic monitoring (PAM) for detecting Arctic Charr at their spawning grounds and (ii) characterize the overall acoustic soundscape of these sites. PAM was carried out over three Arctic Charr spawning grounds in the UK, one lotic and two lentic. 24-h cycles of recordings were collected prior to and during the Arctic Charr spawning season, which was determined from data returns by simultaneous net monitoring. Acoustic analysis consisted of manual quantification of sound sources, Acoustic Complexity Index (ACI) calculation and spectral analysis in 1/3 octave band (SPL; dB re 1 μPa). In the lotic spawning ground, prior to the beginning of Arctic Charr spawning, SPL and ACI showed a restricted range of variation throughout the 24-h, while during spawning the night values of SPL and ACI were found to significantly increase, concurrently with the rate of gravel noise induced by fish spawning activities and fish air passage sounds. Both prior to and during the Arctic Charr run, the lentic soundscape was characterized by diel variation due to the daytime presence of anthropogenic noise and the night-time presence of insect calls, while only a few occurrences of fish air passage sounds and gravel noise were recorded. These findings suggest that PAM over Arctic Charr spawning grounds could provide meaningful information to be used in developing management plans for this threatened species, such as determining the location and time of arrival, diel pattern and length of spawning activities.

Semiautomatic classification of acetabular shape from three-dimensional ultrasound for diagnosis of infant hip dysplasia using geometric features.

Developmental dysplasia of the hip (DDH) is a congenital deformity which in severe cases leads to hip dislocation and in milder cases to premature osteoarthritis. Image-aided diagnosis of DDH is partly based on Graf classification which quantifies the acetabular shape seen at two-dimensional ultrasound (2DUS), which leads to high inter-scan variance. 3D ultrasound (3DUS) is a promising alternative for more reliable DDH diagnosis. However, manual quantification of acetabular shape from 3DUS is cumbersome. Here, we (1) propose a semiautomated segmentation algorithm to delineate 3D acetabular surface models from 3DUS using graph search; (2) propose a fully automated method to classify acetabular shape based on a random forest (RF) classifier using features derived from 3D acetabular surface models; and (3) test diagnostic accuracy on a dataset of 79 3DUS infant hip recordings (36 normal, 16 borderline, 27 dysplastic based on orthopedic surgeon assessment) in 42 patients. For each 3DUS, we performed semiautomated segmentation to produce 3D acetabular surface models and then calculated geometric features including the automatic [Formula: see text]lpha (AA), acetabular contact angle (ACA), kurtosis (K), skewness (S) and convexity (C). Mean values of features obtained from surface models were used as inputs to train a RF classifier. Surface models were generated rapidly (user time 46.2s) via semiautomated segmentation and visually closely correlated with actual acetabular contours (RMS error 1.39 ± 0.7mm). A paired nonparametric u test on of feature values in each category showed statistically significant variation (p<0.001) for AA, ACA and convexity. The RF classifier was 100% specific and 97.2% sensitive in classifying normal versus dysplastic hips and yielded true positive rates of 94.4, 62.5 and 89.9% for normal, borderline and dysplastic hips. The proposed technique reduces the subjectivity of image-aided DDH diagnosis and could be useful in clinical practice.

Novel tools for quantifying secondary growth

Secondary growth occurs in dicotyledons and gymnosperms, and results in an increased girth of plant organs. It is driven primarily by the vascular cambium, which produces thousands of cells throughout the life of several plant species. For instance, even in the small herbaceous model plant Arabidopsis, manual quantification of this massive process is impractical. Here, we provide a comprehensive overview of current methods used to measure radial growth. We discuss the issues and problematics related to its quantification. We highlight recent advances and tools developed for automated cellular phenotyping and its future applications.

Fully Automatic 3-D-TEE Segmentation for the Planning of Transcatheter Aortic Valve Implantation.

