High-resolution dataset of manual claustrum segmentation.

The claustrum has a unique thin sheet-like structure that makes it hard to identify in typical anatomical MRI scans. Attempts have been made to identify the claustrum in anatomical images with either automatic segmentation techniques or using atlas-based approaches. However, the resulting labels fail to include the ventral claustrum portion, which consists of fragmented grey matter referred to as "puddles". The current dataset is a high-resolution label of the whole claustrum manually defined using an ultra-high resolution postmortem MRI image of one individual. Manual labelling was performed by four independent research trainees. Two trainees labelled the left claustrum and another two trainees labelled the right claustrum. For every hemisphere we created a union of the two labels and assessed the label correspondence using dice coefficients. We provide size measurements of the labels in MNI space by calculating the oriented bounding box size. These data are the first manual claustrum segmentation labels that include both the dorsal and ventral claustrum regions at such a high resolution in standard space. The label can be used to approximate the claustrum location in typical in vivo MRI scans of healthy individuals.

Increasing region of interest width reduces neonatal circumferential strain.

OBJECTIVE: There is growing interest in speckle tracking echocardiography-derived strain as a measure of left ventricular function in neonates. However, knowledge gaps remain regarding the effect of image acquisition and processing parameters on circumferential strain measurements. The aim of this study was to evaluate the effect of using different region of interest (ROI) widths on speckle tracking derived circumferential strain in healthy neonates. METHODS: Thirty healthy-term-born neonates were examined with speckle-tracking echocardiography in the short-axis view. Circumferential strain values were acquired and compared using two different ROI widths. Furthermore, strain values in the different vendor-defined wall layers were also compared. RESULTS: Increasing ROI width led to a decrease in global circumferential strain (GCS) in the midwall and epicardial layers, the respective decreases in strain being -23.4 ± .6% to -22.0 ± 1.1%, p < .0001 and 18.5 ± 1.7% to -15.6 ± 2.0%, p < .0001. Segmental analyses were consistent with these results, apart from two segments in the midwall. There was no statistically significant effect on strain for the endocardial layer. A gradient was seen where strain increased from the epicardial to endocardial layers. CONCLUSION: Increasing ROI width led to a decrease in GCS in the midwall and epicardium. There is an increase in circumferential strain when moving from the epicardial toward the endocardial layer. Clinicians wishing to implement circumferential strain into their practice should consider ROI width variation as a potential confounder in their measurements.

Optimizing Choice of Skin Surrogates for Surface-Guided Stereotactic Body Radiotherapy of Lung Lesions Using Four-Dimensional Computed Tomography.

Image-guided radiotherapy supported by surface guidance can help to track lower lung lesions' respiratory motion while reducing a patient's exposure to ionizing radiation. However, it is not always clear how the skin's respiratory motion magnitude and its correlation with the lung lesion's respiratory motion vary between different skin regions of interest (ROI). Four-dimensional computed tomography (4DCT) images provide information on both the skin and lung respiratory motion and are routinely acquired for the purpose of treatment planning in our institution. An analysis of 4DCT images for 57 patients treated in our institution has been conducted to provide information on the respiratory motion magnitudes of nine skin ROIs of the torso, a tracking structure (TS) representing a lower lung lobe lesion, as well as the respiratory motion correlations between the nine ROIs and the TS. The effects of gender and the adipose tissue volume and distribution on these correlations and magnitudes have been analyzed. Significant differences between the ROIs in both the respiratory motion magnitudes and their correlations with the TS have been detected. An overall negative correlation between the ROI respiratory magnitudes and the adipose tissue has been detected for ROIs with rib cage support. A weak to moderate negative correlation between the adipose tissue volume and ROI-to-TS respiratory correlations has been detected for upper thorax ROIs. The respiratory magnitudes in regions without rib support tend to be larger for men than for women, but no differences in the ROI-to-TS correlation between sexes have been detected. The described findings should be considered when choosing skin surrogates for lower lung lesion motion management.

Exploring the impact of excluding intrahepatic segmental vessels on liver stiffness measurement and advanced fibrosis diagnosis using magnetic resonance elastography.

