Manual Quantification Research Articles

Automated Quantification of Epicardial Adipose Tissue Using CT Angiography: Evaluation of a Prototype Software

This study evaluated the performance of a novel automated software tool for epicardial fat volume (EFV) quantification compared to a standard manual technique at coronary CT angiography (cCTA). cCTA data sets of 70 patients (58.6 ± 12.9years, 33 men) were retrospectively analysed using two different post-processing software applications. Observer 1 performed a manual single-plane pericardial border definition and EFVM segmentation (manual approach). Two observers used a software program with fully automated 3D pericardial border definition and EFVA calculation (automated approach). EFV and time required for measuring EFV (including software processing time and manual optimization time) for each method were recorded. Intraobserver and interobserver reliability was assessed on the prototype software measurements. T test, Spearman's rho, and Bland-Altman plots were used for statistical analysis. The final EFVA (with manual border optimization) was strongly correlated with the manual axial segmentation measurement (60.9 ± 33.2mL vs. 65.8 ± 37.0mL, rho = 0.970, P < 0.001). A mean of 3.9 ± 1.9 manual border edits were performed to optimize the automated process. The software prototype required significantly less time to perform the measurements (135.6 ± 24.6s vs. 314.3 ± 76.3s, P < 0.001) and showed high reliability (ICC > 0.9). Automated EFVA quantification is an accurate and time-saving method for quantification of EFV compared to established manual axial segmentation methods. • Manual epicardial fat volume quantification correlates with risk factors but is time-consuming. • The novel software prototype automates measurement of epicardial fat volume with good accuracy. • This novel approach is less time-consuming and could be incorporated into clinical workflow.

Evaluation of RDW-CV, RDW-SD, and MATH-1SD for the detection of erythrocyte anisocytosis observed by optical microscopy

INTRODUCTION AND OBJECTIVE: To evaluate the performance of red cell distribution width reported statistically as coefficient of variation (RDW-CV), standard deviation (RDW-SD), and mathematical deduction of 1 standard deviation (SD) around mean corpuscular volume (MATH-1SD) in identifying anisocytosis in automated blood counts when compared with the manual quantification of erythrocyte anisocytosis in peripheral blood smears. MATERIAL AND METHODS: 806 routine samples obtained from the hematology laboratory of Hospital de Clínicas da Universidade Federal do Paraná (HC-UFPR) were analyzed. Performance evaluations were carried out by dividing samples into microcytic, normocytic and macrocytic mean corpuscular volume (MCV). For each MCV range, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and efficiency were calculated. In addition, the Youden index (Y) was obtained and a comparative analysis with receiver operating characteristic (ROC) curves was done to evaluate the performance of RDW-SD, RDW-CV, and MATH-1SD on different MCV ranges. RESULTS AND DISCUSSION: RDW-CV had the best sensitivity (86.8%) and efficiency (86.8%) in detecting anisocytosis in microcytic MCV ranges. RDW-SD and MATH-1SD were more sensitive and efficient in normocytic (82.9% and 83.3%; 92.1% and 92.3%, respectively) and macrocytic (90.2% and 90.2%; 95.1% and 95.1%, respectively) MCV ranges. A ROC curve analysis indicated that RDW-CV was more efficient in detecting anisocytosis in microcytic MCV ranges (p < 0.05 vs. RDW-SD and MATH-1SD). In normocytic and macrocytic MCV ranges, RDW-SD and MATH-1SD showed similar efficiency in detecting anisocytosis (p < 0.05 vs. RDW-CV). CONCLUSION: RDW-SD, RDW-CV, and MATH-1SD deliver different performances in detecting blood smear anisocytosis according to MCV values. They are parameters that complement each other and should be used together to identify erythrocyte size heterogeneity.

Mesenteric Fat Is Less among Patients with Complicated Crohnʼs Disease: A Real Association?

