Region Of Interest Selection Method Research Articles

Performance of T2 mapping in the staging of Graves' ophthalmopathy based on different region of interest selection methods.

Discriminating the stage of Graves' ophthalmopathy (GO) is crucial for clinical decision. Application of conventional T2-weighted imaging in the staging is still limited. To evaluate the performance of T2 mapping based on two different regions of interest (ROIs) for staging GO. In total, 56 GO patients were retrospectively enrolled and divided into two groups according to the clinical activity score (CAS). T2 relaxation time (T2RT) of extraocular muscle (EOM) on T2 mapping based on two different ROIs (T2RTROI-1: ROIs were drawn separately in the four EOMs; T2RTROI-2: ROI was drawn in the most inflamed EOM) was measured and compared between active and inactive groups. Both T2RTROI-1 and T2RTROI-2 values in the active GO were significantly higher than those of inactive GO (P <0.001). T2RTROI-1 and T2RTROI-2 values were positively correlated with CAS (rs=0.73, 0.69; P <0.001). When the T2RTROI-1 value of 83.3 ms and T2RTROI-2 value of 106.3 ms were used as cutoff values for staging GO, respectively, the best results were obtained with areas under the curve (AUCs) of 0.822 and 0.827. There was no significant difference for AUCs between T2RTROI-1 and T2RTROI-2 (P = 0.751). Excellent and good inter-observer agreements were achieved in quantitative measurements for T2RTROI-1 and T2RTROI-2 values, respectively, with intraclass correlation coefficients of 0.954 and 0.882. The T2RT values derived from two different ROIs were useful for assessment of disease activity. Taking reproducibility and diagnostic performance into consideration, T2RTROI-1 would be an ideal image biomarker for staging GO compared to T2RTROI-2.

Comparing region of interest selection and whole-field analysis for measurement of ciliary beat frequency in high-speed video analysis

BackgroundCiliary beat frequency (CBF) is crucial in mucociliary clearance. High-speed video analysis (HSVA) is commonly used to measure CBF but lacks standardization. We compared visual observation and computer-assisted calculation using fast Fourier transformation (FFT) in freshly collected bronchial ciliary epithelial cells and cultured cells. MethodsBronchial epithelial cells were obtained from 12 patients who required bronchoscopic examination. Eighty-five videos of ciliary movement of freshly collected and cultured cells were recorded and used to calculate CBF using manual observation, region of interest (ROI) selection, and whole-field analysis. ResultsCBF measured by the ROI selection method strongly correlated with that measured using manual observation, especially in freshly collected cells. However, 27.8% of the manual observation method values were doubled in the ROI selection method, probably because a round trip of cilia was calculated as two cycles and needed to be corrected to 1/2 value. Upon increasing the number of ROIs, the results of the ROI selection method came closer to that of WFA. ConclusionsComputer-assisted calculation using FFT can aid in measuring CBF; however, current methods require visual confirmation. Further automated evaluation techniques are needed to establish more standardized and generalized CBF measurement methods using HSVA.

Spirits quality classification based on machine vision technology and expert knowledge

By combining machine vision technology and expert knowledge, this paper proposes an online intelligent classification solution for Chinese spirits, which effectively improves the classification accuracy and production efficiency of spirits. Specifically, an intelligent spirits quality classification system is first designed, including spirits collectors, image sampling cameras, and computing devices. According to the principle that the size and shape of the bubbles in the spirits collector will change with the alcohol content in the spirits, a classification method of spirits quality based on the convolutional neural network (CNN) and bubble region of interest (ROI) selection is proposed. Furthermore, a post-processing method based on expert knowledge is proposed to improve the accuracy of the classification algorithm. A spirits quality classification dataset containing 139 119 images is created, and 15 CNNs are tested. Test results show that the highest spirits quality classification accuracy is 98.62% after using the bubble ROI selection method, and the highest classification accuracy reached 99.82% after adopting the post-processing method. Furthermore, practical application tests show that the solution proposed in this paper can improve spirits’ production quality and efficiency.