BACKGROUND: The impact of excluding intrahepatic segmental vessels from regions of interest (ROIs) on liver stiffness measurement (LSM) via magnetic resonance elastography (MRE) remains uncertain. PURPOSE: To determine the effect of excluding intrahepatic segmental vessels from ROIs on LSM obtained from MRE. MATERIAL AND METHODS: This retrospective analysis included 95 participants who underwent successful two-dimensional gradient recalled-echo MRE before hepatic tumor resection (n = 49) or living liver donation (n = 46). The conventional LSM was determined by manually drawing ROIs on the elastogram within the 95% confidence region, staying 1 cm within the liver capsule and excluding large hilar vessels, the gallbladder, hepatic lesions, and artifacts. In addition, the modified LSM was determined by excluding intrahepatic segmental vessels. LSMs obtained by the two methods were compared with paired sample signed-rank test. Diagnostic performance for advanced fibrosis was calculated and compared using McNemar's test and Delong's test. The stage of hepatic fibrosis was assessed using surgical specimens by the METAVIR system. RESULTS: The modified LSM was larger than the conventional LSM (2.4 kPa vs. 2.2 kPa in reader 1; 2.7 kPa vs. 2.4 kPa in reader 2; P < 0.001). The modified LSM showed superior sensitivity (0.841 vs. 0.659 in reader 1; 0.864 vs. 0.705 in reader 2; P < 0.05) and area under the curve (0.901 vs. 0.820 in reader 1; 0.912 vs. 0.843 in reader 2; P < 0.05) for detecting advanced fibrosis (≥F3) than conventional LSM. CONCLUSION: The exclusion of intrahepatic segmental vessels from ROIs in MRE affected the LSM and enhanced diagnostic performance for advanced fibrosis.

Fine grained automatic left ventricle segmentation via ROI based Tri-Convolutional neural networks.

BACKGROUND: The left ventricle segmentation (LVS) is crucial to the assessment of cardiac function. Globally, cardiovascular disease accounts for the majority of deaths, posing a significant health threat. In recent years, LVS has gained important attention due to its ability to measure vital parameters such as myocardial mass, end-diastolic volume, and ejection fraction. Medical professionals realize that manually segmenting data to evaluate these processes takes a lot of time, effort when diagnosing heart diseases. Yet, manually segmenting these images is labour-intensive and may reduce diagnostic accuracy. OBJECTIVE/METHODS: This paper, propose a combination of different deep neural networks for semantic segmentation of the left ventricle based on Tri-Convolutional Networks (Tri-ConvNets) to obtain highly accurate segmentation. CMRI images are initially pre-processed to remove noise artefacts and enhance image quality, then ROI-based extraction is done in three stages to accurately identify the LV. The extracted features are given as input to three different deep learning structures for segmenting the LV in an efficient way. The contour edges are processed in the standard ConvNet, the contour points are processed using Fully ConvNet and finally the noise free images are converted into patches to perform pixel-wise operations in ConvNets. RESULTS/CONCLUSIONS: The proposed Tri-ConvNets model achieves the Jaccard indices of 0.9491 ± 0.0188 for the sunny brook dataset and 0.9497 ± 0.0237 for the York dataset, and the dice index of 0.9419 ± 0.0178 for the ACDC dataset and 0.9414 ± 0.0247 for LVSC dataset respectively. The experimental results also reveal that the proposed Tri-ConvNets model is faster and requires minimal resources compared to state-of-the-art models.

3D DenseNet with temporal transition layer for heart rate estimation from real-life RGB videos.

BACKGROUND: Deep learning has demonstrated superior performance over traditional methods for the estimation of heart rates in controlled contexts. However, in less controlled scenarios this performance seems to vary based on the training dataset and the architecture of the deep learning models. OBJECTIVES: In this paper, we develop a deep learning-based model leveraging the power of 3D convolutional neural networks (3DCNN) to extract temporal and spatial features that lead to an accurate heart rates estimation from RGB no pre-defined region of interest (ROI) videos. METHODS: We propose a 3D DenseNet with a 3D temporal transition layer for the estimation of heart rates from a large-scale dataset of videos that appear more hospital-like and real-life than other existing facial video-based datasets. RESULTS: Experimentally, our model was trained and tested on this less controlled dataset and showed heart rate estimation performance with root mean square error (RMSE) of 8.68 BPM and mean absolute error (MAE) of 3.34 BPM. CONCLUSION: Moreover, we show that such a model can also achieve better results than the state-of-the-art models when tested on the VIPL-HR public dataset.