Purpose: Mesenteric fat has been associated with more complicated (penetrating and stricturing) disease behaviors among patients with Crohn's disease (CD), suggesting that it is immunologically active. However, this conclusion was based on a cross-sectional analysis with the manual quantification of visceral fat at the level of the umbilicus, which is rather mobile and non-specific. We quantified mesenteric fat at L5-S1, and determined if it was independently associated with complicated CD. Methods: A retrospective study was performed on 176 consecutive CD patients, comparing inflammatory (non-stricturing non-penetrating) with complicated disease (Table). Mesenteric fat was determined at the time of behavior classification. Mesenteric fat was calculated based on CT and CT enterography, using software (TeraRecon, Foster,CA) highlighting the visceral fat by its specific Hounsfield units on a fixed cross sectional level of L5-S1 excluding retroperitoneal, intramuscular, subcutaneous, and intrathecal fat. The association of mesenteric fat with complicated disease behavior was determined after adjustment for disease-specific variables in a logistic regression analysis.TableResults: As expected, patents with complicated disease behavior had a younger age at diagnosis, longer disease duration, and were more likely to have received immunomodulator therapy (Table). There was no difference in mesenteric fat between those with penetrating or stricturing behaviors. Mesenteric fat was significantly lower (92 vs. 66 cm2) among patients with complicated disease. This was confirmed in the multivariate analysis with mesenteric fat negatively associated with complicated disease behavior (p=0.002). Conclusion: Contrary to recent literature, we found that mesenteric fat is negatively associated with complicated (penetrating or stricturing) Crohn's disease. Decreased quantity of mesenteric fat may be important in the development of complicated disease behavior, or, alternatively, may be the result of that behavior. Future prospective studies are necessary to provide measurements prior to the development of penetrating or stricturing complications, to better understand the role of mesenteric fat on the pathogenesis of Crohn's disease.

Fully automated analysis of chemically induced γH2AX foci in human peripheral blood mononuclear cells by indirect immunofluorescence

Analysis of phosphorylated histone protein H2AX (γH2AX) foci is currently the most sensitive method to detect DNA double-strand breaks (DSB). This protein modification has the potential to become an individual biomarker of cellular stress, especially in the diagnosis and monitoring of neoplastic diseases. To make γH2AX foci analysis available as a routine screening method, different software approaches for automated immunofluorescence pattern evaluation have recently been developed. In this study, we used novel pattern recognition algorithms on the AKLIDES® platform to automatically analyze immunofluorescence images of γH2AX foci and compared the results with visual assessments. Dose- and time-dependent γH2AX foci formation was investigated in human peripheral blood mononuclear cells (PBMCs) treated with the chemotherapeutic drug etoposide (ETP). Moreover, the AKLIDES system was used to analyze the impact of different immunomodulatory reagents on γH2AX foci formation in PBMCs. Apart from γH2AX foci counting the use of novel pattern recognition algorithms allowed the measurement of their fluorescence intensity and size, as well as the analysis of overlapping γH2AX foci. The comparison of automated and manual foci quantification showed overall a good correlation. After ETP exposure, a clear dose-dependent increase of γH2AX foci formation was evident using the AKLIDES as well as Western blot analysis. Kinetic experiments on PBMCs incubated with 5 μM ETP demonstrated a peak in γH2AX foci formation after 4 to 8 h, while a removal of ETP resulted in a strong reduction of γH2AX foci after 1 to 4 h. In summary, this study demonstrated that the AKLIDES system can be used as an efficient automatic screening tool for γH2AX foci analysis by providing new evaluation features and facilitating the identification of drugs which induce or modulate DNA damage.