Apparent Diffusion Coefficient in the Differentiation of Common Pediatric Brain Tumors in the Posterior Fossa: Different Region-of-Interest Selection Methods for Time Efficiency, Measurement Reproducibility, and Diagnostic Utility.

This study aimed to explore the diagnostic ability of apparent diffusion coefficient (ADC) values obtained from different region of interest (ROI) measurements in tumor parenchyma for differentiating posterior fossa tumors (PFTs) and the correlations between ADC values and Ki-67. Seventy-three pediatric patients with PFTs who underwent conventional diffusion-weighted imaging were recruited in this study. Five different ROIs were manually drawn by 2 radiologists (ROI-polygon, ROI-3 sections, ROI-3-5 ovals, ROI-more ovals, and ROI-whole). The interreader/intrareader repeatability, time required, diagnostic ability, and Ki-67 correlation analysis of the ADC values based on these ROI strategies were calculated. Both interreader and intrareader reliabilities were excellent for ADC values among the different ROI strategies (intraclass correlation coefficient, 0.899-0.992). There were statistically significant differences in time consumption among the 5 ROI selection methods ( P < 0.001). The time required for the ROI-3-5 ovals was the shortest (32.23 ± 5.14 seconds), whereas the time required for the ROI-whole was the longest (204.52 ± 92.34 seconds). The diagnostic efficiency of the ADC values showed no significant differences among the different ROI measurements ( P > 0.05). The ADC value was negatively correlated with Ki-67 ( r = -0.745 to -0.798, all P < 0.0001). The ROI-3-5 ovals method has the best interobserver repeatability, the shortest amount of time spent, and the best diagnostic ability. Thus, it is considered an effective measurement to produce ADC values in the evaluation of pediatric PFTs.

A Novel Brain Tumor Detection and Coloring Technique from 2D MRI Images

The early automated identification of brain tumors is a difficult task in MRI images. For a long time, continuous research efforts have floated a new idea of replacing different grayscale anatomic regions of diagnostic images with appropriate colors that could overcome the problems being faced by radiologists. The colorization of grayscale images is challenging for enhancing various regions’ contrasts by transforming grayscale images into high-contrast color images. This study investigates standard solutions in discriminating between normal and abnormal regions by assigning colors to grayscale human brain MR images to differentiate different kinds of tissues. The proposed approach is influenced by connected component and index-based colorization methods for applying colors to different regions and abnormal areas. It is an automated approach that varies its inputs using luminance and pixel matrix values and provides the possible outcome. After segmentation, a specific algorithm is devised to colorize the region-of-interest (ROI) areas, which distinguishes and applies colors to differentiate the regions. Results show that implementing the watershed-based area segmentation method and ROI selection method based on the morphological operation helps identify tissues during processing. Moreover, the colorization approach based on luminance and pixel matrix after segmentation and ROI selection is beneficial due to better PSNR and SSIM values and visible contrast improvement. Our proposed algorithm works with less processing overhead and uses less time than those of the industry’s previously used color transfer method.

Using the apparent diffusion coefficient histogram analysis to predict response to neoadjuvant chemotherapy in patients with breast cancer: comparison among three region of interest selection methods.