A novel fully automatic framework for aortic valve (AV) trunk segmentation in three-dimensional (3-D) transesophageal echocardiography (TEE) datasets is proposed. The methodology combines a previously presented semiautomatic segmentation strategy by using shape-based B-spline Explicit Active Surfaces with two novel algorithms to automate the quantification of relevant AV measures. The first combines a fast rotation-invariant 3-D generalized Hough transform with a vessel-like dark tube detector to initialize the segmentation. After segmenting the AV wall, the second algorithm focuses on aligning this surface with the reference ones in order to estimate the short-axis (SAx) planes (at the left ventricular outflow tract, annulus, sinuses of Valsalva, and sinotubular junction) in which to perform the measurements. The framework has been tested in 20 3-D-TEE datasets with both stenotic and nonstenotic AVs. The initialization algorithm presented a median error of around 3mm for the AV axis endpoints, with an overall feasibility of 90%. In its turn, the SAx detection algorithm showed to be highly reproducible, with indistinguishable results compared with the variability found between the experts' defined planes. Automatically extracted measures at the four levels showed a good agreement with the experts' ones, with limits of agreement similar to the interobserver variability. Moreover, a validation set of 20 additional stenotic AV datasets corroborated the method's applicability and accuracy. The proposed approach mitigates the variability associated with the manual quantification while significantly reducing the required analysis time (12s versus 5 to 10min), which shows its appeal for automatic dimensioning of the AV morphology in 3-D-TEE for the planning of transcatheter AV implantation.

Analysis of Color Standardization Methods for the Automatic Quantification of IHQ Stain in Breast TMA

Introduction/ Background IHC biomarkers in breast TMA samples are used daily in pathology departments. This generates large amounts of information, which requires careful analysis [1]. Automatic methods to evaluate positive staining have been investigated since they may save time and reduce errors in the diagnosis that are due to subjective evaluation. Aims The aim of this work is to develop a density tool able to automatically quantify the positive brown IHC stain in breast TMA. One of the problems when dealing with stained samples is color variation and distortion [2]. This is due to several factors such as the fixation process, the amount of stain or the digitalization process. One solution to the color variation problem is to apply standardization of reagents and procedures in histological practice. However, stains fade over time and therefore, it is not possible to achieve complete standardization with the current technology. In this paper, different methods for stain normalization have been analyzed and compared in density quantification. Methods The methods implemented for stain normalization are based on the use of color distribution by means of dominant color descriptor, scalable and color structure descriptor. These algorithms adjust the color values of an image on a pixel-by-pixel basis so as to match the color distribution of the source image to that of a target image. Then, two main processes were performed to estimate TMA density: a) evaluation of total cylinder area and b) quantification of IHC stained area. For the 1st process, the algorithm distinguishes between normal, broken or distorted cylinders. The 2nd process deals with the evaluation of the positive brown pixels inside the cylinder. The segmentation is based on Lab thersholding together with binary thresholding applied to the H, S and B channels of the HSV and RGB color models. Finally, the tool segments all the positive areas and quantifies the brown density areas. Results A dataset of 879 TMA images were used to evaluate the methods. TMAs were prepared with an automatic tissue arrayer (Arraymold tool) composed of 50 holes/TMA with a cylinder diameter of 2mm. Slides were stained using different IHC stains, that is, CD1A, CD4, CD8, CD21, CD57, CD68, CD83, CD123, CK19, OXP3, LAMp3 and S100. The acquisition of the digital TMA images was done with Aperio ScanScope XT scanner at 40x (0.25 µm/ pixel). Afterwards, each cylinder image was individually extracted [3]. The use of color standardization makes the segmentation robust and free of parameter setting. Furthermore, the standardization process is able to reduce noise and facilitate the density quantification. The results were compared to manual density quantification by expert pathologists. The tests carried out provided up to 98% agreement when color standardization was applied against 90% without color standardization. The biggest error comes from FOXP3 samples.

