Over 30 Years of DiI Use for Human Neuroanatomical Tract Tracing: A Scoping Review.

In the present study, we conducted a scoping review to provide an overview of the existing literature on the carbocyanine dye DiI, in human neuroanatomical tract tracing. The PubMed, Scopus, and Web of Science databases were systematically searched. We identified 61 studies published during the last three decades. While studies incorporated specimens across human life from the embryonic stage onwards, the majority of studies focused on adult human tissue. Studies that utilized peripheral nervous system (PNS) tissue were a minority, with the majority of studies focusing on the central nervous system (CNS). The most common topic of interest in previous tract tracing investigations was the connectivity of the visual pathway. DiI crystals were more commonly applied. Nevertheless, several studies utilized DiI in a paste or dissolved form. The maximum tracing distance and tracing speed achieved was, respectively, 70 mm and 1 mm/h. We identified studies that focused on optimizing tracing efficacy by varying parameters such as fixation, incubation temperature, dye re-application, or the application of electric fields. Additional studies aimed at broadening the scope of DiI use by assessing the utility of archival tissue and compatibility of tissue clearing in DiI applications. A combination of DiI tracing and immunohistochemistry in double-labeling studies have been shown to provide the means for assessing connectivity of phenotypically defined human CNS and PNS neuronal populations.

A Robust Machine Learning Framework Built Upon Molecular Representations Predicts CYP450 Inhibition: Toward Precision in Drug Repurposing.

Human cytochrome P450 (CYP450) enzymes play a crucial role in drug metabolism and pharmacokinetics. CYP450 inhibition can lead to toxicity, in particular when drugs are co-administered with other drugs and xenobiotics or in the case of polypharmacy. Predicting CYP450 inhibition is also important for rational drug discovery and development, and precision in drug repurposing. In this overarching context, digital transformation of drug discovery and development, for example, using machine and deep learning approaches, offers prospects for prediction of CYP450 inhibition through computational models. We report here the development of a majority-voting machine learning framework to classify inhibitors and noninhibitors for seven major human liver CYP450 isoforms (CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). For the machine learning models reported herein, we employed interaction fingerprints that were derived from molecular docking simulations, thus adding an additional layer of information for protein-ligand interactions. The proposed machine learning framework is based on the structure of the binding site of isoforms to produce predictions beyond previously reported approaches. Also, we carried out a comparative analysis so as to identify which representation of test compounds (molecular descriptors, molecular fingerprints, or protein-ligand interaction fingerprints) affects the predictive performance of the models. This work underlines the ways in which the structure of the enzyme catalytic site influences machine learning predictions and the need for robust frameworks toward better-informed predictions.

The Biomedical Engineering Labor Market in Greece: A Survey Investigating Job Outlook, Satisfaction and Placement.

In this study, we have evaluated the real-world conditions, the job outlook and the job satisfaction in the Biomedical Engineering (BME) sector in Greece on the basis of the experience of about 12% of the graduates of the BME Department of the University of West Attica, Greece. An anonymous online questionnaire, implemented on the Microsoft Forms platform using multiple choice questions, short text answers and Likert-based scales, became publicly available to the graduates of the BME department. About 12% of the department's graduates responded to the survey. Results show that the time to first employment is very fast for both men and women. About 51.4% of men and 69.4% of women find their first job employment in the BME sector even before their graduation. The internship is considered important for first job placement by more than 50.6% of participants. BME jobs are perceived as most interesting (73.6%), in a good environment (71.9%), with satisfactory career prospects (45.9%), with satisfactory monthly net salary (44.2%) and satisfactory working hours (52.8%). Men are mostly employed in Service (40.5%), whereas women are mostly employed in Sales (33.3%). Most graduates with BSc degree are employed in Service (39.1%) and Sales (21.8%), most graduates with MSc degree are employed in Service (34.6%) and Hospitals/Health care centers (21.2%), and most graduates with PhD degree are employed in Academia and R&D (62.5%). Most well-paid participants (>1500 euros net salary) were PhD holders (71.5%), followed by MSc holders (25%) and BSc holders (16.2%). Maximum monthly salaries were found for those with more than 10 years of experience. In terms of BME sector, most well-paid participants (>1500 euros monthly net salary) are those working with R&D (86.7%), Sales (86.7%) and Management (60%). There is a high demand for biomedical engineers in the labor market in Greece, despite the continuing economic recession that the country is suffering from the past 12 years.

Employing machine learning and microscopy images of AIB1-stained biopsy material to assess the 5-year survival of patients with colorectal cancer.

Our purpose was to employ microscopy images of amplified in breast cancer 1 (AIB1)-stained biopsy material of patients with colorectal cancer (CRC) to: (a) find statistically significant differences (SSDs) in the texture and color of the epithelial gland tissue, between 5-year survivors and non-survivors after the first diagnosis and (b) employ machine learning (ML) methods for predicting the CRC-patient 5-year survival. We collected biopsy material from 54 patients with diagnosed CRC from the archives of the University Hospital of Patras, Greece. Twenty-six of the patients had survived 5 years after the first diagnosis. We selected regions of interest containing the epithelial gland at different microscope lens magnifications. We computed 69 textural and color features. Furthermore, we identified features with SSDs between the two groups of patients and we designed a supervised ML system for predicting the CRC-patient 5-year survival. Additionally, we employed the VGG16 pretrained convolution neural network to extract deep learning (DL) features, the support vector machines classifier, and the bootstrap cross-validation method for boosting the accuracy of predicting 5-year survival. Fourteen features sustained SSDs between the two groups of patients. The supervised ML system achieved 87% accuracy in predicting 5-year survival. In comparison, the DL system, using images from all magnifications, gave 97% classification accuracy. Glandular texture in 5-year non-survivors appeared to be of lower contrast, coarseness, roughness, local pixel correlation, and lower AIB1 variation, all indicating loss of textural definition. The supervised ML system revealed useful information regarding features that discriminate between 5-year survivors and non-survivors while the DL system displayed superior accuracy by employing DL features.