Prediction of histologic grade of hepatocellular carcinoma using dual-layer spectral-detector computed tomography (CT): comparison of two region of interest plotting methods.

Background: Multi-parameter imaging technology, which is based on substance separation, helps to predict the pathological grade of tumors. When using dual-layer spectral-detector computed tomography (DLCT) to quantify tumor properties, different methods of placing regions of interest (ROIs) directly impact the measurement of parameters, thus affecting the clinical diagnosis of lesions. Consequently, in this study, we aimed to compare the performance of 2 different ROI plotting methods on DLCT in differentiating the histologic grade of hepatocellular carcinoma (HCC). Methods: This retrospective study included 48 consecutive patients with pathologically confirmed HCC, who underwent DLCT from May 2022 to March 2023. The attenuation value of conventional computed tomography (CT), electron density relative to water (EDW), normalized effective atomic number (NZeff), and normalized iodine density (NID) were measured by 2 radiologists using the conventional planar sketching (PS) method and the volumetric analysis method, respectively. The differences in parameters between the arterial phase (AP) and venous phase (VP) were calculated for each parameter (∆CT, ∆EDW, ∆NZeff, ∆NID). We used 2-sample t-test or Mann-Whitney U test was used to compare the differences in parameters between the 2 methods. Spearman correlation analysis was used to determine the correlation between each parameter and histologic grade. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance. Results: The mean values for the spectral quantitative parameters (CTAP, NZeffAP, NIDAP) and the difference between the arterial phase and venous phase (AP-VP) of parameters (∆CT, ∆EDW, ∆NZeff) measured using the volumetric analysis method were significantly lower than those of the PS method (P<0.05). For the ∆NZeff, the volumetric analysis method achieved the highest area under the curve (AUC) with a value of 0.918 [95% confidence interval (CI): 0.847-0.988], followed by the PS method (AUC =0.853, 95% CI: 0.743-0.963). Conclusions: The spectral parameters of DLCT provide a novel quantitative method for evaluating histological differentiation in patients with HCC, which is worthy of clinical recommendation. Different ROI plotting methods significantly impact the measurement of spectral parameters. Therefore, the whole tumor region should be covered in the parameter measurement of HCC lesions as much as feasible, which is more helpful in predicting the histological grading of tumors before treatment.

Approaches to Measuring Language Lateralisation: An Exploratory Study Comparing Two fMRI Methods and Functional Transcranial Doppler Ultrasound.

In this exploratory study we compare and contrast two methods for deriving a laterality index (LI) from functional magnetic resonance imaging (fMRI) data: the weighted bootstrapped mean from the LI Toolbox (toolbox method), and a novel method that uses subtraction of activations from homologous regions in left and right hemispheres to give an array of difference scores (mirror method). Data came from 31 individuals who had been selected to include a high proportion of people with atypical laterality when tested with functional transcranial Doppler ultrasound (fTCD). On two tasks, word generation and semantic matching, the mirror method generally gave better agreement with fTCD laterality than the toolbox method, both for individual regions of interest, and for a large region corresponding to the middle cerebral artery. LI estimates from this method had much smaller confidence intervals (CIs) than those from the toolbox method; with the mirror method, most participants were reliably lateralised to left or right, whereas with the toolbox method, a higher proportion were categorised as bilateral (i.e., the CI for the LI spanned zero). Reasons for discrepancies between fMRI methods are discussed: one issue is that the toolbox method averages the LI across a wide range of thresholds. Furthermore, examination of task-related t-statistic maps from the two hemispheres showed that language lateralisation is evident in regions characterised by deactivation, and so key information may be lost by ignoring voxel activations below zero, as is done with conventional estimates of the LI.

Abnormal regional homogeneity as a potential imaging indicator for identifying adolescent-onset schizophrenia: Insights from resting-state functional magnetic resonance imaging.