BackgroundThe apparent diffusion coefficient (ADC) value using histogram analysis is helpful to predict responses to neoadjuvant chemotherapy (NAC) in breast cancer. However, the measurement method has not reached a consensus. This study was to assess the diagnostic performance of the ADC histogram analysis at predicting patient response prior to NAC in breast cancer patients using different region of interest (ROI) selection methods.MethodsA total of 75 patients who underwent diffusion weighted imaging (DWI) prior to NAC were retrospectively enrolled from February 2017 to December 2019. Images were measured using small 2-dimensional (2D) ROI, large 2D ROI, and volume ROI methods. The measurement time and ROI size were recorded. Histopathologic responses were acquired using the Miller-Payne grading system after surgery. The inter- and intra-observer repeatability was analyzed and the ADC histogram values from the three ROI methods were compared. The efficacy of each method at predicting patient response prior to NAC was assessed using the area under the receiver operating characteristic curve (AUC) for the whole study population and subgroups according to molecular subtype.ResultsAmong the 75 enrolled patients, 26 (34.67%) were responsive to NAC therapy. The ADC histogram values were significantly different among the three ROI methods (P≤0.038). Inter- and intra-observer repeatability of the large 2D ROI method and the volume ROI method was generally greater than that observed with the 2D ROI method. The 10% ADC value of the large 2D ROI method showed the greatest AUC (0.701) in the whole study population and in the luminal subgroup (AUC 0.804). The volume ROI method required significantly more time than the other two ROI methods (P<0.001).ConclusionsThe small 2D ROI method is not appropriate for predicting response prior to NAC in breast cancer patients due to the poor repeatability. When choosing the ROI method and the histogram parameters for predicting response prior to NAC in breast cancer patients using ADC-derived histogram analysis, 10% of the large 2D ROI method is recommended, especially in luminal A subtype patients.

Thermal imaging evaluation of the felines paw pad temperature before and after walking: A pilot study

The purposes of this pilot study were to explore the temperature changes of the paw pads in healthy cats before and after a 6-min walking test (6MWT), and to further compare the accuracy of the point and line region of interest (ROI) selection methods of the thermal imaging. Five healthy intact cats were recruited and trained to run on the treadmill, and to keep still during the standing phase on a glass panel when capturing thermographic imaging data. The paw pad temperature was recorded using a thermal camera and then analysed using both the point and line ROI before and after the 6MWT. The symmetry index (SI) between the limbs (left and right) was further calculated. Compared to the baseline, there were significant increases in the paw pad temperature of the forelimbs (point of left P &amp;lt; 0.001, point of right P = 0.048, point of average P = 0.002, line of left P &amp;lt; 0.001, line of right P = 0.007, line of average P &amp;lt; 0.001), hindlimbs (point of left P &amp;lt; 0.001, point of right P &amp;lt; 0.001, point of average P &amp;lt; 0.001, line of left P &amp;lt; 0.001, line of right P &amp;lt; 0.001, line of average P &amp;lt; 0.001), and average value after the 6MWT both using the point and line ROI selection methods. However, there is no temperature difference between the point and line ROI either before or after the 6MWT. In addition, no difference in the SI was found between the baseline and post-test or between the point and line ROI. These results preliminarily indicated that a 6WMT and thermal imaging could be a good combination for further clinical practices to recognise lameness or any other gait disabilities in cats, and both the point and line ROI selection methods can be considered when analysing thermographic data.

Evaluation of Renal Tissue Oxygenation Using Blood Oxygen Level-Dependent Magnetic Resonance Imaging in Chronic Kidney Disease

Background: Blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) has been widely used to assess renal oxygenation changes in different kidney diseases in recent years. This study was designed to evaluate and compare renal tissue oxygenation using 2 BOLD-MRI analysis methods, namely, the regional and whole-kidney region of interest (ROI) selection methods. Methods: The study ended up with 10 healthy controls and 40 chronic kidney disease (CKD) patients without dialysis. Their renal BOLD-MRI data were analyzed using whole-kidney ROI selection method and compared with regional ROI selection method. Results: We found the cortical, medullary, and whole-kidney R2* values were significantly higher in CKD patients than those in controls. Compared with the regional ROI selection method, the whole-kidney ROI selection method yielded higher cortical R2* values in both controls and CKD patients. The whole-kidney R2* values of deteriorating renal function group were significantly higher than those in stable renal function group. Conclusions: Cortical and medullary oxygenation was decreased significantly in CKD patients compared with the healthy controls, particularly in the medulla. The whole-kidney R2* values were positively correlated with kidney function and inversely correlated with the estimated glomerular filtration rate and effective renal plasma flow. Whole-Kidney R2* value might effectively predict the progression of renal function in patients with CKD.

Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability.

Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting IDH-1 mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data. Sixty-six patients (29 females; median age, 45years) with pathologically proved gliomas (low-grade gliomas, 36; high-grade gliomas, 30) were prospectively included, and their clinical data were collected. All patients underwent DKI examination. DKI maps of each metric were derived. Three groups of ROIs (ten spots, ROI-10s; three biggest tumor slices, ROI-3s; and whole-tumor parenchyma, ROI-whole) were manually drawn by two independent radiologists. The interobserver consistency, time spent, diagnostic efficacy, and survival analysis of DKI metrics based on these three ROI methods were analyzed. The intraexaminer reliability for all parameters among these three ROI methods was good, and the time spent on ROI-10s was significantly less than that of the other two methods (p < 0.001). DKI based on ROI-10s demonstrated a slightly better diagnostic value than the other two ROI methods for grading and predicting the IDH-1 mutation status of glioma, whereas DKI metrics derived from ROI-10s performed much better than those of the ROI-3s and ROI-whole in identifying 1p19q co-deletion. In survival analysis, the model based on ROI-10s that included patient age and mean diffusivity showed the highest prediction value (C-index, 0.81). Among the three ROI methods, the ROI-10s method had the least time spent and the best diagnostic value for a comprehensive evaluation of glioma. It is an effective way to process DKI data and has important application value in the clinical evaluation of glioma. • The intraexaminer reliability for all DKI parameters among different ROI methods was good, and the time spent on ROI-10 spots was significantly less than the other two ROI methods. • DKI metrics derived from ROI-10 spots performed the best in ROI selection methods (ROI-10s, ten-spot ROIs; ROI-3s, three biggest tumor slices ROI; and ROI-whole, whole-tumor parenchyma ROI) for a comprehensive evaluation of glioma. • The ROI-10 spots method is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.

Electrical equipment identification in infrared images based on ROI-selected CNN method

The thermographic evaluation method is widely used in substations to detect potential faults in advance. However, thousands of infrared images are expected for each substation. Almost all the data is collected, sorted and analyzed by humans. Therefore, to automate the data management and diagnosis, this paper proposes a method based on the deep convolutional neural network (CNN) for infrared image recognition of electrical equipment. Firstly, a deep CNN identification model is built based on MobileNet with initial weights in ImageNet. In addition, combined with the dataset augmentation including cropping, flipping, rotation and zoom, 3547 images of 500 kV substation equipment are used for training. Besides, a fine-tuning method is adopted to optimize the training. Finally, a fast region of interest (ROI) selection method based on hotspot sensitivity in infrared images is used to improve the identification accuracy. The original image aspect ratio is fixed throughout the process. Results demonstrate that the prediction accuracy of the proposed method reaches 97.72% in validation, and the ROI selection method improves the confidence by 8% in the test. As a result, this proposed method can promote the calculation efficiency, and has good application prospects in embedded devices such as cameras and substation robots.

A Comparison of Regions of Interest in Parenchymal Analysis for Breast Cancer Risk Assessment.

Computerized parenchymal analysis has shown potential to be utilized as an imaging biomarker to estimate the risk of breast cancer. Parenchymal analysis of digital mammograms is based on the extraction of computerized measures to build machine learning-based models for the prediction of breast cancer risk. However, the choice of the region of interest (ROI) for feature extraction within the breast remains an open problem. In this work we perform a comparison between five different methods suggested in the literature for automated ROI selection, including the whole breast (WB), the maximum squared (MS), the retro-areolar region (RA), the lattice-based (LB), and the polar-based (PB) selection methods. For the experiments, we built a retrospective dataset of 896 screening mammograms from 224 women (112 cases and 112 healthy controls). The performance of each ROI selection method was measured in terms of the area under the curve (AUC) values. The AUC values varied between 0.55 and 0.79 depending on the method and experimental settings. The best performance on an independent test set was achieved by the MS method (AUC of 0.59, 95% CI: 0.55-0.64). This method is fully-automated and does not require adjusting hyper-parameters. Based on our results, we prompt the use of the MS method for ROI selection in the computerized parenchymal analysis for breast cancer risk assessment.

