Region Of Interest Definition Research Articles

Bleb vessel density as a predictive factor for surgical revisions after Preserflo Microshunt implantation.

Bleb failure is a common complication after glaucoma filtration surgery. Different bleb classification schemes incorporating filtration bleb vascularization have been proposed, but the reported correlation with intraocular pressure (IOP) has been variable, possibly because of subjective vascularization grading. The purpose of the present study was to evaluate bleb vascularization after Preserflo Microshunt (PM) implantation using anterior segment OCT-angiography (AS-OCTA) as a biomarker for bleb failure. Twenty-three eyes of twenty-three patients underwent PM implantation. Up to 12 months after surgery PM scleral passage-centred AS-OCTA measurements (PLEX Elite 9000) for bleb-vessel density (BVD) determination were performed and IOP as well as necessity for surgical revisions (needling and open revision) were documented. After multi-step image analysis (region of interest definition, artefact removal, binarization, BVD calculation), the predictive value of early postoperative BVD for surgical revisions was assessed using logistic regression modelling. Baseline IOP (23.57 ± 7.75 mmHg) decreased significantly to 8.30 ± 2.12, 9.17 ± 2.33 and 11.70 ± 4.40 mmHg after 1, 2 and 4 week(s), and 13.48 ± 5.83, 11.87 ± 4.49, 12.30 ± 6.65, 11.87 ± 3.11 and 13.05 ± 4.12 mmHg after 2, 3, 6, 9 and 12 month(s), respectively (p < 0.001). Nine patients (39%) needed surgical revisions after a median time of 2 months. Bleb vessel densities at 2 and 4 weeks were significantly associated with future surgical revisions upon logistic regression analysis (2 W/4 W likelihood-ratio test p-value: 0.0244/0.0098; 2 W/4 W area under the receiver operating characteristics curve: 0.796/0.909). Filtration bleb vessel density can be determined using AS-OCTA in the early postoperative period and is predictive for bleb failure after PM implantation.

Dose-response Analysis in Hepatic Tumors Treated with 90Y-TARE According to a Personalized Dosimetric Workflow: Preliminary Results.

Transarterial Radioembolization (TARE) is a widespread radiation therapy for unresectable hepatic lesions, but a clear understanding of the dose-response link is still missing. The aim of this preliminary study is to investigate the role of both dosimetric and clinical parameters as classifiers or predictors of response and survival for TARE in hepatic tumors and to present possible response cut-off. 20 patients treated with glass or resin microspheres according to a personalized workflow were included. Dosimetric parameters were extracted from personalized absorbed dose maps obtained from the convolution of 90Y PET images with 90Y voxel S-values. D95 ≥ 104 Gy and tumor mean absorbed dose MADt ≥ 229 Gy were found to be optimal cut-off values for complete response, while D30 ≥ 180 Gy and MADt ≥ 117 Gy were selected as cut-off values for at least partial response and predicted better survival. Clinical parameters Alanine Transaminase (ALT) and Model for End-Stage Liver Disease (MELD) didn't show sufficient classification capability for response or survival. These preliminary results highlight the importance of an accurate dosimetric evaluation and suggest a cautious approach when considering clinical indicators. Dosimetric cut-off values could be a support tool in both planning and post-treatment phases. Larger multi-centric randomized trials, with standardized methods regarding patient selection, response criteria, Regions of Interest definition, dosimetric approach and activity planning are needed to confirm these promising results.