BACKGROUND: In patients with schizophrenia, there is abnormal regional functional synchrony. However, whether it also in patients with adolescent-onset schizophrenia (AOS) remains unclear. The goal of this study was to analyze the regional homogeneity (ReHo) of resting functional magnetic resonance imaging to explore the functional abnormalities of the brain in patients with AOS. METHODS: The study included 107 drug-naive first-episode AOS patients and 67 healthy, age, sex, and education-matched controls using resting-state functional magnetic resonance imaging scans. The ReHo method was used to analyze the imaging dataset. RESULTS: Compared with the control group, the ReHo values of the right inferior frontal gyrus orbital part, right middle frontal gyrus (MFG.R), left inferior parietal, but supramarginal and angular gyri, and left precentral gyrus (PreCG.L) were significantly increased and the ReHo value of the left posterior cingulate cortex/anterior cuneiform lobe was significantly decreased in schizophrenia patients. ROC analysis showed that the ReHo values of the MFG.R and PreCG.L might be regarded as potential markers in helping to identify patients. Furthermore, the PANSS scores in the patient group and the ReHo values showed a positive correlation between MFG.R ReHo values and general scores. CONCLUSIONS: Our results suggested that AOS patients had ReHo abnormalities. The ReHo values of these abnormal regions may serve as potential imaging biomarkers for the identification of AOS patients.

Optimizing diagnosis and surgical decisions for chronic osteomyelitis through radiomics in the precision medicine era.

Introduction: Chronic osteomyelitis is a complex clinical condition that is associated with a high recurrence rate. Traditional surgical interventions often face challenges in achieving a balance between thorough debridement and managing resultant bone defects. Radiomics is an emerging technique that extracts quantitative features from medical images to reveal pathological information imperceptible to the naked eye. This study aims to investigate the potential of radiomics in optimizing osteomyelitis diagnosis and surgical treatment. Methods: Magnetic resonance imaging (MRI) scans of 93 suspected osteomyelitis patients were analyzed. Radiomics features were extracted from the original lesion region of interest (ROI) and an expanded ROI delineated by enlarging the original by 5 mm. Feature selection was performed and support vector machine (SVM) models were developed using the two ROI datasets. To assess the diagnostic efficacy of the established models, we conducted receiver operating characteristic (ROC) curve analysis, employing histopathological results as the reference standard. The model's performance was evaluated by calculating the area under the curve (AUC), sensitivity, specificity, and accuracy. Discrepancies in the ROC between the two models were evaluated using the DeLong method. All statistical analyses were carried out using Python, and a significance threshold of p < 0.05 was employed to determine statistical significance. Results and Discussion: A total of 1,037 radiomics features were extracted from each ROI. The expanded ROI model achieved significantly higher accuracy (0.894 vs. 0.821), sensitivity (0.947 vs. 0.857), specificity (0.842 vs. 0.785) and AUC (0.920 vs. 0.859) than the original ROI model. Key discriminative features included shape metrics and wavelet-filtered texture features. Radiomics analysis of MRI exhibits promising clinical translational potential in enhancing the diagnosis of chronic osteomyelitis by accurately delineating lesions and identifying surgical margins. The inclusion of an expanded ROI that encompasses perilesional tissue significantly improves diagnostic performance compared to solely focusing on the lesions. This study provides clinicians with a more precise and effective tool for diagnosis and surgical decision-making, ultimately leading to improved outcomes in this patient population.

Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images - Nevus & Melanoma.

Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep-learning methods used in computational pathology may help us to reduce costs and increase the speed and accuracy of cancer diagnosis. We started with the UNC Melanocytic Tumor Dataset cohort that contains 160 hematoxylin and eosin whole-slide images of primary melanomas (86) and nevi (74). We randomly assigned 80% (134) as a training set and built an in-house deep-learning method to allow for classification, at the slide level, of nevi and melanomas. The proposed method performed well on the other 20% (26) test dataset; the accuracy of the slide classification task was 92.3% and our model also performed well in terms of predicting the region of interest annotated by the pathologists, showing excellent performance of our model on melanocytic skin tumors. Even though we tested the experiments on the skin tumor dataset, our work could also be extended to other medical image detection problems to benefit the clinical evaluation and diagnosis of different tumors.

Structural neuroimaging changes associated with subjective cognitive decline from a clinical sample.