Evaluation of Thermographic Imaging in Canine Hindlimb Muscles After 6 Min of Walking-A Pilot Study.

Objective: To evaluate changes in superficial temperature of hindlimb muscles before and after a 6-min walk in healthy dogs.Methods: Two infrared thermographic images were captured of the proximal and distal hindlimbs of 11 healthy dogs before and after a 6-min walk. Orthopedic exam and objective gait analysis confirmed the healthy status of study subjects. Superficial temperatures of the gastrocnemius, biceps femoris, and gracilis were assessed. Analysis of images was performed using 2 different methods of region of interest (ROI) selection. ROI were selected first using one point (single pixel) in the muscle and then separately by selecting a line (LN) corresponding to many points of each muscle belly from which an average was taken. P < 0.05 was considered significant.Results: There was no significant change in temperature using point ROI before and after 6 min of walking of the gastrocnemius, gracilis, and biceps femoris muscles (p = 0.273, p = 0.349, p = 0.351, respectively). Using linear ROI, both biceps femoris and gracilis muscles exhibited significant increases in temperature (p < 0.0001, p = 0.032, respectively). There was no significant increase in temperature of gastrocnemius muscle for both point and linear ROI selection (p = 0.273, p = 0.448, respectively). The right biceps femoris temperatures were higher compared to left biceps femoris using the linear ROI before and after walks (p < 0.0001). The overall (left and right limbs pooled) standard deviation of point selected values were greater than LN selected values of the biceps femoris (1.35 and 1.11) and gastrocnemius (1.51 and 1.23). In contrast, standard deviation for the gracilis measurements were decreased using point selection vs. LN selection (1.09 and 1.3).Conclusions: The biceps femoris and gracilis muscles demonstrated significant increases in surface temperature after 6 min of walking using the linear method of ROI. Measurement of numerous points along the entire length of the biceps femoris and gastrocnemius muscles may provide a more accurate assessment of the increased vascularity within the tissues resulting from work compared to single point selection.Clinical Significance: Prior activity and ROI selection method should be considered when interpreting thermography results.

Utility of T2 mapping in the staging of thyroid-associated ophthalmopathy: efficiency of region of interest selection methods.

Discriminating the stage of thyroid-associated ophthalmopathy (TAO) is crucial for the treatment strategy and prognosis prediction. Utility of conventional magnetic resonance imaging in the disease staging is limited. To investigate the performance of T2 mapping based on different region of interest (ROI) selection methods in the staging of TAO. Thirty-two patients with TAO were retrospectively enrolled. Two radiologists independently measured the T2 relaxation time (T2RT) of extraocular muscles using two different ROIs (hotspot [ROIHS]: T2RT-hot; single-slice [ROISS]: T2RT-mean, T2RT-max, T2RT-min). Independent-samples t test, Wilcoxon signed rank test, Spearman correlation analysis, receiver operating characteristic (ROC) curves analyses, multiple ROC comparisons, and intra-class correlation coefficient (ICC) were used for statistical analyses. No significant difference was found in the measuring time between ROIHS and ROISS methods (P = 0.066). T2RT-mean demonstrated the highest ICC for measurement, followed by T2RT-max and T2RT-min, and T2RT-hot showed the poorest reproducibility. Active TAOs showed significantly higher values for all the T2RTs than inactive mimics (all P < 0.001). Significant positive correlations were found between T2RTs and CAS (all P < 0.005). T2RT-hot and T2RT-max showed significantly higher areas under the curve than that of T2RT-mean (P = 0.013 and 0.024, respectively), while the difference between T2RT-hot and T2RT-max was not significant (P = 0.970). The T2RTs derived from both ROI selection methods could be useful for the staging of TAO. The results of measuring time, reproducibility, and diagnostic performance suggest that T2RT-max would be the optimal indicator for staging.

Comparing attenuations of malignant and benign solitary pulmonary nodule using semi-automated region of interest selection on contrast-enhanced CT.

