Region Of Interest Method Research Articles

Examining the Influence of Season Phase, Age, and Anthropometrics on Body Composition Trends in National Basketball Association Athletes.

Russell, JL, Baker, BS, Mercer, RA, and McLean, BD. Examining the influence of season phase, age, and anthropometrics on body composition trends in NBA athletes. J Strength Cond Res XX(X): 000-000, 2024-This study aimed to describe seasonal body composition changes in NBA athletes using Dual-energy X-ray Absorptiometry (DXA) and to explore the relationship between these changes and factors such as age and anthropometrics. A retrospective analysis was conducted using 402 DXA scans from 62 professional male basketball players, obtained from one NBA team between 2012 and 2023. Seasonal phases were defined as preseason, pre all-star, post all-star, and offseason. A custom region of interest (ROI) method was used to ensure consistent scan areas for taller athletes who exceeded the DXA bed length. Linear mixed models analyzed the influence of seasonal phases, age, and height on lean mass (LM), fat mass (FM), and bone mineral content (BMC). In-season phases (pre all-star and post all-star) showed small increases in LM and decreases in FM compared with the preseason, with no significant BMC changes. Chronological age was associated with increases in LM but not FM. Taller athletes exhibited increases in LM, FM, and BMC. Significant random variance across players and seasons indicated that additional unmeasured factors might influence body composition. NBA athletes generally gain LM and lose FM during the season, with older players showing increased LM gain. The study's novel ROI method provide reliable data for athletes exceeding standard DXA limits, highlighting the importance of tailored body composition assessments in professional basketball. These findings can inform strength and conditioning, nutrition, and player development strategies across different seasonal phases and ages.

The association between frontotemporal connecting fibers and seizure severity in patients with temporal lobe epilepsy

The patients with temporal lobe epilepsy (TLE) admitted in the Department of Neurology, Zhongshan Hospital, Fudan University from June 2009 to February 2012 were prospectively enrolled. The diffusion tensor imaing was performed on the patients at the time of enrollment and 3 years later. The fractional anisotropy (FA) values of the white matter connecting fibers(bilateral hooked, arcuate, cingulate, and superior longitudinal tracts), the connecting fibers of both hemispheres(anterior union, anterior callosal forceps, posterior forceps, and bilateral fornix), and fibers of perirhinal cortices system(bilateral radiating crown and anterior limb of the internal capsule) were measured by the region of interest method. The severity of epilepsy was evaluated using the Veterans Administration Seizure Type and Frequency Rating Scale(VA-2) and National Hospital Seizure Severity Scale (NHS3). A total of 51 patients with TLE were screened, with 27 patients completing the 3-year follow-up. There were 13 males and 14 females with an age of (32±11) years and a follow-up duration of (39.1±1.1) months. During the follow-up, 6 patients had increased/unchanged NHS3 or VA-2 scores, while 21 patients had decreased scores. Three years later, the FA values of the bilateral arcuate fasciculus, the right superior longitudinal fasciculus, the right radial coronal and corpus callosum anterior forceps in TLE patients decreased compared to baseline(P<0.05). However, compared to the patients with decreased VA-2 scores during the follow-up, the degree of increase in FA values (ΔFA, follow-up FA value-baseline FA value) of the ipsilateral hook bundle caused by epilepsy was more significant in the group with increased/unchanged VA-2 scores (decreased score group vs increased/unchanged score group:-0.032±0.063 vs 0.018±0.043, t=2.305, P=0.035). The value of ΔFA in epileptic patients with increased/unchanged NHS3 scores (0.075±0.113) was higher compared to those with decreased scores (-0.079±0.099, t=2.804, P=0.010). Correlation analysis also showed the changes in FA values of epileptic lateral fasciculus (r=0.503, P=0.009) and arcuate fasciculus (r=0.602, P=0.001)were positively correlated with the changes in VA-2 and HNS3 scores, respectively. The seizure severity in patients with TLE was closely associated with the microstructure changes in the frontal and temporal white matter, especially the arcuate and uncinate tracts, on the same side that caused seizures, which may indicate the white matter remodeling and abnormal network reformation associated with seizures.