BACKGROUND: Alzheimer's disease (AD) is characterized by progressive deterioration of cognitive functions. Some individuals with subjective cognitive decline (SCD) are in the early phase of the disease and subsequently progress through the AD continuum. Although neuroimaging biomarkers could be used for the accurate and early diagnosis of preclinical AD, the findings in SCD samples have been heterogeneous. This study established the morphological differences in brain magnetic resonance imaging (MRI) findings between individuals with SCD and those without cognitive impairment based on a clinical sample of patients defined according to SCD-Initiative recommendations. Moreover, we investigated baseline structural changes in the brains of participants who remained stable or progressed to mild cognitive impairment or dementia. METHODS: This study included 309 participants with SCD and 43 healthy controls (HCs) with high-quality brain MRI at baseline. Among the 99 subjects in the SCD group who were followed clinically, 32 progressed (SCDp) and 67 remained stable (SCDnp). A voxel-wise statistical comparison of gray and white matter (WM) volume was performed between the HC and SCD groups and between the HC, SCDp, and SCDnp groups. XTRACT ATLAS was used to define the anatomical location of WM tract damage. Region-of-interest (ROI) analyses were performed to determine brain volumetric differences. White matter lesion (WML) burden was established in each group. RESULTS: Voxel-based morphometry (VBM) analysis revealed that the SCD group exhibited gray matter atrophy in the middle frontal gyri, superior orbital gyri, superior frontal gyri, right rectal gyrus, whole occipital lobule, and both thalami and precunei. Meanwhile, ROI analysis revealed decreased volume in the left rectal gyrus, bilateral medial orbital gyri, middle frontal gyri, superior frontal gyri, calcarine fissure, and left thalamus. The SCDp group exhibited greater hippocampal atrophy (p < 0.001) than the SCDnp and HC groups on ROI analyses. On VBM analysis, however, the SCDp group exhibited increased hippocampal atrophy only when compared to the SCDnp group (p < 0.001). The SCD group demonstrated lower WM volume in the uncinate fasciculus, cingulum, inferior fronto-occipital fasciculus, anterior thalamic radiation, and callosum forceps than the HC group. However, no significant differences in WML number (p = 0.345) or volume (p = 0.156) were observed between the SCD and HC groups. CONCLUSIONS: The SCD group showed brain atrophy mainly in the frontal and occipital lobes. However, only the SCDp group demonstrated atrophy in the medial temporal lobe at baseline. Structural damage in the brain regions was anatomically connected, which may contribute to early memory decline.

Classification of presence of missing teeth in each quadrant using deep learning artificial intelligence on panoramic radiographs of pediatric patients.

Early tooth loss in pediatric patients can lead to various complications, making quick and accurate diagnosis essential. This study aimed to develop a novel deep learning model for classification of missing teeth on panoramic radiographs in pediatric patients and to assess the accuracy. The study included patients aged 8-16 years who visited the Pusan National University Dental Hospital and underwent panoramic radiography. A total of 806 panoramic radiographs were retrospectively analyzed to determine the presence or absence of missing teeth for each tooth number. Moreover, each panoramic radiograph was divided into four quadrants, each of a smaller size, containing both primary and permanent teeth, generating 3224 data. Quadrants with missing teeth (n = 1457) were set as the experimental group, and quadrants without missing teeth (n = 1767) were set as the control group. The data were split into training and validation sets in a 4:1 ratio, and a 5-fold cross-validation was conducted. A gradient-weighted class activation map was used to visualize the deep learning model. The average values of sensitivity, specificity, accuracy, precision, recall and F1-score of this deep learning model were 0.635, 0.814, 0.738, 0.730, 0.732 and 0.731, respectively. In the experimental group, the accuracy was the highest for missing canines and premolars, and the lowest for molars. The deep learning model exhibited a moderate to good distinguishing power with a classification performance of 0.730. This deep learning model and the newly defined small sized region of interest proved adequate for classifying the presence of missing teeth.

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

BACKGROUND: Discriminating the stage of Graves' ophthalmopathy (GO) is crucial for clinical decision. Application of conventional T2-weighted imaging in the staging is still limited. PURPOSE: To evaluate the performance of T2 mapping based on two different regions of interest (ROIs) for staging GO. MATERIAL AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Microstructural changes of the white matter in systemic lupus erythematosus patients without neuropsychiatric symptoms: a multi-shell diffusion imaging study.