Initial experiences with Direct Imaging of Neuronal Activity (DIANA) in humans

Abstract Functional MRI (fMRI) has been widely used to study activity patterns in the human brain. It infers neuronal activity from the associated hemodynamic response, which fundamentally limits its spatiotemporal specificity. In mice, the Direct Imaging of Neuronal Activity (DIANA) method revealed MRI signals that correlated with extracellular electric activity, showing high spatiotemporal specificity. In this work, we attempted DIANA in humans. Five experimental paradigms were tested, exploring different stimulus types (flickering noise patterns, and naturalistic images), stimulus durations (50–200 ms), and imaging resolution (2 × 2 × 5 mm3 and 1 × 1 × 5 mm3). Regions of interest (ROI) were derived from Blood Oxygen Level Dependent (BOLD) fMRI acquisitions (both EPI and FLASH based) and T1-weighted anatomical scans. In Paradigm I (n = 1), using flickering noise patterns, signals were detected that resembled possible functional activity from a small ROI. However, changes in stimulus duration did not lead to corresponding signal changes (Paradigm II; n = 1). Therefore, care should be taken not to mistake artifacts for neuronal activity. In Paradigm III (n = 3), when averaged across multiple subjects, a ~200 ms long 0.02% signal increase was observed ~100 ms after the stimulus onset (10x smaller than the expected signal). However, white matter control ROIs showed similarly large signal fluctuations. In Paradigm IV (n = 3), naturalistic image stimuli were used, but did not reveal signs of a potential functional signal. To reduce partial voluming effects and improve ROI definition, in Paradigm V (n = 3), we acquired data with higher resolution (1 × 1 × 5 mm3) using naturalistic images. However, no sign of activation was found. It is important to note that repetitive experiments with short interstimulus intervals were found to be strenuous for the subjects, which likely impacted data quality. To obtain better data, improvements in sequence and stimulus designs are needed to maximize the DIANA signal and minimize confounds. However, without a clear understanding of DIANA’s biophysical underpinnings it is difficult to do so. Therefore, it may be more effective to first investigate DIANA signals with simultaneously recorded electrophysiological signals in more controlled settings, e.g., in anesthetized mice.

Delineating regions of interest for mass spectrometry imaging by multimodally corroborated spatial segmentation.

Mass spectrometry imaging (MSI), which localizes molecules in a tag-free, spatially resolved manner, is a powerful tool for the understanding of underlying biochemical mechanisms of biological phenomena. When analyzing MSI data, it is essential to delineate regions of interest (ROIs) that correspond to tissue areas of different anatomical or pathological labels. Spatial segmentation, obtained by clustering MSI pixels according to their mass spectral similarities, is a popular approach to automate ROI definition. However, how to select the number of clusters (#Clusters), which determines the granularity of segmentation, remains to be resolved, and an inappropriate #Clusters may lead to ROIs not biologically real. Here we report a multimodal fusion strategy to enable an objective and trustworthy selection of #Clusters by utilizing additional information from corresponding histology images. A deep learning-based algorithm is proposed to extract "histomorphological feature spectra" across an entire hematoxylin and eosin image. Clustering is then similarly performed to produce histology segmentation. Since ROIs originating from instrumental noise or artifacts would not be reproduced cross-modally, the consistency between histology and MSI segmentation becomes an effective measure of the biological validity of the results. So, #Clusters that maximize the consistency is deemed as most probable. We validated our strategy on mouse kidney and renal tumor specimens by producing multimodally corroborated ROIs that agreed excellently with ground truths. Downstream analysis based on the said ROIs revealed lipid molecules highly specific to tissue anatomy or pathology. Our work will greatly facilitate MSI-mediated spatial lipidomics, metabolomics, and proteomics research by providing intelligent software to automatically and reliably generate ROIs.

The Diagnostic Efficiency of Quantitative Diffusion Weighted Imaging in Differentiating Medulloblastoma from Posterior Fossa Tumors: A Systematic Review and Meta-Analysis.

Medulloblastoma (MB) is considered the most common and highly malignant posterior fossa tumor (PFT) in children. The accurate preoperative diagnosis of MB is beneficial in choosing the appropriate surgical methods and treatment strategies. Diffusion-weighted imaging (DWI) has improved the accuracy of differential diagnosis of posterior fossa tumors. Nonetheless, further studies are needed to confirm its value for clinical application. This study aimed to evaluate the performance of DWI in differentiating MB from other PFT. A literature search was conducted using databases PubMed, Embase, and Web of Science for studies reporting the diagnostic performance of DWI for PFT from January 2000 to January 2022. A bivariate random-effects model was employed to evaluate the pooled sensitivities and specificities. A univariable meta-regression analysis was used to assess relevant factors for heterogeneity, and subgroup analyses were performed. A total of 15 studies with 823 patients were eligible for data extraction. Overall pooled sensitivity and specificity of DWI were 0.94 (95% confident interval [CI]: 0.89-0.97) and 0.94 (95% CI: 0.90-0.96) respectively. The area under the curve (AUC) of DWI was 0.98 (95% CI: 0.96-0.99). Heterogeneity was found in the sensitivity (I2 = 62.59%) and the specificity (I2 = 35.94%). Magnetic field intensity, region of interest definition and DWI diagnostic parameters are the factors that affect the diagnostic performance of DWI. DWI has excellent diagnostic accuracy for differentiating MB from other PFT. Hence, it is necessary to set DWI as a routine examination sequence for posterior fossa tumors.