The purpose of this study was to determine whether semi-automated region of interest (ROI) measurement of CT attenuations of solitary pulmonary nodule (SPN) is an accurate approach in differentiating malignant from benign SPN. Ninety cases of pathologically proven SPN were retrospectively reviewed. CT attenuations of SPN before and after contrast injection were measured using semi-automated ROI selection method. Attenuations within a range of -100 to 200 Hounsfield units (HU) as soft tissue density range were set. The ROI included the entire SPN regardless of its internal soft tissue contents after automatic elimination of airs, calcific, or bony densities. There were 42 (46.7%) malignant SPN and 48 (53.3%) benign SPN, which were grouped into A (18 tuberculoma, 13 fungus), B (5 focal organizing pneumonia, 5 abscess), and C (7 other benign tumors). The malignant SPN showed significantly higher mean attenuations of enhancement and net-enhancement than all benign SPN (P<0.001). Using the area under the receiver operating characteristic curve (AUC), the cut-off net-enhancement of 15 HU gave 83% sensitivity, 65% specificity and 73% accuracy for predicting malignancy. Malignant SPN (mean 67.9 HU) had significantly higher enhancement than group A (mean 52.6 HU, P<0.001, 95% CI: 8.73, 21.81) and group B (mean 57.0 HU, P=0.025, 95% CI: -1.43, 20.34) while group C showed no significant difference (mean 68.1 HU, P=0.97). Net enhancements were higher in group B (mean 18.8 HU) than in group A (mean 8.8 HU) (P<0.001, 95% CI: 11.8, 23.18). The semi-automated ROI measurement of SPN's attenuations on CT is an accurate approach in distinguishing indeterminate SPN.

Histogram analysis of apparent diffusion coefficient maps in the prognosis of patients with locally advanced head and neck squamous cell carcinoma: Comparison of different region of interest selection methods

ObjectiveTo investigate the influence of different region of interest (ROI) selection methods on the histogram analysis of apparent diffusion coefficient (ADC) maps and to compare their performance in predicting overall survival (OS) in patients with locally advanced head and neck squamous cell carcinoma (HNSCC). MethodsA total of 73 patients with locally advanced HNSCC who underwent pretreatment diffusion-weighted magnetic resonance imaging were included. Based on the largest slice ROI (ROILS) and whole tumor ROI (ROIWT), ADC histogram parameters including mean ADC (ADCmean); median ADC (ADCmedian); 10th, 25th, 75th, and 90th percentiles of ADC values (ADC10, ADC25, ADC75, and ADC90); kurtosis; and skewness were obtained. Intraclass correlation coefficients (ICCs) and Bland–Altman plots were used to evaluate measurement reproducibility. The association of ADC histogram parameters and clinicopathological factors with OS was analyzed using log-rank tests and Cox regression. ResultsThe measurements of ADC histogram parameters based on ROIWT showed better reproducibility than ROILS (ICCs for ROIWT: 0.772–0.961; ICCs for ROILS: 0.511–0.851). The higher ADC values (ADCmean, ADCmedian, ADC10, and ADC25 based on both ROIs; ADC75 based on ROILS) and lower kurtosis based on ROILS were significantly associated with worse OS of patients with locally advanced HNSCC (all P < 0.05). In the multivariate Cox analysis, ADC10 measured with ROIWT (P = 0.019, hazard ratio = 2.63, 95% confidence interval 1.17–5.90) was an independent prognostic factor after adjusting for clinicopathological factors. ConclusionsROI selection methods could influence ADC histogram analysis. ADC10 based on ROIWT had the best independent prognostic value for patients with locally advanced HNSCC.

Dynamic contrast-enhanced MRI in orbital lymphoproliferative disorders: Effects of region of interest selection methods on time efficiency, measurement reproducibility, and diagnostic ability.