BACKGROUND: Diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) provide more comprehensive and informative perspective on microstructural alterations of cerebral white matter (WM) than single-shell diffusion tensor imaging (DTI), especially in the detection of crossing fiber. However, studies on systemic lupus erythematosus patients without neuropsychiatric symptoms (non-NPSLE patients) using multi-shell diffusion imaging remain scarce. METHODS: Totally 49 non-NPSLE patients and 41 age-, sex-, and education-matched healthy controls underwent multi-shell diffusion magnetic resonance imaging. Totally 10 diffusion metrics based on DKI (fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity, mean kurtosis, axial kurtosis and radial kurtosis) and NODDI (neurite density index, orientation dispersion index and volume fraction of the isotropic diffusion compartment) were evaluated. Tract-based spatial statistics (TBSS) and atlas-based region-of-interest (ROI) analyses were performed to determine group differences in brain WM microstructure. The associations of multi-shell diffusion metrics with clinical indicators were determined for further investigation. RESULTS: TBSS analysis revealed reduced FA, AD and RK and increased ODI in the WM of non-NPSLE patients (P < 0.05, family-wise error corrected), and ODI showed the best discriminative ability. Atlas-based ROI analysis found increased ODI values in anterior thalamic radiation (ATR), inferior frontal-occipital fasciculus (IFOF), forceps major (F_major), forceps minor (F_minor) and uncinate fasciculus (UF) in non-NPSLE patients, and the right ATR showed the best discriminative ability. ODI in the F_major was positively correlated to C3. CONCLUSION: This study suggested that DKI and NODDI metrics can complementarily detect WM abnormalities in non-NPSLE patients and revealed ODI as a more sensitive and specific biomarker than DKI, guiding further understanding of the pathophysiological mechanism of normal-appearing WM injury in SLE.

Region of interest selection in heterogeneous digital image: Wine age prediction by comprehensive two-dimensional gas chromatography.

This study integrates genetic algorithm (GA) with partial least squares regression (PLSR) and various variable selection methods to identify impactful regions of interest (ROI) in heterogeneous 2D chromatogram images for predicting wine age. As wine quality and aroma evolve over time, transitioning from youthful fruitiness to mature, complex flavors, which leads to alterations in the composition of essential aroma-contributing compounds. Chromatograms are segmented into subimages, and the GA-PLSR algorithm optimizes combinations based on grayscale, red-green-blue (RGB), and hue-saturation-value (HSV) histograms. The selected subimage histograms are further refined through interval selection, highlighting the compounds with the most significant influence on wine aging. Experimental validation involving 38 wine samples demonstrates the effectiveness of this approach. Cross-validation reduces the PLS model error from 2.8 to 2.4 years within a 10 × 10 subset, and during prediction, the error decreases from 2.5 to 2.3 years. The study presents a novel approach utilizing the selection of ROI for efficient processing of 2D chromatograms focusing on predicting wine age.

Growth-Associated Protein 43 and Tensor-Based Morphometry Indices in Mild Cognitive Impairment.

Growth-associated protein 43 (GAP-43) is found in the axonal terminal of neurons in the limbic system, which is affected in people with Alzheimer's disease (AD). We assumed GAP-43 may contribute to AD progression and serve as a biomarker. So, in a two-year follow-up study, we assessed GAP-43 changes and whether they are correlated with tensor-based morphometry (TBM) findings in patients with mild cognitive impairment (MCI). We included MCI and cognitively normal (CN) people with available baseline and follow-up cerebrospinal fluid (CSF) GAP-43 and TBM findings from the ADNI database. We assessed the difference between the two groups and correlations in each group at each time point. CSF GAP-43 and TBM measures were similar in the two study groups in all time points, except for the accelerated anatomical region of interest (ROI) of CN subjects that were significantly greater than those of MCI. The only significant correlations with GAP-43 observed were those inverse correlations with accelerated and non-accelerated anatomical ROI in MCI subjects at baseline. Plus, all TBM metrics decreased significantly in all study groups during the follow-up in contrast to CSF GAP-43 levels. Our study revealed significant associations between CSF GAP-43 levels and TBM indices among people of the AD spectrum.

Automated Region of Interest-Based Data Augmentation for Fallen Person Detection in Off-Road Autonomous Agricultural Vehicles.