Abstract 232: High Glucose Attenuates In Vitro Tube Growth of Microvascular Endothelial Cells

Aim: To investigate how high glucose affects the ability of microvascular endothelial cells to form tube-like structures in vitro and to develop open source software that automates the analysis of tube formation assays. Background: Over 300 million people worldwide suffer from diabetes. Patients with diabetes experience impaired angiogenesis. Tube formation assays (TFAs) mimic in vivo angiogenesis. During a TFA, endothelial cells that are placed onto a matrigel coated surface arrange into tube-like structures. Quantification of TFAs is difficult; automation typically requires a fluorescent stain and a specific software package. Manual quantification is tedious and subject to inter-user variability. In this study, we describe how high glucose affects the ability of rat microvascular endothelial cells (RMVECs) to form tube-like structures and the development of an open source tool called “Pipeline” that analyzes brightfield TFA images quantifying the total tube length and number of branchpoints. Methods: RMVECs were cultured in media with normal (5.6 mM) or high glucose (25 mM) for one and two week intervals. RMVECs were added to four-chamber slides coated with growth factor reduced Matrigel. After 24 and 48 hours, brightfield images were taken of the TFA. Images were analyzed by manually tracing the total tube length in MetaMorph image processing software or automatically using Pipeline. Results from automatic and manual analysis were compared. The algorithm was validated by measuring the total length of images generated in silico . The code was compiled in C and will run on any computer with the freely available MATLAB R2012a runtime environment. Results: A significant decrease in the total tube length was found when comparing groups treated with high glucose for 2 weeks versus 1 weeks (20.3 +/- 1.4mm vs 25.2 +/- 1.3mm, p&lt;0.05). Additionally, cells in high glucose for 2 weeks versus 1 week were found to have significantly less branch points (68 +/- 6.1 vs 90.9 +/- 5.8, p&lt;0.05). When validating the Pipeline algorithm against images generated in silico (at multiple orientations), Pipeline was able to detect total image length within 1% (9216μm vs 9348 +/- 98μm). Analysis using pipeline was found to be 25X faster than manual analysis (7 minutes versus 180 minutes).

Detection of fallen trees in forested areas using small footprint airborne laser scanning data

Deadwood was identified as an important indicator for habitat condition and biodiversity in forests. The assessment of downed trees is therefore part of sustainable forest management and ecological monitoring. However, manual quantification of deadwood in forests is challenging, time consuming, and considered cost-inefficient. Full-waveform airborne laser scanning (FWF-ALS) can be used to support the assessment process. The amplitude and width of the backscattered pulses contain information on the properties of the surface. We used these observations for the identification of downed trees in a Natura2000 forest site. A high density FWF-ALS data set was acquired under leaf-off conditions. Echo width and type (i.e., first, intermediate, and last) information as well as normalized echo heights were used to filter the point cloud and derive a digital height model (DHM). This DHM depicts downed stems as line-like features. Image processing was applied to derive and refine regions representing fallen trees. Terrestrial reference data consisting of locations and dimensions of downed trees, as well as state of decay were used for evaluation. Direct identification of downed trees in FWF-ALS point clouds was possible (completeness 75%, correctness 90%), but it was influenced by factors such as dimension, state of decay, vegetation density, and penetration of the laser.

Target lesion selection as a source of variability of response classification by RECIST 1.1.

11077 Background: Response classification in RECIST is based on manual uni-dimensional quantification of changes of target lesion size. In RECIST 1.1, the maximum number of target lesions was reduced to 5, with a maximum of 2 per organ. We analyzed the importance of different factors (manual vs. automated size measurement, uni- vs. three-dimensional size assessment, and between-reader-variability of target lesion selection) on response categorization. Methods: 41 female patients (58.1±13.2 y) with metastatic breast cancer underwent contrast-enhanced thoraco-abdominal CT for initial staging and first follow-up after systemic chemotherapy. Data were independently and prospectively interpreted by three radiologists. In addition, response was evaluated by a CAD system that allowed automated uni- and three-dimensional assessment of target lesions. Results: Response classification differed between readers in 19/41 patients (46%). In 25/41 patients, readers chose the same target lesions. In 6 of these 25 patients (24%), readers disagreed with regards to response classification. In 16/41 patients, readers chose different target lesions. In 13 of these 16 patients, readers disagreed (81%) (p &lt; 0.001). When dichotomizing response classification according to its therapeutic implication into progressive vs. non-progressive disease, readers disagreed in 11/41 patients (27%). In 9 of these 11 patients, readers had chosen different target lesions. Classification by manual vs. automated uni-dimensional measurement differed in 11/41 of patients (27%). Classification by uni-dimensional vs. volumetric measurements differed in 6/41 of patients (15%). Conclusions: Response classification by RECIST suffers from substantial between-reader variability. Major source of variability is not the manual or uni-dimensional measurement, but the variable choice of target lesions between readers. [Table: see text]