Rahbin: A quadcopter unmanned aerial vehicle based on a systematic image processing approach toward an automated asphalt pavement inspection

Automatic inspection of pavement cracking is a critical issue in pavement management systems. In this study, a quadcopter-based digital imaging system is introduced for collecting pavement surface data over a distressed area for visual conditions interpretation. An aerial evaluation was carried out using a quadcopter unmanned aerial vehicle (QUAV), which is a device equipped with a set of automatic systems. Since QUAV flies autonomously and has high maneuverability, it is potentially useful in a variety of conditions particularly the positions dangerous for surveillance and reconnaissance. The main purpose of this work is to design a multi-stage system for QUAV image analysis consisting of image processing, threshold selection, and classification stages. The images are transformed into a new domain; then, an adaptive thresholding is applied to build the pattern of transformed cracks; and finally, the polar support vector machine (the PSVM) is applied for interpretation of crack distress. The PSVM is an automation procedure based on the support vector machine (SVM) classifier defined in the polar coordinate frame. A Mixture of Wavelet modulus and three-dimensional polar Radon transform (3DPRT) are used for feature generation. We show that the PSVM method can be successfully applied to classify the crack and is capable of providing new features about cracking distress, threshold selection and classification. In order to show the applicability and efficiency of the proposed system and method, a test was conducted applying a variety of pavement distresses. The experimental results demonstrate that the applied system provides reliable output. In addition, the comparison of the derived information with the on-site manual quantifications revealed the potentiality of the QUAV and multi-stage system for future practice.

Mitral valve annulus localization in 3D echocardiography.

The Mitral Valve is a structure on the left side of the human heart that regulates the flow of oxygenated blood into the Left Ventricle and also helps maintain the pressure within the Left Ventricle when the blood gets pumped to the rest of the body from the Left Ventricle. Pathology of the Mitral Valve often manifests through structural changes in the anatomy. Assessment of Mitral Valve pathology as well as determination of specific interventions require quantification of various structures of the Mitral Valve and one of these structures of interest is the Mitral Valve annulus. Three dimensional echocardiography is a popular imaging technique that clinicians use for volumetric quantification of cardiac structures - but manual quantification is cumbersome and subjective. In this paper we describe a semi-automated approach for maximum-a-posteriori estimation of the Mitral Valve annulus from three-dimensional echocardiography images using a simple analytic shape model coupled with a Näive Bayes classifier. We validate our approach against manual annotations on over 15 real patient cases with an average localization error ≤ 2.59 mm.

Agreement between automated and manual quantification of corneal nerve fiber length: Implications for diabetic neuropathy research

AimsQuantification of corneal nerve fiber length (CNFL) by in vivo corneal confocal microscopy represents a promising diabetic neuropathy biomarker, but applicability is limited by resource-intensive image analysis. We aimed to evaluate, in cross-sectional analysis of non-diabetic controls and patients with type 1 and type 2 diabetes with and without neuropathy, the agreement between manual and automated analysis protocols. MethodsSixty-eight controls, 139 type 1 diabetes, and 249 type 2 diabetes participants underwent CNFL measurement (N=456). Neuropathy status was determined by clinical and electrophysiological criteria. CNFL was determined by manual (CNFLManual, reference standard) and automated (CNFLAuto) protocols, and results were compared for correlation and agreement using Spearman coefficients and the method of Bland and Altman (CNFLManual subtracted from CNFLAuto). ResultsParticipants demonstrated broad variability in clinical characteristics associated with neuropathy. The mean age, diabetes duration, and HbA1c were 53±18years, 15.9±12.6years, and 7.4±1.7%, respectively, and 218 (56%) individuals with diabetes had neuropathy. Mean CNFLManual was 15.1±4.9mm/mm2, and mean CNFLAuto was 10.5±3.7mm/mm2 (CNFLAuto underestimation bias, −4.6±2.6mm/mm2 corresponding to −29±17%). Percent bias was similar across non-diabetic controls (−33±12%), type 1 (−30±20%), and type 2 diabetes (−28±16%) subgroups (ANOVA, p=0.068), and similarly in diabetes participants with and without neuropathy. Levels of CNFLAuto and CNFLManual were both inversely associated with neuropathy status. ConclusionsAlthough CNFLAuto substantially underestimated CNFLManual, its bias was non-differential between diverse patient groups and its relationship with neuropathy status was preserved. Determination of diagnostic thresholds specific to CNFLAuto should be pursued in diagnostic studies of diabetic neuropathy.