Evidence-Based Definition of Region of Interest (ROI) for Abdominal DIBH Surface-Guided Radiotherapy

<h3>Purpose/Objective(s)</h3> To define and evaluate the appropriate abdominal region of interest (ROI) as a surrogate of both body and diaphragm positioning in deep-inspiration breath-hold (DIBH) for surface-guided radiotherapy (SGRT) using 3D optical surface imaging (OSI). <h3>Materials/Methods</h3> Six simulated abdominal ROIs were evaluated to calculate the correlations with the diaphragm position using 4DCT images of 20 abdominal patients. Twelve points of interest (POIs) were defined (6 on the central soft tissue and 6 on the bilateral ribs) at 3 superior-inferior levels between the xiphoid and umbilicus, and different sub-groups represented different ROIs. ROI-1 was the largest, containing all 12 POIs while ROI-2 had only 8 inferior POIs, ROI-3 had 6 lateral POIs, and ROI-4 had 4 superior-lateral POIs over the ribs, ROI-5 contained 6 central and 2 most inferior-lateral POIs, and ROI-6 contained 6 central and 4 inferior-lateral POIs. The lateral body midlines defined the ROI coverages to represent body position. Internally, the right diaphragm dome positions were measured in 4DCT (0% and 50% phases). The Pearson correlation coefficients were calculated between the diaphragm and all 12 external POIs individually or grouped as 6 ROIs. The quality of the abdominal ROIs was evaluated as potential internal surrogates and, therefore, potential ROIs for SGRT DIBH setup. <h3>Results</h3> The four most inferior POIs show the highest mean correlation (r=0.75) with diaphragmatic motion, and the correlation decreases as POIs move superiorly. The mean correlations are higher at r=0.67 for ROI-2, r=0.64 for ROI-5, and r=0.63 for ROI-6, while ROI-1 has r=0.60, ROI-3 has r=0.50, and ROI-4 has only r=0.28. <h3>Conclusion</h3> This study demonstrates that the rectangular/triangular soft-tissue ROI is the optimal surrogate for body positioning and diaphragmatic motion, even when treating tumors under the rib cage. This evidence-based ROI definition should be utilized when treating abdominal cancers with FB and/or DIBH setup.

Definition of the Region of Interest for the Assessment of Alveolar Bone Repair Using Micro-computed Tomography.

The objective of this study was to evaluate the influence of the extraction socket (distal or lingual root) and the type of region of interest (ROI) definition (manual or predefined) on the assessment of alveolar repair following tooth extraction using micro-computed tomography (micro-CT). The software package used for scanning, reconstruction, reorientation, and analysis of images (NRecon®, DataViewer®, CT-Analyzer®) was acquired through Bruker < https://www.bruker.com > . The sample comprised the micro-CT volumes of seven Wistar rat mandibles, in which the right first molar was extracted. The reconstructed images were analyzed using the extraction sockets, i.e., the distal and intermediate lingual root and the method of ROI definition: manual (MA), central round (CR), and peripheral round (PR). The bone volume fraction (BV/TV) values obtained were analyzed by two-way ANOVA with Tukey's post hoc test (α = 5%). The distal extraction socket resulted in significantly lower BV/TV values than the intermediate lingual socket for MA (P = 0.001), CR (P < 0.001), and PR (P < 0.001). Regarding the ROI, when evaluating the distal extraction socket, the BV/TV was significantly higher (P < 0.001) for MA than for CR and PR, with a lower BV/TV for CR. However, no significant difference was observed for MA (P = 0.855), CR (P = 0.769), or PR (P = 0.453) in the intermediate lingual extraction socket. The bone neoformation outcome (BV/TV) for alveolar bone repair after tooth extraction is significantly influenced by the ROI and the extraction socket. Using the predefined method with a standardized ROI in the central region of the distal extraction socket resulted in the assessment of bone volume, demonstrating the most critical region of the bone neoformation process.