A novel application of motion analysis for detecting stress responses in embryos at different stages of development

BackgroundMotion analysis is one of the tools available to biologists to extract biologically relevant information from image datasets and has been applied to a diverse range of organisms. The application of motion analysis during early development presents a challenge, as embryos often exhibit complex, subtle and diverse movement patterns. A method of motion analysis able to holistically quantify complex embryonic movements could be a powerful tool for fields such as toxicology and developmental biology to investigate whole organism stress responses. Here we assessed whether motion analysis could be used to distinguish the effects of stressors on three early developmental stages of each of three species: (i) the zebrafish Danio rerio (stages 19 h, 21.5 h and 33 h exposed to 1.5% ethanol and a salinity of 5); (ii) the African clawed toad Xenopus laevis (stages 24, 32 and 34 exposed to a salinity of 20); and iii) the pond snail Radix balthica (stages E3, E4, E6, E9 and E11 exposed to salinities of 5, 10 and 15). Image sequences were analysed using Sparse Optic Flow and the resultant frame-to-frame motion parameters were analysed using Discrete Fourier Transform to quantify the distribution of energy at different frequencies. This spectral frequency dataset was then used to construct a Bray-Curtis similarity matrix and differences in movement patterns between embryos in this matrix were tested for using ANOSIM.ResultsSpectral frequency analysis of these motion parameters was able to distinguish stage-specific effects of environmental stressors in most cases, including Xenopus laevis at stages 24, 32 and 34 exposed to a salinity of 20, Danio rerio at 33 hpf exposed to 1.5% ethanol, and Radix balthica at stages E4, E9 and E11 exposed to salinities of 5, 10 and 15. This technique was better able to distinguish embryos exposed to stressors than analysis of manual quantification of movement and within species distinguished most of the developmental stages studied in the control treatments.ConclusionThis innovative use of motion analysis incorporates data quantifying embryonic movements at a range of frequencies and so provides an holistic analysis of an embryo’s movement patterns. This technique has potential applications for quantifying embryonic responses to environmental stressors such as exposure to pharmaceuticals or pollutants, and also as an automated tool for developmental staging of embryos.

Challenges in quantifying the Ki67 proliferation index in well-differentiated gastroenteropancreatic neuroendocrine tumours: assessment of a standardised manual technique

<h3>Aims</h3> Treatment decisions for gastroenteropancreatic neuroendo-crine tumours (GEP-NETs) are currently based on grading systems that incorporate the Ki67 proliferation index (PI). The reported Ki67 PI is often an ‘eyeball' estimation that is prone to inter-observer variability. We trialled the use of a standardised method of manual Ki67 quantification. <h3>Methods</h3> The Miller Square is an ocular graticule that is typically used in haematology to facilitate the rapid, accurate and reproducible counting of reticulocytes. We used this method to calculate the Ki67 PI in a retrospective review of thirty consecutive GEP-NETs. <h3>Results</h3> The Miller Square method produced high inter-observer agreement, with an intra-class correlation coefficient (ICC) of 0.99 (95%CI 0.98–0.99). When restricted to grade 1 and 2 tumours, there was only moderate inter-observer agreement, ICC 0.49 (95%CI 0.14–0.73). Using this method, five (18%) of 27 grade 1 and 2 tumours were classified differently by the two observers. Three (11%) of the tumours classified as grade 1 by eyeball estimation were upgraded by the Miller technique. <h3>Discussion</h3> Precise quantification of the Ki67 PI at low values to distinguish grade 1 and grade 2 tumours is challenging, both by eyeball estimation and when using a standardised counting technique. These results highlight the practical difficulties of current grading systems.