Preoperative assessment of bone density for dental implantation: a comparative study of three different ROI methods

BackgroundDental cone beam computed tomography (CBCT) is commonly used to evaluate cancellous bone density before dental implant surgery. However, to our knowledge, no measurement approach has been standardized yet. This study aimed to evaluate the relationship between three different regions of interest (ROI) methods on cancellous bone density at the dental implant site using dental CBCT images.MethodsPatients’ dental CBCT images (n = 300) obtained before dental implant surgery were processed using Mimics (Materialise, Leuven, Belgium). At the potential implant sites, the rectangle, cylinder, and surrounding cylinder ROI methods were used to measure bone density. Repeated measures one-way analysis of variance was performed to compare the three ROI methods in terms of measurement results. Pearson correlation analysis was performed to identify the likely pair-wise correlations between the three ROI methods.ResultsThe density value obtained using the surrounding cylinder approach (grayscale value [GV],523.56 ± 228.03) was significantly higher than the values obtained using the rectangle (GV, 497.04 ± 236.69) and cylinder (GV,493 ± 231.19) ROI methods in terms of results. Furthermore, significant correlations were noted between the ROI methods (r > 0.965; p < 0.001).ConclusionsThe density measured using the surrounding cylinder method was the highest. The choice of method may not influence the trends of measurement results.Trial registrationThis study was approved by the Institutional Review Board of China Medical University Hospital, No. CMUH111-REC3-205. Informed consent was waived by the Institutional Review Board of China Medical University Hospital, CMUH111-REC3-205, owing to the retrospective nature of the study.

Does volumetric measurement of ADC values achieve higher diagnostic performance in bladder cancer MRI?

Apparent diffusion coefficient (ADC) value is an important part of bladder cancer magnetic resonance imaging (MRI) assessment and can predict the aggressive and invasive potentials. There is growing interest in whole tumor volume measurements. To investigate if the volumetric ADC measurement method will significantly exceed the diagnostic performance of the selected region of interest (ROI) method in everyday practice. A prospective evaluation was carried out of 50 patients with bladder cancer by two radiologists. The mean and the minimum ADC values were measured using both methods. The inter-reader agreement was determined by the intraclass correlation coefficient. The ADC values were compared between different grades, states of muscle invasion, and lympho-vascular invasion (LVI); then, validity was evaluated using receiver operating characteristic (ROC) curves. Areas under the curve (AUC) were then compared for the level of statistical significance. The inter-observer agreement was excellent for the ADC values using both methods. The volumetric measurement provides higher mean and lower minimum ADC values with statistically significant differences (P <0.00001). The highest diagnostic accuracy for differentiating tumor grade and predicting muscle invasion was for the minimum ADC by a selected ROI. However, the differences between the achieved AUCs were of no statistical significance. None of the ADC values predicted LVI with statistical significance. The selected ROI and volumetric measurement methods of mean and minimum ADC in bladder cancer yield different values, still having comparable diagnostic performance with accurate ROI sampling. The minimum ADC value by ROI is preferred in everyday clinical practice.

Quantitative Assessment of Breast Tumor: Comparison of Four Methods of Positioning Region of Interest for Synthetic Relaxometry and Diffusion Measurement

ObjectivesTo compare the differences in apparent diffusion coefficient (ADC) and synthetic magnetic resonance (MR) measurements of four region of interest (ROI) placement methods for breast tumor and to investigate their diagnostic performance. Methods110 (70 malignant, 40 benign) newly diagnosed breast tumors were evaluated. The patients underwent 3.0T MR examinations including diffusion-weighted imaging and synthetic MR. Two radiologists independently measured ADCs, T1 relaxation time (T1), T2 relaxation time (T2), and proton density (PD) using four ROI methods: round, square, freehand, and whole-tumor volume (WTV). The interclass correlation coefficient (ICC) was used to assess their measurement reliability. Diagnostic performance was evaluated using multivariate logistic regression analysis and the receiver operating characteristic (ROC) curves. ResultsThe mean values of all ROI methods showed good or excellent interobserver reproducibility (0.79~0.99) and showed the best diagnostic performance compared to the minimum and maximum values. The square ROI exhibited superior performance in differentiating between benign from malignant breast lesions, followed by the freehand ROI. T2, PD, and ADC values were significantly lower in malignant breast lesions compared to benign ones for all ROI methods (p < 0.05). Multiparameters of T2 + ADC demonstrated the highest AUC values (0.82~0.95), surpassing the diagnostic efficacy of ADC or T2 alone (p < 0.05). ConclusionsROI placement significantly influences ADC and synthetic MR values measured in breast tumors. Square ROI and mean values showed superior performance in differentiating benign and malignant breast lesions. The multiparameters of T2 + ADC surpassed the diagnostic efficacy of a single parameter.