Evidence-based region of interest (ROI) definition for surface-guided radiotherapy (SGRT) of abdominal cancers using deep-inspiration breath-hold (DIBH).

To define and evaluate the appropriate abdominal region of interest (ROI) as a surrogate of diaphragm positioning in deep-inspiration breath-hold (DIBH) for surface-guided radiotherapy (SGRT) of abdominal cancers using 3D optical surface imaging (OSI). Six potential abdominal ROIs were evaluated to calculate their correlations with the diaphragm position using 4DCT images of 20 abdominal patients. Twelve points of interest (POIs) were defined (six on the central soft tissue and six on the bilateral ribs) at three superior-inferior levels, and different sub-groups represented different ROIs. ROI-1 was the largest, containing all 12 POIs from the xiphoid to the umbilicus and between the lateral body midlines while ROI-2 had only eight inferior POIs, ROI-3 had six lateral POIs, and ROI-4 had four superior-lateral POIs over the ribs, ROI-5 contained six central and two most inferior-lateral POIs and ROI-6 contained six central and four inferior-lateral POIs. Internally, the right diaphragm dome was used to represent its positions in 4DCT (0% and 50% within the cycle). The Pearson correlation coefficients were calculated between the diaphragm dome and all 12 external POIs individually or grouped as six ROIs. The quality of the abdominal ROIs was evaluated as potential internal surrogates and, therefore, potential ROIs for SGRT DIBH setup. The four most inferior POIs show the highest mean correlation (r=0.75) with diaphragmatic motion, and the correlation decreases as POIs move superiorly. The mean correlations are the highest for ROIs with little or no rib support: r=0.67 for ROI-2, r=0.64 for ROI-5, and r=0.63 for ROI-6, while lower for ROIs with rib support: ROI-1 has r=0.60, ROI-3 has r=0.50, and ROI-4 has only r=0.28. This study demonstrates that the rectangular/triangular soft-tissue ROI (with little rib support) is an optimal surrogate for body positioning and diaphragmatic motion, even when treating tumors under the rib cage. This evidence-based ROI definition should be utilized when treating abdominal cancers with free-breathing (FB) and/or DIBH setup.

NICUface: Robust Neonatal Face Detection in Complex NICU Scenes

The development of non-contact patient monitoring applications for the neonatal intensive care unit (NICU) is an active research area, particularly in facial video analysis. Recent studies have used facial video data to estimate vital signs, assess pain from facial expression, differentiate sleep-wake status, detect jaundice, and in face recognition. These applications depend on an accurate definition of the patient&#x2019;s face as a region of interest (ROI). Most studies have required manual ROI definition, while others have leveraged automated face detectors developed for adult patients, without systematic validation for the neonatal population. To overcome these issues, this paper first evaluates the state-of-the-art in face detection in the NICU setting. Finding that such methods often fail in complex NICU environments, we demonstrate how fine-tuning can increase neonatal face detector robustness, resulting in our NICUface models. A large and diverse neonatal dataset was gathered from actual patients admitted to the NICU across three studies and gold standard face annotations were completed. In comparison to state-of-the-art face detectors, our NICUface models address NICU-specific challenges such as ongoing clinical intervention, phototherapy lighting, occlusions from hospital equipment, etc. These analyses culminate in the creation of robust NICUface detectors with improvements on our most challenging neonatal dataset of +36.14, +35.86, and +32.19 in AP30, AP50, and mAP respectively, relative to state-of-the-art CE-CLM, MTCNN, img2pose, RetinaFace, and YOLO5Face models. Face orientation estimation is also addressed, leading to an accuracy of 99.45%. Fine-tuned NICUface models, gold-standard face annotation data, and the face orientation estimation method are also released here.