Validation of an automated morphological MRI-based 123I-FP-CIT SPECT evaluation method

IntroductionDopamine transporter imaging with 123I-FP-CIT single photon emission computed tomography (SPECT) is helpful for the differential diagnosis between Parkinsonian syndrome (PS) and essential tremor (ET). Although visual assessment and time-consuming manual evaluation techniques are readily available, a fully objective and automated dopamine transporter quantification technique is always preferable, at least in research and follow-up investigations. Our aim was to develop a novel automated magnetic resonance imaging (MRI)-based evaluation technique of dopamine transporter SPECT images and to compare its diagnostic accuracy with those of the gold-standard visual grading and manual dopamine transporter binding quantification methods. Methods123I-FP-CIT SPECT and MRI sessions were conducted in 33 patients with PS (15 men; mean age: 60.3 ± 9.7 years) and 15 patients with ET (8 men; mean age: 54.7 ± 16.3 years). Striatal dopamine transporter binding was visually classified by 2 independent experts as normal or abnormal grade I, II and III. Caudal and putaminal specific uptake ratios were calculated by both automated MRI-based and manual evaluation techniques. ResultsWe found almost perfect agreement (κ = 0.829) between the visual scores by the 2 observers. The automated method showed strong correlation with the visual and manual evaluation techniques and its diagnostic accuracy (sensitivity = 97.0%; specificity = 93.3%) was also comparable to these methods. The automatically determined uptake parameters showed negative correlation with the clinical severity of parkinsonism. Based on ordinal regression modelling, the automated MRI-based method could reliably determine the visual grading scores. ConclusionThe novel MRI-based evaluation of 123I-FP-CIT SPECT images is useful for the differentiation of PS from ET.

Increased hepatic progenitor cell response and ductular reaction in patients with severe recurrent HCV post-liver transplantation.

Post-liver transplant (LT) hepatitis C virus (HCV) patients may develop allograft cirrhosis and rarely fibrosing cholestatic hepatitis (FCH), while others have a stable course. Hepatic progenitor cells (HPC) may be implicated in liver injury and fibrogenesis through ductular reaction (DR). We studied HPC response and DR in three distinct post-LT patterns of HCV: stable recurrence, allograft cirrhosis, and FCH. We identified 52 patients with untreated recurrent HCV and longitudinal liver biopsies (20 stable/23 cirrhosis/9 FCH) and eight healthy controls. Archived liver biopsy specimens for three time points (LT; initial recurrence; and clinical outcome) were stained for cytokeratin-7. Manual HPC counts and DR quantification using image analysis were performed. HCV counts and DR at LT did not differ across groups. At initial recurrence, HPC expansion occurred only in patients who developed cirrhosis, while prominent DR was present in those who developed FCH vs. stable and controls (p < 0.05). At outcome biopsies, HPC response and DR were increased in cirrhosis and FCH vs. stable and controls (p < 0.05). HPC response and DR did not differ in stable vs. These findings suggest that an altered HPC response assessed by cytokeratin-7 stain after LT may predict severity of HCV recurrence.

COMPARISON OF TOTAL LEUKOCYTE QUANTIFICATION METHODS IN FREE-LIVING GALAPAGOS TORTOISES (CHELONOIDIS SPP.).