Digital image analysis as a tool to quantify gaping and morphology in smoked salmon slices

Gaping in salmon fillets refers to the appearance of slits in the muscle. This is unsightly and leads to downgrading and hence economical loss, particularly for salmon smoke houses. Present day manual and semi automatic methods for quantifying gaping are labour intensive and subjective. This study presents an automated and objective image analysis method for quantifying gaping in smoked salmon slices. The slices are first photographed in a photobox and then analysed by the fully automated image analysis method. The method has four main steps: (1) pre-processing, (2) segmentation of slices from the background, (3) labelling of each slice, (4) identification of gaps within the slices (holes), (5) identification of gaps on the perifery (notches) and (6) quantification of gaps and notches. The results obtained by the automatic image analysis are visually convincing and there is a strong correlation with manual quantification of gaps (r=0.83, p-value<0.05). The automatic image analysis method can easily be extended to also include morphological parameters as shape, red and white muscle area, myocommata and myotome area. Interestingly, the automatic image analysis demonstrated that gaps are strongly associated with white, and not red, muscle tissue.

Sensitivity and accuracy of volumetry of pulmonary nodules on low-dose 16- and 64-row multi-detector CT: an anthropomorphic phantom study

ObjectiveTo assess the sensitivity of detection and accuracy of volumetry by manual and semi-automated quantification of artificial pulmonary nodules in an anthropomorphic thoracic phantom on low-dose CT.MethodsFifteen artificial spherical nodules (diameter 3, 5, 8, 10 and 12 mm; CT densities -800, -630 and +100 HU) were randomly placed inside an anthropomorphic thoracic phantom. The phantom was examined on 16- and 64-row multidetector CT with a low-dose protocol. Two independent blinded observers screened for pulmonary nodules. Nodule diameter was measured manually, and volume calculated. For solid nodules (+100 HU), diameter and volume were also evaluated by semi-automated software. Differences in observed volumes between the manual and semi-automated method were evaluated by a t-test.ResultsSensitivity was 100 % for all nodules of >5 mm and larger, 60–80 % for solid and 0–20 % for non-solid 3-mm nodules. No false-positive nodules but high inter-observer reliability and inter-technique correlation were found. Volume was underestimated manually by 24.1 ± 14.0 % for nodules of any density, and 26.4 ± 15.5 % for solid nodules, compared with 7.6 ± 8.5 % (P < 0.01) semi-automatically.ConclusionIn an anthropomorphic phantom study, the sensitivity of detection is 100 % for nodules of >5 mm in diameter. Semi-automated volumetry yielded more accurate nodule volumes than manual measurements.Key Points• Computed tomography has become the definitive investigation of the chest. • Low-dose CT techniques have recently been introduced. • Low-dose CT is reliable for detecting spherical pulmonary nodules of >5 mm. • Semi-automated volumetry is more accurate than manual measurement for pulmonary nodules. • No difference in the accuracy of volumetry was found between 16- and 64- MDCT.

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS.