Assessment of hiatus defect size in hiatal hernia patients using computed tomography

Objectives: The aim of this study is to investigate the hiatus defect diameter by measuring on multi-detector computed tomography images in hiatal hernia patients. Methods: The multi-detector computed tomography images of 50 patients and 50 individuals in control group included in this study were investigated. The hiatus surface area (cm²), hiatus antero-posterior and transverse diameters (cm), and the thickness of both diaphragmatic crura (mm) were measured by reformatting contrast-enhanced thoraco-abdomino-pelvic computed tomography images using the region of interest method. Results: In this study, a significant difference was obtained among groups according to hiatus surface area, hiatus antero-posterior, and transverse diameter measurements, and both left and right diaphragmatic crural thickness measurements (P&amp;lt;0.001). In the patient group, the cut-off values were determined by using ROC analysis, and the values above these cut-off values enabled a hernia diagnosis with high sensitivity and specificity. Conclusions: Measuring the hiatus surface area on multi-detector computed tomography images could serve as a supplementary criterion for diagnosing of hiatal hernia.

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy.

Accurate and efficient dose calculation is essential for on-line adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results in photon therapy. However, there is a scarcity of DL-based dose prediction methods specifically designed for proton therapy. Successful dose prediction method for proton therapy should account for more challenging dose prediction problems in pencil beam scanning proton therapy (PBSPT) due to its sensitivity to heterogeneities. To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. PBSPT plans of 103 prostate cancer patients (93 for training and the other 10 for independent testing) and 83 lung cancer patients (73 for training and the other 10 for independent testing) previously treated at our institution were included in the study, each with computed tomography scans (CTs), structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine (considered as the ground truth in the model training and testing). For the ablation study, we designed three experiments corresponding to the following three methods: (1) Experiment 1, the conventional region of interest (ROI) (composed of targets and organs-at-risk [OARs]) method. (2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. (3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates with a criterion of 3%/3mm/10%, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency. Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices (for lung cancer, CTV D98 absolute deviation: 0.74 ± 0.18 vs. 0.57 ± 0.21 vs. 0.54 ± 0.15 Gy[RBE], ROI vs. beam mask vs. sliding window methods, respectively). For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method improved the passing rates in these regions and the sliding window method further improved them (for prostate cancer, targets: 96.93% ± 0.53% vs. 98.88% ± 0.49% vs. 99.97% ± 0.07%, BODY: 86.88% ± 0.74% vs. 93.21% ± 0.56% vs. 95.17% ± 0.59%). A similar trend was also observed for the dice coefficients. This trend was especially remarkable for relatively low prescription isodose lines (for lung cancer, 10% isodose line dice: 0.871 ± 0.027 vs. 0.911 ± 0.023 vs. 0.927 ± 0.017). The dose predictions for all the testing cases were completed within 0.25s. An accurate and efficient deep learning-augmented proton dose prediction framework has been developed for PBSPT, which can predict accurate dose distributions not only inside but also outside ROI efficiently. The framework can potentially further reduce the initial planning and adaptive replanning workload in PBSPT.

Diffusion tensor imaging techniques show that parkin gene S/N167 polymorphism is responsible for extensive brain white matter damage in patients with Parkinson's disease