Due to the global population increase and the recovery of agricultural demand after the COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural vehicles is growing. Fallen person detection is critical to preventing fatal accidents during autonomous agricultural vehicle operations. However, there is a challenge due to the relatively limited dataset for fallen persons in off-road environments compared to on-road pedestrian datasets. To enhance the generalization performance of fallen person detection off-road using object detection technology, data augmentation is necessary. This paper proposes a data augmentation technique called Automated Region of Interest Copy-Paste (ARCP) to address the issue of data scarcity. The technique involves copying real fallen person objects obtained from public source datasets and then pasting the objects onto a background off-road dataset. Segmentation annotations for these objects are generated using YOLOv8x-seg and Grounded-Segment-Anything, respectively. The proposed algorithm is then applied to automatically produce augmented data based on the generated segmentation annotations. The technique encompasses segmentation annotation generation, Intersection over Union-based segment setting, and Region of Interest configuration. When the ARCP technique is applied, significant improvements in detection accuracy are observed for two state-of-the-art object detectors: anchor-based YOLOv7x and anchor-free YOLOv8x, showing an increase of 17.8% (from 77.8% to 95.6%) and 12.4% (from 83.8% to 96.2%), respectively. This suggests high applicability for addressing the challenges of limited datasets in off-road environments and is expected to have a significant impact on the advancement of object detection technology in the agricultural industry.

Application of bolus tracking: The effect of ROI positions on the images quality of cervicocerebral CT angiography.

Background: Cervicocerebral CT angiography (CTA) using the bolus tracking technique has been widely used for the assessment of cerebrovascular diseases. Regions of interest (ROI) can be placed in the descending aorta, ascending aorta, and the aortic arch. However, no study has compared the arteries and veins display when when the region of interest (ROI) is placed at different sites. In this study, we showed the impact of ROI positions on the image quality of cervicocerebral CTA. Methods: Two hundred and seventy patients who underwent cervicocerebral CTA with bolus tracking technique were randomly divided into three groups based on the position of the ROI placement: ascending aorta (Group 1, n = 90), aortic arch (Group 2, n = 90), and descending aorta (Group 3, n = 90). The scanning parameters and contrast agent injection protocols were consistent across all groups. Three observers independently assessed the objective image quality, while two observers jointly assessed the subjective image quality using a grade scale: poor (grade 1), average (grade 2), good (grade 3), and excellent (grade 4). The differences in intravascular CT values, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), AVCR (arterial venous contrast ratio), and subjective image quality scores were compared among the three groups. Results: The CT values of the intracranial veins (superior sagittal sinus, ethmoid sinus and great cerebral vein) in group 1 were significantly lower than those in group 3 (p < 0.001). However, no significant differences were observed in CT values, SNR and CNR in the internal carotid artery and middle cerebral artery among the three groups. The proportion of images with grade 4 was significantly higher in group 1 than group 2 and 3 (41.1% vs 15.6% and 13.3%, p < 0.001). The proportion of images with grade 1 was significantly lower in group 1 than group 2 and 3 (1.1% vs 6.6% and 17.8%, p < 0.001). Conclusion: The ROI positions for cervicocerebral CTA did not affect the arterial image quality, but venous structures imaging was affected when the ROI was placed in the ascending aorta.

A novel approach to evaluation of lumbar bone density using Hounsfield units in volume of interest on computed tomography imaging.

OBJECTIVE: The purpose of this retrospective study was to evaluate the relationship between bone mineral density (BMD), as assessed with dual-energy x-ray absorptiometry (DEXA), and Hounsfield units (HU) measured in volumes of interest (VOIs) and regions of interest (ROIs) on lumbar spine CT. METHODS: A retrospective analysis was performed on data of lumbar vertebrae obtained from patients who underwent both DEXA and lumbar spine CT scan within a 6-month period. Vertebrae with a history of compression fracture, infectious spondylitis, cement reinforcement, or lumbar surgery were excluded. HU measurements were performed in the VOI and ROI (midaxial, midcoronal, and midsagittal sections) with CT, whereas BMD was assessed with DEXA. Statistical analyses, including correlation assessments and receiver operating characteristic (ROC) curve analyses, were performed. RESULTS: This analysis included 712 lumbar vertebrae, with a median patient age of 72.0 years. BMD values and HU measurements in the VOI increased sequentially from L1 to L4, whereas HU values in the ROI did not show a consistent pattern. HU values in the VOI consistently showed a stronger correlation with BMD than those in the ROI. ROC analysis revealed patient-level cutoff values for the diagnosis of osteoporosis at different lumbar vertebral levels with high sensitivity and specificity, as well as an excellent area under the curve. CONCLUSIONS: This is the first study to introduce a novel approach using the HU value in the VOI to assess bone health at the lumbar spine. There is a strong correlation between the HU value in the VOI and BMD, and the HU value in the VOI can be used to predict osteoporosis.