A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures

Nuclear magnetic resonance (NMR) spectra are widely used in metabolomics to obtain profiles of metabolites dissolved in biofluids such as cell supernatants. Methods for estimating metabolite concentrations from these spectra are presently confined to manual peak fitting and to binning procedures for integrating resonance peaks. Extensive information on the patterns of spectral resonance generated by human metabolites is now available in online databases. By incorporating this information into a Bayesian model, we can deconvolve resonance peaks from a spectrum and obtain explicit concentration estimates for the corresponding metabolites. Spectral resonances that cannot be deconvolved in this way may also be of scientific interest; so, we model them jointly using wavelets. We describe a Markov chain Monte Carlo algorithm that allows us to sample from the joint posterior distribution of the model parameters, using specifically designed block updates to improve mixing. The strong prior on resonance patterns allows the algorithm to identify peaks corresponding to particular metabolites automatically, eliminating the need for manual peak assignment. We assess our method for peak alignment and concentration estimation. Except in cases when the target resonance signal is very weak, alignment is unbiased and precise. We compare the Bayesian concentration estimates with those obtained from a conventional numerical integration method and find that our point estimates have six-fold lower mean squared error. Finally, we apply our method to a spectral dataset taken from an investigation of the metabolic response of yeast to recombinant protein expression. We estimate the concentrations of 26 metabolites and compare with manual quantification by five expert spectroscopists. We discuss the reason for discrepancies and the robustness of our method's concentration estimates. This article has supplementary materials online.

Automated drop detection using image analysis for online particle size monitoring in multiphase systems

Image analysis has become a powerful tool for the work with particulate systems, occurring in chemical engineering. A major challenge is still the excessive manual work load which comes with such applications. Additionally manual quantification also generates bias by different observers, as shown in this study. Therefore a full automation of those systems is desirable. A MATLAB® based image recognition algorithm has been implemented to automatically count and measure particles in multiphase systems.A given image series is pre-filtered to minimize misleading information. The subsequent particle recognition consists of three steps: pattern recognition by correlating the pre-filtered images with search patterns, pre-selection of plausible drops and the classification of these plausible drops by examining corresponding edges individually. The software employs a normalized cross correlation procedure algorithm. The program has reached hit rates of 95% with an error quotient under 1% and a detection rate of 250 particles per minute depending on the system.

Automated Quantification of MART1-Verified Ki67 Indices by Digital Image Analysis in Melanocytic Lesions

The proliferation marker Ki67 is an important diagnostic and prognostic aid in surgical pathology. However, manual quantification in a counting frame to accurately establish the proliferation rate (Ki67 index) is cumbersome and time-consuming. Instead, digital image analysis of Ki67/MART1 double stains may provide fast and novel index computations for entire tumor sections. To design and compare image analysis protocols that compute Ki67 indices of Ki67/MART1 double stains, to compare automated indices with previously published manual indices, and to compare the total number of proliferating cells (mimicking a Ki67 single stain) with the number of MART1-verified proliferating cells. Whole slide images were captured from 48 melanomas and 77 nevi stained with an immunohistochemical cocktail against Ki67 and MART1. Ki67 indices were determined by digital image analysis and different equations based on number or area. The differences between mean indices of melanomas and nevi were significant (P < .001) in all index computations. Number-based image analysis of lesions with more than 250 melanocytic cells misclassified 1 of 42 melanomas and 4 of 53 nevi, numbers comparable with manual counting. Automated indices were significantly higher than manual indices, as were indices of mimicked Ki67 single stains compared with MART1-verified Ki67 indices (P < .001). Ki67 indices established by digital image analysis of Ki67/MART1 double stains demonstrated excellent abilities to discriminate melanomas from nevi with diagnostic performances equal to manually performed indices. Testing different definitions of the automated MART1-verified Ki67 index, no single definition stood out; thus, a variety of definitions may be used.