ObjectiveTo explore the influence of disease and genetic factors on the white matter microstructure in patients with PD. The white matter microstructural changes in the substantia nigra-striatum system were detected by diffusion tensor imaging (DTI) using the region of interest (ROI) and diffusion tensor tracer (DTT) methods. MethodsPatients with primary Parkinson's disease (PD) without a family history of PD were selected and divided into PD-G/G and PD-G/A groups according to their parkin S/N167 polymorphism. Control groups matched for age, sex, and gene type (G/G and G/A) were also included. Three-dimensional brain volume imaging (3D-BRAVO) and DTI were performed. The microstructural changes in the substantia nigra-striatum system were evaluated by the ROI and DTT methods. The Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Hoehn-Yahr (H–Y) staging, and the third part of the Unified Parkinson's Disease Rating (UPDRS-III) scales evaluated the cognitive and motor function impairment in patients with PD. Independent samples t-test compared normally-distributed data, and the Wilcoxon rank sum test compared measurement or categorical non-normally distributed data. Multiple regression analysis was used to analyze the correlation between various DTI indicators and the MMSE, MoCA, UPDRS-III, and H–Y scores in the PD-G/G and PD-G/A groups. P < 0.05 was considered statistically significant. ResultsThe white matter microstructural changes in the nigrostriatal pathway differed significantly between the PD or PD-G/A and the control group (P < 0.05)The ROI method showed that the left globus pallidus radial diffusivity (RD) value was negatively correlated with the MMSE score (r = −0.404, P = 0.040), and the left substantia nigra (LSN) fractional anisotropy (FA) value was positively correlated with the MoCA score (r = 0.405, P = 0.040) and negatively with the H–Y stage (r = −0.479, P = 0.013).The DTT method showed that the MMSE score was positively correlated with the right substantia nigra (RSN) FA value (r = 0.592, P = 0.001) and negatively with its RD value (r = −0.439, P = 0.025). The H–Y grade was negatively correlated with the number of fibers in the RSN (r = −0.406, P = 0.040). The UPDRS-Ⅲ score was positively correlated with the mean diffusivity (r = 0.420, P = 0.033) and RD (r = 0.396, P = 0.045) values of the LSN, and the AD value of the RSN (r = 0.439, P = 0.025). ConclusionThe DTI technique detected extensive white matter fiber damage in patients with PD, primarily in those with the G/A genotype, that led to motor and cognitivesymptoms.

Problem Solving MRI to Reduce False-Positive Biopsy Related to Breast US: Conductivity vs. DWI vs. Abbreviated Contrast-Enhanced MRI.

While breast ultrasound (US) is a useful tool for diagnosing breast masses, it can entail false-positive biopsy results because of some overlapping features between benign and malignant breast masses and subjective interpretation. To evaluate the performance of conductivity imaging for reducing false-positive biopsy results related to breast US, as compared to diffusion-weighted imaging (DWI) and abbreviated MRI consisting of one pre- and one post-contrast T1-weighted imaging. Prospective. Seventy-nine women (median age, 44 years) with 86 Breast Imaging Reporting and Data System (BI-RADS) category 4 masses as detected by breast US. 3-T, T2-weighted turbo spin echo sequence, DWI, and abbreviated contrast-enhanced MRI (T1-weighted gradient echo sequence). US-guided biopsy (reference standard) was obtained on the same day as MRI. The maximum and mean conductivity parameters from whole and single regions of interest (ROIs) were measured. Apparent diffusion coefficient (ADC) values were obtained from an area with the lowest signal within a lesion on the ADC map. The performance of conductivity, ADC, and abbreviated MRI for reducing false-positive biopsies was evaluated using the following criteria: lowest conductivity and highest ADC values among malignant breast lesions and BI-RADS categories 2 or 3 on abbreviated MRI. One conductivity parameter with the maximum area under the curve (AUC) from receiver operating characteristics was selected. A P-value <0.05 was considered statistically significant. US-guided biopsy revealed 65 benign lesions and 21 malignant lesions. The mean conductivity parameter of the single ROI method was selected (AUC = 0.74). Considering conductivity (≤0.10 S/m), ADC (≥1.60 × 10-3 mm2 /sec), and BI-RADS categories 2 or 3 reduced false-positive biopsies by 23% (15 of 65), 38% (25 of 65), and 43% (28 of 65), respectively, without missing malignant lesions. Conductivity imaging may show lower performance than DWI and abbreviated MRI in reducing unnecessary biopsies. 2 TECHNICAL EFFICACY: Stage 2.