Noninvasive estimation of human radiation dosimetry of 18F-FDG by whole-body small animal PET imaging in rats

PurposeSmall animal PET provides the biodistribution of administrated radiotracer in vivo and have a potential to contribute on dosimetry study. The aim of this study is to investigate the effect of region-of-interest (ROI)-delineation in whole-body rat PET image toward non-invasive estimation of human dosimetry of 18F-FDG. MethodAfter administration of 18F-FDG (averaged 11.7 MBq), 3.5-h PET and 20-min CT scans were sequentially performed for three rats by Clairvivo PET/CT system. Seven source organs, and the remainder of the body, were studied to extrapolate %ID(t) and estimate time-integrated activity coefficients [kBq-h/MBq] in human. The mean absorbed dose in each target organ and the effective dose were estimated by MIRD method. Effects of ROI-definitions on both extrapolated %ID(t) in human and estimated doses were also investigated by using (i) small ROIs of high uptake region and (ii) whole organ ROIs. ResultsAveraged effective doses of 18F-FDG in human by using high-uptake and whole-organ ROIs were 27.8 ± 6.54 and 19.3 ± 2.72 μSv/MBq, respectively. ConclusionThe use of small animal PET scanner, which allows repeatedly PET scans, have a potential to contribute on the reduction of the number of experimental animals. However, the ways of ROI drawing influences on the estimated effective dose and safe-side ROI definition may be preferred.

Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy

A brain atlas is necessary for analyzing structure and function in neuroimaging research. Although various annotation volumes (AVs) for the mouse brain have been proposed, it is common in magnetic resonance imaging (MRI) of the mouse brain that regions-of-interest (ROIs) for brain structures (nodes) are created arbitrarily according to each researcher’s necessity, leading to inconsistent ROIs among studies. One reason for such a situation is the fact that earlier AVs were fixed, i.e. combination and division of nodes were not implemented. This report presents a pipeline for constructing a flexible annotation atlas (FAA) of the mouse brain by leveraging public resources of the Allen Institute for Brain Science on brain structure, gene expression, and axonal projection. A mere two-step procedure with user-specified, text-based information and Python codes constructs FAA with nodes which can be combined or divided objectively while maintaining anatomical hierarchy of brain structures. Four FAAs with total node count of 4, 101, 866, and 1381 were demonstrated. Unique characteristics of FAA realized analysis of resting-state functional connectivity (FC) across the anatomical hierarchy and among cortical layers, which were thin but large brain structures. FAA can improve the consistency of whole brain ROI definition among laboratories by fulfilling various requests from researchers with its flexibility and reproducibility.

Teeth Segmentation of Bitewing X-Ray Images Using Wavelet Transform

Within the recent twenty years, the dental X-ray images have widely been employed in forensic odontology for human identification, particularly where mass disasters happen. In this paper, a novel method is proposed for the process of teeth segmentation and individual teeth isolation of Bitewing X-ray radiographs. The main objective of this study is to develop an automatic teeth segmentation approach that can be used in an Automated Dental Identification System (ADIS). The proposed method is based on separating teeth according to edge lines between crowns of teeth. It comprises four phases as image enhancement, edge detection by using wavelet transform, Region of Interest (ROI) definition, and morphological processing. Image enhancement in our case is done by image sharpening using a Butterworth high pass filter. Directional changes of the image and a blurred version of it are obtained by wavelet transform in the second phase. In ROI definition the upper and lower jaws are first separated using the integral intensity projection and then a region containing the desired edge lines are defined. In the final stage, some morphological operations are applied to isolate the teeth based on separating edge lines. The evaluation of the teeth segmentation is measured by isolating accuracy and visual inspection. Experimental results with 90.6% isolation accuracy of total 681 teeth illustrate that the proposed method is more efficient that the existing algorithms.