Prognostic value of mitosis-specific antibodies and computer image analysis in calculating mitotic rate in melanoma.

e19003 Background: Mitotic rate (MR) is an important part of staging for thin melanomas (≤1 mm) but its value in thick melanomas (&gt;1 mm) has not been determined. Additionally, accurate calculation of MR on hematoxylin and eosin (H&amp;E) stained sections can be challenging. Several mitosis-specific antibodies are commercially available but their role in predicting clinical outcome in melanoma has not been adequately assessed compared to H&amp;E. Methods: Primary tissues from 190 cutaneous melanoma patients [≤1 mm (n = 22); &gt;1 mm (n=168)] prospectively enrolled at New York University Medical Center were stained with MPM-2 and PHH3, two mitosis-specific antibodies. Using light microscopy, MR was quantified manually from corresponding H&amp;E, MPM-2, and PHH3-stained sections. Computer-assisted image analysis was then applied to detect immunolabeled mitoses on the previously examined PHH3 and MPM-2 slides. We then analyzed the association between MR and progression-free survival (PFS) and melanoma-specific survival (MSS). Results: Manual quantification of PHH3-derived MR was associated with shorter PFS in thick melanomas (P=0.042). Computer-assisted quantification of PHH3 showed a significant association with shorter PFS and MSS in thick melanomas (P=0.021, P=0.04, respectively). Relative to manual analysis of corresponding H&amp;E-stained sections, manual analysis of PHH3 and MPM-2 stains increased mean MR (86% and 159% increases, respectively). MPM-2 did not have value as mitosis-specific marker in melanoma of any thickness. Conclusions: PHH3-derived MR might be useful in predicting worse clinical outcome in primary melanoma patients with thicker melanoma.

Automated measurement of uptake in cerebellum, liver, and aortic arch in full‐body FDG PET/CT scans

The purpose of this work was to develop and validate fully automated methods for uptake measurement of cerebellum, liver, and aortic arch in full-body PET/CT scans. Such measurements are of interest in the context of uptake normalization for quantitative assessment of metabolic activity and/or automated image quality control. Cerebellum, liver, and aortic arch regions were segmented with different automated approaches. Cerebella were segmented in PET volumes by means of a robust active shape model (ASM) based method. For liver segmentation, a largest possible hyperellipsoid was fitted to the liver in PET scans. The aortic arch was first segmented in CT images of a PET/CT scan by a tubular structure analysis approach, and the segmented result was then mapped to the corresponding PET scan. For each of the segmented structures, the average standardized uptake value (SUV) was calculated. To generate an independent reference standard for method validation, expert image analysts were asked to segment several cross sections of each of the three structures in 134 F-18 fluorodeoxyglucose (FDG) PET/CT scans. For each case, the true average SUV was estimated by utilizing statistical models and served as the independent reference standard. For automated aorta and liver SUV measurements, no statistically significant scale or shift differences were observed between automated results and the independent standard. In the case of the cerebellum, the scale and shift were not significantly different, if measured in the same cross sections that were utilized for generating the reference. In contrast, automated results were scaled 5% lower on average although not shifted, if FDG uptake was calculated from the whole segmented cerebellum volume. The estimated reduction in total SUV measurement error ranged between 54.7% and 99.2%, and the reduction was found to be statistically significant for cerebellum and aortic arch. With the proposed methods, the authors have demonstrated that automated SUV uptake measurements in cerebellum, liver, and aortic arch agree with expert-defined independent standards. The proposed methods were found to be accurate and showed less intra- and interobserver variability, compared to manual analysis. The approach provides an alternative to manual uptake quantification, which is time-consuming. Such an approach will be important for application of quantitative PET imaging to large scale clinical trials.

Automated Quantification of Optic Nerve Axons in Primate Glaucomatous and Normal Eyes—Method and Comparison to Semi-Automated Manual Quantification