ROI-BMC-DNNet: An efficient automatic analysis model of whole-slide scanned bone marrow aspirate images for the diagnosis of hematological disorders

Cell differential counting in the scanned whole-slide image (WSI) is essential but still challenging in the intelligent diagnosis of hematological disorders based on bone marrow aspirate smear images. The huge size and the complex content of WSI pose multiple obstacles to training an efficient and accurate analysis model. This paper proposes a novel analysis framework termed ROI-BMC-DNNet. To capture patch images in the cell well-spread areas, we provide a pyramid segmentation network based on prior pathological knowledge to automatically locate regions of interest (ROI) that pathologists usually manually focus on to count nucleated cells. Specifically, we describe a simple but effective alignment method for multiscale ROI, aiming to maintain the accuracy of the semantic and the spatial information of the pyramid network. Furthermore, we design a patch sampling algorithm and a patch quality evaluating network to ensure the effectiveness of the sampled patches. Additionally, we provide a cell detection model to realize the automatic differential counting of the nucleated cells in the sampled patches. Experiments are conducted on the datasets of scanned WSIs from 120 patients. Our method achieves 92.27% retrieval precision for the high-quality patches (patches with nucleated cells with better morphology and without particles). When the missed nucleated cells are evaluated, the detection Recall and Precision of the bone marrow nucleated cells from the WSIs are 87.13% and 87.90% respectively. Notably, the recognition Recall and Precision of the blast cell are 91.37% and 90.47%, respectively. The results confirm that the proposed method is promising to work as an auxiliary diagnostic tool for hematological disorders.

Quantitative susceptibility mapping shows lower brain iron content in children with childhood epilepsy with centrotemporal spikes.

The dysregulation of brain iron homeostasis is closely relevant to a multitude of chronic neurological disorders. This study employed quantitative susceptibility mapping (QSM) to detect and compare whole-brain iron content between childhood epilepsy with centrotemporal spikes (CECTS) children and typically developing children. 32 children with CECTS and 25 age- and gender-matched healthy children were enrolled. All participants were imaged with 3.0-T MRI to acquire the structural and susceptibility-weighted data. The susceptibility-weighted data were processed using STISuite toolbox to obtain QSM. The magnetic susceptibility difference between the two groups was compared using voxel-wise and region of interest methods. Multivariable linear regression, controlling for age, were employed to investigate the associations between the brain magnetic susceptibility and age at onset. Lower magnetic susceptibility was mainly observed in sensory- and motor-related brain regions in children with CECTS, including bilateral middle frontal gyrus, supplementary motor area, midcingulate cortex, paracentral lobule and precentral gyrus, the magnetic susceptibility of right paracentral lobule, right precuneus and left supplementary motor area were found to have positive correlation with the age at onset. This study suggests that the potential iron deficiency in certain brain regions is associated with CECTS, which might be helpful for further illumination of potential pathogenesis mechanism of CECTS.

Incremental value of 99m Tc-MIBI single-photon emission computed tomography/computed tomography fusion imaging for the diagnosis of secondary hyperparathyroidism.

To assess the added value of 99m Tc-MIBI single-photon emission computed tomography/computed tomography (SPECT/CT) fusion imaging over dual-phase scintigraphy in the diagnosis of secondary hyperparathyroidism (SHPT). This retrospective study included 23 patients with SHPT. The diagnostic efficacy of 99m Tc-MIBI dual-phase scintigraphy and SPECT/CT fusion imaging was analyzed and compared based on the result of postoperative pathology and follow-up. To evaluate the diagnostic ability of 99m Tc-MIBI dual-phase scintigraphy, the volume and radioactive count of parathyroid lesions were assessed using the region of interest method. A total of 79 hyperplastic parathyroid glands and two thyroid tissues were surgically removed from 23 SHPT patients and 13 normal parathyroid glands were preserved. 99m Tc-MIBI SPECT/CT fusion imaging showed higher sensitivity and accuracy than 99m Tc-MIBI dual-phase scintigraphy [sensitivity, 77.2% (61/79) vs 46.8% (37/79); accuracy, 80.4% (74/92) vs 54.3% (50/92), respectively], but comparable specificity [100% (13/13)). Among 61 positive lesions detected by 99m Tc-MIBI SPECT/CT fusion imaging, 37 were dual-phase scintigraphy positive and 24 were dual-phase scintigraphy false negative. The radioactivity counts and radioactivity per unit volume in dual-phase scintigraphy positive were higher than that in dual-phase scintigraphy false negative ( P < 0.05), but the volume of parathyroid lesions between the two groups had no significant difference ( P > 0.05). Compared with 99m Tc-MIBI dual-phase scintigraphy, 99m Tc-MIBI SPECT/CT fusion imaging has incremental value in the diagnosis of SHPT. The low uptake of MIBI in the whole gland and low MIBI uptake per unit volume are easy to cause dual-phase scintigraphy false negative.