METROID: an automated method for robust quantification of subcellular fluorescence events at low SNR

BackgroundIn cell biology, increasing focus has been directed to fast events at subcellular space with the advent of fluorescent probes. As an example, voltage sensitive dyes (VSD) have been used to measure membrane potentials. Yet, even the most recently developed genetically encoded voltage sensors have demanded exhausting signal averaging through repeated experiments to quantify action potentials (AP). This analysis may be further hampered in subcellular signals defined by small regions of interest (ROI), where signal-to-noise ratio (SNR) may fall substantially. Signal processing techniques like blind source separation (BSS) are designed to separate a multichannel mixture of signals into uncorrelated or independent sources, whose potential to separate ROI signal from noise has been poorly explored. Our aims are to develop a method capable of retrieving subcellular events with minimal a priori information from noisy cell fluorescence images and to provide it as a computational tool to be readily employed by the scientific community.ResultsIn this paper, we have developed METROID (Morphological Extraction of Transmembrane potential from Regions Of Interest Device), a new computational tool to filter fluorescence signals from multiple ROIs, whose code and graphical interface are freely available. In this tool, we developed a new ROI definition procedure to automatically generate similar-area ROIs that follow cell shape. In addition, simulations and real data analysis were performed to recover AP and electroporation signals contaminated by noise by means of four types of BSS: Principal Component Analysis (PCA), Independent Component Analysis (ICA), and two versions with discrete wavelet transform (DWT). All these strategies allowed for signal extraction at low SNR (− 10 dB) without apparent signal distortion.ConclusionsWe demonstrate the great capability of our method to filter subcellular signals from noisy fluorescence images in a single trial, avoiding repeated experiments. We provide this novel biomedical application with a graphical user interface at https://doi.org/10.6084/m9.figshare.11344046.v1, and its code and datasets are available in GitHub at https://github.com/zoccoler/metroid.

Robust Finger-vein ROI Localization Based on the 3σ Criterion Dynamic Threshold Strategy.

Region of interest (ROI) localization is one of the key preprocessing technologies for a finger-vein identification system, so an effective ROI definition can improve the matching accuracy. However, due to the impact of uneven illumination, equipment noise, as well as the distortion of finger position, etc., these make accurate ROI localization a very difficult task. To address these issues, in this paper, we propose a robust finger-vein ROI localization method, which is based on the 3 criterion dynamic threshold strategy. The proposed method includes three main steps: First, the Kirsch edge detector is introduced to detect the horizontal-like edges in the acquired finger-vein image. Then, the obtained edge gradient image is divided into four parts: upper-left, upper-right, lower-left, and lower-right. For each part of the image, the three-level dynamic threshold, which is based on the 3 criterion of the normal distribution, is imposed to obtain more distinct and complete edge information. Finally, through labeling the longest connected component at the same horizontal line, two reliable finger boundaries, which represent the upper and lower boundaries, respectively, are defined, and the ROI is localized in the region between these two boundaries. Extensive experiments are carried out on four different finger-vein image datasets, including three publicly available datasets and one of our newly developed finger-vein datasets with 37,080 finger-vein samples and 1030 individuals. The experimental results indicate that our proposed method has very competitive ROI localization performance, as well as satisfactory matching results on different datasets.

Validation of ThermoHuman automatic thermographic software for assessing foot temperature before and after running.

The aim of the study was to evaluate an automatic thermographic software package (ThermoHuman®) for assessing skin temperature on the soles of the feet before and after running and to compare it with two manual definitions of the regions of interest (ROIs). 120 thermal images of the soles of the feet of 30 participants, at two measurement points (before and after running 30min) and on two measurement days were analyzed. Three different models of thermographic image analyses were used to obtain the mean temperature of 9 ROIs: A) ThermoHuman (automatic definition of ROIs using ThermoHuman® software), B) Manual (manual delimitation of ROIs by proportion criteria), and C) Manual-TH (manual delimitation of ROIs in an attempt to replicate the regions analyzed by ThermoHuman). ThermoHuman resulted in an 86% reduction in time involved compared to manual delimitation. Fourteen of the 120 images (12%) presented some error in one or more of the ROI delimitations. Although the three procedures presented significant differences between them (53% in the comparison between ThermoHuman and Manual, 47% between ThermoHuman and Manual-TH, and 28% between Manual and Manual-TH), all differences had a small effect size (ES 0.2-0.4) or lower (ES<0.2). Bland-Altman plots showed similar 95% limits of agreement between the three procedures before and after running. Intraclass correlation coefficient analysis of the three procedures presented excellent reliability (ICC>0.8). In conclusion, ThermoHuman® software was observed to be time-saving for image analysis with excellent reliability. Although results suggest that ThermoHuman® and manual methods are both valid in themselves, combining them is not recommended due to the differences observed between them.