Reptile hematologic data provide important health information for conservation efforts of vulnerable wildlife species such as the Galapagos tortoise (Chelonoidis spp.). Given the reported discrepancies between manual leukocyte counts for nonmammalian species, two manual leukocyte quantification methods, the Natt and Herrick's (NH) and the Eopette (EO), were compared to white blood cell (WBC) estimates from blood films of 42 free-living, clinically healthy, adult female Galapagos tortoises. To investigate the effects of delay in sample processing, estimated WBC counts and leukocyte differentials were compared for blood films prepared at time of collection under field conditions (T0) to blood films prepared from samples that were stored for 18-23 hr at 4°C in the laboratory (T1). Passing-Bablok regression analysis revealed no constant or proportional error between the NH and WBC estimates (T0 and T1) with slopes of 1.1 and 0.9, respectively. However both constant and proportional errors were present between EO and WBC estimates (T0 and T1) with slopes of 3.1 and 2.7, respectively. Bland Altman plots also showed agreement between the NH and WBC estimates where the points fell within the confidence-interval limit lines and were evenly distributed about the mean. In contrast, the EO and WBC estimate comparisons showed numerous points above the upper limit line, especially at higher concentrations. WBC estimates obtained from T0 and T1 films were in agreement, whereas heterophil and monocyte percentages based on differentials were not. Cell morphology and preservation were superior in T0 blood films because thrombocytes exhibited swelling after storage, becoming difficult to differentiate from lymphocytes. In this study, the highest quality and most reliable hematologic data in Galapagos tortoises were obtained by combining immediate blood film preparation with the NH leukocyte quantification method and a confirmatory WBC estimate from the blood film.

Automated pericardial fat quantification from coronary magnetic resonance angiography: feasibility study.

Pericardial fat volume (PFV) is emerging as an important parameter for cardiovascular risk stratification. We propose a hybrid approach for automated PFV quantification from water/fat-resolved whole-heart noncontrast coronary magnetic resonance angiography (MRA). Ten coronary MRA datasets were acquired. Image reconstruction and phase-based water-fat separation were conducted offline. Our proposed algorithm first roughly segments the heart region on the original image using a simplified atlas-based segmentation with four cases in the atlas. To get exact boundaries of pericardial fat, a three-dimensional graph-based segmentation is used to generate fat and nonfat components on the fat-only image. The algorithm then selects the components that represent pericardial fat. We validated the quantification results on the remaining six subjects and compared them with manual quantifications by an expert reader. The PFV quantified by our algorithm was [Formula: see text], compared to [Formula: see text] by the expert reader, which were not significantly different ([Formula: see text]) and showed excellent correlation ([Formula: see text],[Formula: see text]). The mean absolute difference in PFV between the algorithm and the expert reader was [Formula: see text]. The mean value of the paired differences was [Formula: see text] (95% confidence interval: [Formula: see text] to 6.21). The mean Dice coefficient of pericardial fat voxels was [Formula: see text]. Our approach may potentially be applied in a clinical setting, allowing for accurate magnetic resonance imaging (MRI)-based PFV quantification without tedious manual tracing.

Fully automated pixel-wise myocardial blood flow quantification with first-pass perfusion CMR

Methods Rest and adenosine stress perfusion imaging was performed on 17 normal volunteers. A saturation recovery SSFP dual-sequence technique was used to acquire three myocardial slices and an arterial input function (AIF) image series. A proton-density weighted image was acquired at the beginning of each series. Fully quantitative perfusion pixel maps were generated by an automated processing method, including non-rigid motion correction, surface-coil intensity correction, AIF and myocardial signal and contrast timing detection, and model constrained deconvolution. The results of the automatically generated MBF pixel maps were compared with manual quantification using an 18-segment model.

Agreement Between Automated and Manual Quantification of Corneal Nerve Fiber Length: Implications for Diabetic Neuropathy Research

Quantification of corneal nerve fiber length (CNFL) by in vivo corneal confocal microscopy represents a promising diabetic neuropathy biomarker, but applicability is limited by resource-intensive image analysis. We evaluated agreement between the established manual analysis protocol and a novel automated protocol.