Impact of tumour region of interest delineation method for mid-treatment FDG-PET response prediction in head and neck squamous cell carcinoma undergoing radiotherapy.

The aim of this study was to evaluate the impact of tumour region of interest (ROI) delineation method on mid-treatment 18F-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) response prediction in mucosal head and neck squamous cell carcinoma during radiotherapy. A total of 52 patients undergoing definitive radiotherapy with or without systemic therapy from two prospective imaging biomarker studies were analysed. FDG-PET was performed at baseline and during radiotherapy (week 3). Primary tumour was delineated using a fixed SUV 2.5 threshold (MTV2.5), relative threshold (MTV40%) and a gradient based segmentation method (PET Edge). PET parameters SUVmax, SUVmean, metabolic tumour volume (MTV) and total lesion glycolysis (TLG) were calculated using different ROI methods. Absolute and relative change (∆) in PET parameters were correlated to 2-year locoregional recurrence. Strength of correlation was tested using receiver operator characteristic analysis using area under the curve (AUC). Response was categorized using optimal cut-off (OC) values. Correlation and agreement between different ROI methods was determined using Bland-Altman analysis. A significant difference in SUVmean, MTV and TLG values were noted between ROI delineation methods. When measuring relative change at week 3, a greater agreement was seen between PET Edge and MTV2.5 methods with average difference in ∆SUVmax, ∆SUVmean, ∆MTV and ∆TLG of 0.0%, 3.6%, 10.3% and 13.6% respectively. A total of 12 patients (22.2%) experienced locoregional recurrence. ∆MTV using PET Edge was the best predictor of locoregional recurrence (AUC =0.761, 95% CI: 0.573-0.948, P=0.001; OC ∆>50%). The corresponding 2-year locoregional recurrence rate was 7% vs. 35%, P=0.001. Our findings suggest that it is preferable to use gradient based method to assess volumetric tumour response during radiotherapy and offers advantage in predicting treatment outcomes compared with threshold-based methods. This finding requires further validation and can assist in future response-adaptive clinical trials.

Novel superpixel method to visualize fundus blood flow resistivity in healthy adults

We aimed to perform superpixel segmentation of ocular blood flow maps obtained using laser speckle flowgraphy (LSFG) and investigate the effects of systemic parameters such as body weight, height, and sex on ocular blood flow resistivity. We studied 757 healthy participants (583 men, 174 women). We calculated the average beat strength over mean blur rate (BOM) as a LSFG resistivity index, as a function of age and sex using ordinary regions of interest (ROI) centered on the optic nerve head (ONH), the retinal vessels region and tissue around the ONH, and the choroid (CHD). We compared the ROI and superpixel-based methods, which are segmented based on image processing, for calculating the BOM. The sex differences in the BOM for the ONH, retinal-vessels region and tissue region of the ONH and CHD were significant for individuals aged ≤ 50 years (P < 0.01) but not those > 50 years old (P > 0.05). The average BOMs calculated using the ROI and superpixel methods were strongly correlated in the ONH (coefficient = 0.87, R2 = 0.8, P < 0.0001, n = 5465). In summary, a superpixel-segmented BOM map is suitable for two-dimensional visualization of ocular blood flow resistivity.

An Integrated Framework for COVID-19 Classification Based on Ensembles of Deep Features and Entropy Coded GLEO Feature Selection

COVID-19 is a challenging worldwide pandemic disease nowadays that spreads from person to person in a very fast manner. It is necessary to develop an automated technique for COVID-19 identification. This work investigates a new framework that predicts COVID-19 based on X-ray images. The suggested methodology contains core phases as preprocessing, feature extraction, selection and categorization. The Guided and 2D Gaussian filters are utilized for image improvement as a preprocessing phase. The outcome is then passed to 2D-superpixel method for region of interest (ROI). The pre-trained models such as Darknet-53 and Densenet-201 are then applied for features extraction from the segmented images. The entropy coded GLEO features selection is based on the extracted and selected features, and ensemble serially to produce a single feature vector. The single vector is finally supplied as an input to the variations of the SVM classifier for the categorization of the normal/abnormal (COVID-19) X-rays images. The presented approach is evaluated with different measures known as accuracy, recall, F1 Score, and precision. The integrated framework for the proposed system achieves the acceptable accuracies on the SVM Classifiers, which authenticate the proposed approach’s effectiveness.