Discrimination of benign from malignant breast lesions in dense breasts with model-based analysis of regions-of-interest using directional diffusion-weighted images

BackgroundThere is an increasing interest in non-contrast-enhanced magnetic resonance imaging (MRI) for detecting and evaluating breast lesions. We present a methodology utilizing lesion core and periphery region of interest (ROI) features derived from directional diffusion-weighted imaging (DWI) data to evaluate performance in discriminating benign from malignant lesions in dense breasts.MethodsWe accrued 55 dense-breast cases with 69 lesions (31 benign; 38 cancer) at a single institution in a prospective study; cases with ROIs exceeding 7.50 cm2 were excluded, resulting in analysis of 50 cases with 63 lesions (29 benign, 34 cancers). Spin-echo echo-planar imaging DWI was acquired at 1.5 T and 3 T. Data from three diffusion encoding gradient directions were exported and processed independently. Lesion ROIs were hand-drawn on DWI images by two radiologists. A region growing algorithm generated 3D lesion models on augmented apparent-diffusion coefficient (ADC) maps and defined lesion core and lesion periphery sub-ROIs. A lesion-core and a lesion-periphery feature were defined and combined into an overall classifier whose performance was compared to that of mean ADC using receiver operating characteristic (ROC) analysis. Inter-observer variability in ROI definition was measured using Dice Similarity Coefficient (DSC).ResultsThe region-growing algorithm for 3D lesion model generation improved inter-observer variability over hand drawn ROIs (DSC: 0.66 vs 0.56 (p < 0.001) with substantial agreement (DSC > 0.8) in 46% vs 13% of cases, respectively (p < 0.001)). The overall classifier improved discrimination over mean ADC, (ROC- area under the curve (AUC): 0.85 vs 0.75 and 0.83 vs 0.74 respectively for the two readers).ConclusionsA classifier generated from directional DWI information using lesion core and lesion periphery information separately can improve lesion discrimination in dense breasts over mean ADC and should be considered for inclusion in computer-aided diagnosis algorithms. Our model-based ROIs could facilitate standardization of breast MRI computer-aided diagnostics (CADx).

Personode: A Toolbox for ICA Map Classification and Individualized ROI Definition.

Canonical resting state networks (RSNs) can be obtained through independent component analysis (ICA). RSNs are reproducible across subjects but also present inter-individual differences, which can be used to individualize regions-of-interest (ROI) definition, thus making fMRI analyses more accurate. Unfortunately, no automatic tool for defining subject-specific ROIs exists, making the classification of ICAs as representatives of RSN time-consuming and largely dependent on visual inspection. Here, we present Personode, a user-friendly and open source MATLAB-based toolbox that semi-automatically performs the classification of RSN and allows for defining subject- and group-specific ROIs. To validate the applicability of our new approach and to assess potential improvements compared to previous approaches, we applied Personode to both task-related activation and resting-state data. Our analyses show that for task-related activation analyses, subject-specific spherical ROIs defined with Personode produced higher activity contrasts compared to ROIs derived from single-study and meta-analytic coordinates. We also show that subject-specific irregular ROIs defined with Personode improved ROI-to-ROI functional connectivity analyses.Hence, Personode might be a useful toolbox for ICA map classification into RSNs and group- as well as subject-specific ROI definitions, leading to improved analyses of task-related activation and functional connectivity.

Semantic segmentation with DenseNets for carotid artery ultrasound plaque segmentation and CIMT estimation.