To describe an algorithm and software application (APP) for 100% optic nerve axon counting and to compare its performance with a semi-automated manual (SAM) method in optic nerve cross-section images (images) from normal and experimental glaucoma (EG) nonhuman primate (NHP) eyes. ON cross sections from eight EG eyes from eight NHPs, five EG and five normal eyes from five NHPs, and 12 normal eyes from 12 NHPs were imaged at 100×. Calibration (n = 500) and validation (n = 50) image sets ranging from normal to end-stage damage were assembled. Correlation between APP and SAM axon counts was assessed by Deming regression within the calibration set and a compensation formula was generated to account for the subtle, systematic differences. Then, compensated APP counts for each validation image were compared with the mean and 95% confidence interval of five SAM counts of the validation set performed by a single observer. Calibration set APP counts linearly correlated to SAM counts (APP = 10.77 + 1.03 [SAM]; R(2) = 0.94, P < 0.0001) in normal to end-stage damage images. In the validation set, compensated APP counts fell within the 95% confidence interval of the SAM counts in 42 of the 50 images and were within 12 axons of the confidence intervals in six of the eight remaining images. Uncompensated axon density maps for the normal and EG eyes of a representative NHP were generated. An APP for 100% ON axon counts has been calibrated and validated relative to SAM counts in normal and EG NHP eyes.

Comparison of various methods for quantitative evaluation of myocardial infarct volume from magnetic resonance delayed enhancement data

BackgroundLate gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) enables the estimation of myocardial infarct (MI) extent. Nevertheless, manual quantification is time consuming and subjective. We sought to assess MI volume with different quantitative methods in both acute (AMI) and chronic MI (CMI). MethodsCMR was performed 50±21h after MI in 52 patients and was repeated 100±21days later in a subgroup of 34 patients. Then, necrosis volumes were quantified using: 1) manual delineation, 2) automated fuzzy c-means method, and 3) +2 to 6SD thresholding approaches. Results were compared against peak values of serum Troponin I (TnI), creatine kinase (CK) and left ventricular (LV) functional parameters: LV ejection fraction (LVEF), indexed end-diastolic (EDVi), end-systolic volumes (ESVi) and the number of hypokinetic segments (NbHk). ResultsFor CMI, quantitative evaluation of infarct size using manual, +2SD, +3SD and fuzzy c-means provided equivalent results in terms of correlation coefficients for comparisons of MI volumes against LV function parameters (LVEF: r>0.79, p<0.0001; ESVi: r>0.82, p<0.0001, EDVi: r>0.67, p<0.0001, NbHk: r>0.54, p<0.0009). For AMI, +2SD and fuzzy c-means approaches provided higher correlations for comparisons of AMI volumes against biochemical markers (CK: r>0.79, p<0.0001,TnI: r>0.77, p<0.0001) and chronic LV function parameters (LVEF: r>0.82, p<0.0001, NbHk: r>0.59, p<0.0002). ConclusionsThe fuzzy c-means and 2SD methods provided highest correlations with biochemical MI quantification as well as LV function parameters. The fuzzy c-means approach which does not require an arbitrary identification of the remote myocardium is fast and reproducible. It may be clinically useful in the evaluation of patients with MI.

Resolution of spectrally rank‐deficient multivariate curve resolution: alternating least squares components in comprehensive two‐dimensional liquid chromatographic analysis

The advancements in comprehensive two‐dimensional liquid chromatographic systems have led to more efficient and effective analyses of complex biological samples. Correspondingly, there has been an increase in the data complexity. Several chemometric techniques have been proposed to extract the maximum amount of information and to overcome the numerous challenges associated with data analysis. However, quantification remains troublesome, especially when dealing with spectrally rank‐deficient peaks. To this end, a new constraint for multivariate curve resolution‐alternating least squares (MCR‐ALS) is proposed for the separation of chromatographic peaks appearing within the same MCR‐ALS component and dividing them among additionally created components with highly similar spectral characteristics. Application of the constraint to every sample individually leads to two important advantages: the need for the selection of an overall dividing point for all samples is avoided, and the serious issue of retention time shifting is handled by the algorithm. Manual quantification of the compounds of interest is facilitated because the peaks in the second‐dimension chromatograms can be attributed to a single compound, but it leads to comparable percent relative standard deviations (% RSD) prior to and after application of the constraint. Nonetheless, the presence of a single compound in each component allows for automated quantification by simple summation of the second‐dimension chromatograms. Copyright © 2012 John Wiley &amp; Sons, Ltd.