Comparison of semi-quantitative and visual assessment of early MRI signal evolution in acute ischaemic stroke

BackgroundThe evaluation of DWI/FLAIR mismatch in ischaemic stroke patients with unknown, time from onset can determine the treatment strategy. This approach is based on, visual assessment and may be subject to insufficient inter-rater agreement. ObjectiveTo compare the inter-rater agreement of visual evaluation of FLAIR MRI and proposed region of interest (ROI) semiquantitative method in large vessel occlusion (LVO) strokes. MethodsFive readers have analysed MRIs of 104 patients obtained within six hours of the onset of stroke symptoms resulting from LVO visually and semi-quantitatively. For the semiquantitative analysis, a ROI method was used to obtain relative signal intensity compared to the unaffected side. Cut-off values of 1.15 and 1.10 were tested. The analysis yielded FLAIR-positive (abnormal) and negative (normal) findings. Percentage agreement and Fleiss kappa coefficients were calculated. ResultsThe visual agreement of 5/5 readers and ≥ 4/5 readers occurred in 31% and 59% of cases respectively. Semi-quantitative evaluation using a cut-off value of 1.15 increased the agreements to 67% and 88% respectively. The agreement of visual evaluation was fair. The semi-quantitative method utilising the cut-off of 1.15 had moderate agreement although it increased the number of FLAIR-negative results compared to the visual evaluation. A low cut-off value of 1.10 didn’t improve the agreement significantly. ConclusionThe inter-rater agreement of visual evaluation of FLAIR in patients with short-duration large vessel occlusion stroke was fair. The high cut-off value of semiquantitative evaluation increased the agreement although it changed the proportion of FLAIR positive and negative results.

Progress in magnetic particle imaging signal and iron quantification methods in vivo - application to long circulating SPIONs.

The strengths of Magnetic Particle Imaging (MPI) lay in its sensitivity, quantitative nature, and lack of signal attenuation for Superparamagnetic Iron Oxide Nanoparticles (SPION). These advantages make MPI a powerful tool for the non-invasive monitoring of tracer behaviour over time. With more MPI studies emerging, a standardized method for determining the boundaries of a region of interest (ROI) and iron quantification is crucial. The current approaches are inconsistent, making it challenging to compare studies, hindering MPI progression. Here we showcase three different ROI selection methods for the quantification of iron in vivo and ex vivo. Healthy mice were intravenously administered a long circulating tracer, never before applied in MPI, and the ROI methods were tested for their ability to accurately quantify the total signal present, in addition to the accumulation of the tracer in individual organs. We discuss how the quantified iron amount can be vastly altered based on the choice of ROI, the importance of the standard curve and the challenges associated with each method. Lastly, the user variability and accuracy of each method was compared by 3 independent users to ensure their consistency and lack of bias.

Abnormal cortical thickness and structural covariance networks in systemic lupus erythematosus patients without major neuropsychiatric manifestations

BackgroundNon-neuropsychiatric systemic lupus erythematosus (non-NPSLE) has been confirmed to have subtle changes in brain structure before the appearance of obvious neuropsychiatric symptoms. Previous literature mainly focuses on brain structure loss in non-NPSLE; however, the results are heterogeneous, and the impact of structural changes on the topological structure of patients’ brain networks remains to be determined. In this study, we combined neuroimaging and network analysis methods to evaluate the changes in cortical thickness and its structural covariance networks (SCNs) in patients with non-NPSLE.MethodsWe compare the cortical thickness of non-NPSLE patients (N=108) and healthy controls (HCs, N=88) using both surface-based morphometry (SBM) and regions of interest (ROI) methods, respectively. After that, we analyzed the correlation between the abnormal cortical thickness results found in the ROI method and a series of clinical features. Finally, we constructed the SCNs of two groups using the regional cortical thickness and analyzed the abnormal SCNs of non-NPSLE.ResultsBy SBM method, we found that cortical thickness of 34 clusters in the non-NPSLE group was thinner than that in the HC group. ROI method based on Destrieux atlas showed that cortical thickness of 57 regions in the non-NPSLE group was thinner than that in the HC group and related to the course of disease, autoantibodies, the cumulative amount of immunosuppressive agents, and cognitive psychological scale. In the SCN analysis, the cortical thickness SCNs of the non-NPSLE group did not follow the small-world attribute at a few densities, and the global clustering coefficient appeared to increase. The area under the curve analysis showed that there were significant differences between the two groups in clustering coefficient, degree, betweenness, and local efficiency. There are a total of seven hubs for non-NPSLE, and five hubs in HCs, the two groups do not share a common hub distribution.ConclusionExtensive and obvious reduction in cortical thickness and abnormal topological organization of SCNs are observed in non-NPSLE patients. The observed abnormalities may not only be the realization of brain damage caused by the disease, but also the contribution of the compensatory changes within the nervous system.