The measurement of carotid intima media thickness (CIMT) in ultrasound images can be used to detect the presence of atherosclerotic plaques. Usually, the CIMT estimation strategy is semi-automatic, since it requires: (1) a manual examination of the ultrasound image for the localization of a region of interest (ROI), a fast and useful operation when only a small number of images need to be measured; and (2) an automatic delineation of the CIM region within the ROI. The existing efforts for automating the process have replicated the same two-step structure, resulting in two consecutive independent approaches. In this work, we propose a fully automatic single-step approach based on semantic segmentation that allows us to segment the plaque and to estimate the CIMT in a fast and useful manner for large data sets of images. Our single-step approach is based on densely connected convolutional neural networks (DenseNets) for semantic segmentation of the whole image. It has two remarkable characteristics: (1) it avoids ROI definition, and (2) it captures multi-scale contextual information in the complete image interpretation, due to the concatenation of feature maps carried out in DenseNets. Once the input image is segmented, a straightforward method for CIMT estimation and plaque detection is applied. The proposed method has been validated with a large data set (REGICOR) of more than 8000 images, corresponding to two territories of the carotid artery: common carotid artery (CCA) and bulb. Among them, a subset of 331 images has been used to evaluate the performance of semantic segmentation (≈90% for train, ≈10% for test). The experimental results demonstrated that our method outperforms other deep models and shallow approaches found in the literature. In particular, our CIMT estimation reaches a correlation coefficient of 0.81, and a CIMT mean error of 0.02 and 0.06 mm in CCA and Bulb images, respectively. Furthermore, the accuracy for plaque detection is 96.45% and 78.09% in CCA and Bulb, respectively. To test the generalization power, the method has also been tested with another data set (NEFRONA) that includes images acquired with different equipment. The validation carried out demonstrates that the proposed method is accurate and objective for both plaque detection and CIMT measurement. Moreover, the robustness and generalization capacity of the method have been proven with two different data sets.

Assessment of a white matter reference region for 11C-UCB-J PET quantification

11C-UCB-J is a positron emission tomography (PET) radioligand that has been used in humans for synaptic vesicle glycoprotein 2A (SV2A) imaging and as a potential synaptic density marker. The centrum semiovale (CS) is a proposed reference region for noninvasive quantification of 11C-UCB-J, due to negligible concentrations of SV2A in this region in baboon brain assessed by in vitro methods. However, in displacement scans with SV2A-specific drug levetiracetam in humans, a decrease in 11C-UCB-J concentration was observed in the CS, consistent with some degree of specific binding. The current study aims to validate the CS as a reference region by (1) optimizing CS region of interest (ROI) to minimize spill-in from gray matter with high radioactivity concentrations; (2) investigating convergence of CS ROI values using ordered subset expectation maximization (OS-EM) reconstruction, and (3) comparing baseline CS volume of distribution (VT) to nondisplaceable uptake in gray matter, VND. Improving ROI definition and increasing OS-EM iterations during reconstruction decreased the difference between CS VT and VND. However, even with these corrections, CS VT overestimated VND by ∼35–40%. These measures showed significant correlation, suggesting that, though biased, the CS may be a useful estimate of nondisplaceable uptake, allowing for noninvasive quantification for SV2A PET.

An assessment of the information lost when applying data reduction techniques to dynamic plantar pressure measurements

Data reduction techniques are commonly applied to dynamic plantar pressure measurements, often prior to the measurement’s analysis. In performing these data reductions, information is discarded from the measurement before it can be evaluated, leading to unkonwn consequences. In this study, we aim to provide the first assessment of what impact data reduction techniques have on plantar pressure measurements. Specifically, we quantify the extent to which information of any kind is discarded when performing common data reductions. Plantar pressure measurements were collected from 33 healthy controls, 8 Hallux Valgus patients, and 10 Metatarsalgia patients. Eleven common data reductions were then applied to the measurements, and the resulting datasets were compared to the original measurement in three ways. First, information theory was used to estimate the information content present in the original and reduced datasets. Second, principal component analysis was used to estimate the number of intrinsic dimensions present. Finally, a permutational multivariate ANOVA was performed to evaluate the significance of group differences between the healthy controls, Hallux Valgus, and Metatarsalgia groups. The evaluated data reductions showed a minimum of 99.1% loss in information content and losses of dimensionality between 20.8% and 83.3%. Significant group differences were also lost after each of the 11 data reductions (α=0.05), but these results may differ for other patient groups (especially those with highly-deformed footprints) or other region of interest definitions. Nevertheless, the existence of these results suggest that the diagnostic content of dynamic plantar pressure measurements is yet to be fully exploited.