Previous studies indicated that dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could serve as a useful biomarker for differentiating malignant from benign orbital lymphoproliferative disorders (OLPDs). To investigate the influence of different region of interest (ROI) selection methods on the measurements of DCE-MRI parameters, and their diagnostic ability in discriminating malignant from benign OLPDs. Retrospective study. In all, 46 patients with OLPDs (22 benign and 24 malignant). 3.T DCE-MRI using a 2D turbo fast low angle shot sequence postcontrast. DCE-MRI data were analyzed using three different ROI selection methods, including whole-tumor ROI (ROIWT ), single-slice ROI (ROISS ) and hot-spot ROI (ROIHS ). Quantitative parameters (Ktrans , Kep , Ve ) were calculated based on a modified Tofts model. Analysis of variance test, intraclass correlation coefficient (ICC), Bland-Altman plots, independent t-test, and receiver operating characteristic curve analyses were used for statistical analyses. The time required for outlining ROIWT was significantly longer than ROISS and ROIHS (P < 0.001). The measurements of DCE-MRI-derived parameters based on ROIHS demonstrated lowest ICC, followed by ROISS and ROIWT . Malignant OLPDs showed significantly higher Kep than benign mimics (P < 0.001), while no significant differences were found on Ktrans (ROIWT , P = 0.535; ROISS , P = 0.557; ROIHS , P = 0.400) and Ve (ROIWT , P = 0.071; ROISS , P = 0.079; ROIHS , P = 0.057). Kep -ROIWT showed the highest area under curve for differentiating malignant from benign OLPDs, followed by Kep -ROISS , and Kep-ROIHS ; however, the differences were not significant (ROIWT vs. ROISS , P = 0.407; ROIWT vs. ROIHS , P = 0.363; ROISS vs. ROIHS , P = 0.887). ROI selection methods could have an influence on the measurements of DCE-MRI parameters. Taking measurement time, reproducibility, and diagnostic ability into account, we suggest single-slice ROI to be used for differentiating malignant from benign OLPDs in clinical practice. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1298-1305.

Mapping of human brown adipose tissue in lean and obese young men

Human brown adipose tissue (BAT) can be activated to increase glucose uptake and energy expenditure, making it a potential target for treating obesity and metabolic disease. Data on the functional and anatomic characteristics of BAT are limited, however. In 20 healthy young men [12 lean, mean body mass index (BMI) 23.2 ± 1.9 kg/m2; 8 obese, BMI 34.8 ± 3.3 kg/m2] after 5 h of tolerable cold exposure, we measured BAT volume and activity by 18F-labeled fluorodeoxyglucose positron emission tomography/computerized tomography (PET/CT). Obese men had less activated BAT than lean men (mean, 130 vs. 334 mL) but more fat in BAT-containing depots (mean, 1,646 vs. 855 mL) with a wide range (0.1-71%) in the ratio of activated BAT to inactive fat between individuals. Six anatomic regions had activated BAT-cervical, supraclavicular, axillary, mediastinal, paraspinal, and abdominal-with 67 ± 20% of all activated BAT concentrated in a continuous fascial layer comprising the first three depots in the upper torso. These nonsubcutaneous fat depots amounted to 1.5% of total body mass (4.3% of total fat mass), and up to 90% of each depot could be activated BAT. The amount and activity of BAT was significantly influenced by region of interest selection methods, PET threshold criteria, and PET resolutions. The present study suggests that active BAT can be found in specific adipose depots in adult humans, but less than one-half of the fat in these depots is stimulated by acute cold exposure, demonstrating a previously underappreciated thermogenic potential.

Ultrasound estimates of muscle quality in older adults: reliability and comparison of Photoshop and ImageJ for the grayscale analysis of muscle echogenicity.