Automating the customer verification process in a car sharing system based on machine learning methods

Convenient and accurate verification of the user of a car sharing system is one of the key components of the successful functioning of the car sharing system as a whole. The machine learning-based KYC (Know your customer) process algorithm makes it possible to improve the accuracy of customer data validation and verification. This makes it possible to eliminate possible losses and reputational losses of the company in case of unforeseen situations while using the client's car sharing services. The object of this study is to find a solution to the problem of user verification in a car sharing system based on the KYC process using deep learning methods with a combination of OCR (Optical Character Recognition) methods. The statement of the user verification problem in the car sharing system was formalized and the key parameters for the KYC process have been determined. The algorithm of the KYC process was constructed. The algorithm includes six successive stages: separating a face in the photograph, comparing faces, checking documents and their validity period, establishing and recognizing ROI (region of interest), formulating a verification decision. To separate the face in the client's photograph and compare faces, methods based on deep learning, as well as the quick HoG method (Histogram of oriented gradients), were considered and implemented. Verification of these methods on a test dataset, which includes images of documents of two thousand clients, showed that the recognition accuracy was 91 % according to Jaccard's metric. The average time of face separation using the HoG method was 0.2 seconds and when using trained models – 3.3 seconds. Using a combination of ROI and ORC separation methods makes it possible to significantly improve the accuracy of verification. The proposed client verification algorithm is implemented as an API on an ML server and integrated into the car sharing system.

Factors influencing the time-intensity curve analysis of contrast-enhanced ultrasound in kidney transplanted patients: Toward a standardized contrast-enhanced ultrasound examination.

BackgroundTime-intensity curve analysis (TIC analysis) based on contrast-enhanced ultrasound (CEUS) provides quantifiable information about the microcirculation of different tissues. TIC analysis of kidney transplantations is still a field of research, and standardized study protocols are missing though being mandatory for the interpretation of TIC parameters in the clinical context. The aim of this study was to evaluate the impact of different sizes and forms of regions of interest (ROIs) on the variance of different TIC parameters and the level of interoperator variance between the different ROI methods in kidney transplantations.MethodsIn 25 renal transplanted patients, 33 CEUS of the transplanted kidney were performed, and TIC analysis with ROIs sized 5 mm2 (ROI5), 10 mm2 (ROI10), and ROIs circumscribing the outlines of anatomical regions (ROIAnat) were analyzed based on CEUS examination. The TIC analysis was repeated by a second independent operator for ROI5 and ROIAnat.ResultsStatistical analysis revealed significant differences between TIC parameters of different ROI methods, and overall, the interoperator variance was low. But a greater ROI surface (ROI10) led to higher values of the intensity parameters A and AUC compared with ROI5 (p < 0.05). The difference in the ROI form led to high variation of certain TIC parameters between ROI5 and ROIAnat in the myelon [intraclass correlation coefficient (A, ICC = 0.578 (0.139–0.793); TIC parameter (TTP); and ICC = 0.679 (0.344–0.842) (p < 0.05)]. A mean variation of 1 cm of the depth of ROI5 in the cortex did not show significant differences in the TIC parameters, though there was an impact of depth of ROIAnat on the values of TIC parameters. The interoperator variance in the cortex was low and equal for ROI5 and ROIAnat, but increased in the myelon, especially for ROIAnat. Furthermore, the analysis revealed a strong correlation between the parameter AUC and the time interval applied for the TIC analysis in the cortex and myelon (r = 0.710, 0.674, p < 0.000).ConclusionOur findings suggest the application of multiple ROIs of 5 mm2 in the cortex and medulla to perform TIC analysis of kidney transplants. For clinical interpretation of AUC, a standardized time interval for TIC analysis should be developed. After the standardization of the TIC analysis, the clinical predictive value could be investigated in further studies.