Background. Quantitative diagnostic ultrasound imaging has been proposed as a method of estimating muscle quality using measures of echogenicity. The Rectangular Marquee Tool (RMT) and the Free Hand Tool (FHT) are two types of editing features used in Photoshop and ImageJ for determining a region of interest (ROI) within an ultrasound image. The primary objective of this study is to determine the intrarater and interrater reliability of Photoshop and ImageJ for the estimate of muscle tissue echogenicity in older adults via grayscale histogram analysis. The secondary objective is to compare the mean grayscale values obtained using both the RMT and FHT methods across both image analysis platforms.Methods. This cross-sectional observational study features 18 community-dwelling men (age = 61.5 ± 2.32 years). Longitudinal views of the rectus femoris were captured using B-mode ultrasound. The ROI for each scan was selected by 2 examiners using the RMT and FHT methods from each software program. Their reliability is assessed using intraclass correlation coefficients (ICCs) and the standard error of the measurement (SEM). Measurement agreement for these values is depicted using Bland-Altman plots. A paired t-test is used to determine mean differences in echogenicity expressed as grayscale values using the RMT and FHT methods to select the post-image acquisition ROI. The degree of association among ROI selection methods and image analysis platforms is analyzed using the coefficient of determination (R2).Results. The raters demonstrated excellent intrarater and interrater reliability using the RMT and FHT methods across both platforms (lower bound 95% CI ICC = .97–.99, p < .001). Mean differences between the echogenicity estimates obtained with the RMT and FHT methods was .87 grayscale levels (95% CI [.54–1.21], p < .0001) using data obtained with both programs. The SEM for Photoshop was .97 and 1.05 grayscale levels when using the RMT and FHT ROI selection methods, respectively. Comparatively, the SEM values were .72 and .81 grayscale levels, respectively, when using the RMT and FHT ROI selection methods in ImageJ. Uniform coefficients of determination (R2 = .96–.99, p < .001) indicate strong positive associations among the grayscale histogram analysis measurement conditions independent of the ROI selection methods and imaging platform.Conclusion. Our method for evaluating muscle echogenicity demonstrated a high degree of intrarater and interrater reliability using both the RMT and FHT methods across 2 common image analysis platforms. The minimal measurement error exhibited by the examiners demonstrates that the ROI selection methods used with Photoshop and ImageJ are suitable for the post-acquisition image analysis of tissue echogenicity in older adults.

Development of an automated region of interest selection method for 3D surface monitoring of head motion

To simplify the often complex and user-dependent manual region of interest (ROI) selection process for head motion monitoring, an automatic ROI selection method was developed. The automatic ROI selection algorithm calculated the displacements and velocities of 3D surface points between a temporally correlated 3D image series and a reference image. Only facial surfaces satisfying certain spatial and temporal criteria were selected. The algorithm was tested on five healthy volunteers instructed to perform different types of facial movements for a total of 27 real-time image sets (40-120 images for each image set). The algorithm detected and excluded surface areas affected by different types of local facial movements that were independent of actual net head motion. Eye, eyebrow, and mandible motion were most commonly detected as being independent of head motion and were excluded from the final ROI. For 3D images taken with substantial facial or whole head motion, either most of the facial area was excluded or only small areas with random patterns were included in the final ROI. Surface image registration using iterative closest point (ICP) methods showed more stable real-time head tracking using the automatically selected ROI than manual user defined ROIs. The automatic selection method successfully found ROIs stable over time for tracking head motion by excluding locally varying facial motions. By automating the ROI selection process, it is feasible that the time and complexity of current ROI definition can be reduced, together with user-dependent registration errors.

Systematic method to assess microvascular recruitment using contrast-enhanced ultrasound. Application to insulin-induced capillary recruitment in subjects with T1DM

Contrast-enhanced ultrasound (CEU) is an ultrasound imaging technique used to assess tissue perfusion. Analysis of microvascular recruitment necessitates the definition of a region of interest (ROI) containing exclusively the tissues to be studied. Conventional ROI selection requires examining the images and drawing the ROI by hand, making the analysis of CEU images non-reproducible and analyst-dependent. We have designed a systematic ROI selection method that is both reproducible and analyst-independent. Microvascular blood volume (MBV) assessed in 21 sequences of images was used to correlate the systematic ROI selection method with the conventional method performed by two independent analysts (correlation of 0.88 and 0.87 respectively) and the MBV sample distribution from the systematic method was not significantly different from those obtained from the conventional one. Using the systematic method, we found no significant insulin-induced capillary recruitment in subjects with type 1 diabetes mellitus, which might be related to the observed low glucose uptake during the hyperinsulinemic euglycemic clamp compared to healthy patients.