Assessment of HPV Risk Type in H&E-stained Biopsy Specimens of the Cervix by Microscopy Image Analysis.

OBJECTIVES: The objective of this study was (a) to identify, by computer processing of digitized images of hematoxylin and eosin (H&E)-stained biopsy material of the cervix, differences in the structure of nuclei between high-risk (HR) and low-risk (LR) human papillomavirus virus (HPV) types and (b) to assess the HPV risk type by designing a decision-support system (DSS). MATERIALS AND METHODS: Clinical material comprised H&E-stained biopsies from squamous intraepithelial lesions of 55 patients with polymerase chain reaction-verified HR-HPV (26 patients) or LR-HPV (29 patients) infection. From each patient's biopsy specimen, we digitized 1 region of interest, guided by the expert physician. After the segmentation of nuclei, we quantified from each nucleus 77 textural and morphologic features. We represented each patient by a 77-feature vector, the feature means of all nuclei, and we created 2 classes for HR-HPV and LR-HPV types. We carried out (a) a statistical analysis to determine features with statistically significant differences between the 2 classes and (b) a discriminant analysis, by designing a DSS, to estimate the HPV risk type. RESULTS: Statistical analysis revealed 40 features with between-classes statistically significant differences and discriminant analysis showed that the best DSS design achieved a high accuracy of about 93% in identifying the HPV risk type on data not used in the design of the DSS. CONCLUSIONS: Nuclei of HR-HPV types were of higher intensity, contained larger structures, had higher edges, were coarser, rougher, had higher contrast, were larger, and attained more irregular shapes. The proposed DSS indicates that discrimination of HPV risk type from images of H&E-stained biopsy material of the cervix is promising.

GPU-enabled design of an adaptable pattern recognition system for discriminating squamous intraepithelial lesions of the cervix.

The aim of the present study was to design an adaptable pattern recognition (PR) system to discriminate low- from high-grade squamous intraepithelial lesions (LSIL and HSIL, respectively) of the cervix using microscopy images of hematoxylin and eosin (H&E)-stained biopsy material from two different medical centers. Clinical material comprised H&E-stained biopsies of 66 patients diagnosed with LSIL (34 cases) or HSIL (32 cases). Regions of interest were selected from each patient's digitized microscopy images. Seventy-seven features were generated, regarding the texture, morphology and spatial distribution of nuclei. The probabilistic neural network (PNN) classifier, the exhaustive search feature selection method, the leave-one-out (LOO) and the bootstrap validation methods were used to design the PR system and to assess its precision. Optimal PR system design and evaluation were made feasible by the employment of graphics processing unit (GPU) and Compute Unified Device Architecture (CUDA) technologies. The accuracy of the PR-system was 93% and 88.6% when using the LOO and bootstrap validation methods, respectively. The proposed PR system for discriminating LSIL from HSIL of the cervix was designed to operate in a clinical environment, having the capability of being redesigned when new verified cases are added to its repository and when data from other medical centers are included, following similar biopsy material preparation procedures.

Correlating Changes in the Epithelial Gland Tissue With Advancing Colorectal Cancer Histologic Grade, Using IHC Stained for AIB1 Expression Biopsy Material.

OBJECTIVE: The objective of this study was to study the textural and color changes occurring in the epithelial gland tissue with advancing colorectal cancer (CRC), utilizing immunohistochemical stain for AIB1 expression biopsy material. MATERIAL AND METHODS: Clinical material comprised biopsy specimens of 67 patients with a diagnosis of CRC. Two experienced pathologists used H&E-stained material for grading CRC lesions and immunohistochemical (IHC) stain for AIB1 expression. Twenty six patients were diagnosed with grade I, 28 with grade II, and 13 with grade III CRC. Guided by pathologists, we selected the regions of interest from AIB1-digitized images of each patient, encompassing the epithelial gland, and we computed 69 features, quantifying textural and color properties of the AIB1-stained lesions. We evaluated the statistical differences between grades by means of the Wilcoxon statistical test for each feature, and we assessed changes in feature values with advancing tumor grade by means of the Point Biserial Correlation. RESULTS: Statistical analysis revealed 14 single features, quantifying textural and color properties of the epithelial gland, which sustained statistically significant differences between LG-CRC and HG-CRC cases. These features were drawn from the gray-level image histogram, the cooccurrence matrix, the run length matrix, the discrete wavelet transform, the Tamura method, and the L*a*b color transform. CONCLUSIONS: A systematic statistical analysis of AIB1-stained biopsy material showed that high-grade CRC lesions contain higher intensity levels, appear coarser, are more homogeneous with smooth variation across the image, have lower contrast that is slowly varying across the image, have lower AIB1 staining, and have lower edges. A combination of textural and color attributes, evaluating image gray-tone distribution, textural roughness, inhomogeneity, AIB1 staining, and image coarseness should be considered in evaluating AIB1-stained CRC lesions.

Multifeature Quantification of Nuclear Properties from Images of H&E-Stained Biopsy Material for Investigating Changes in Nuclear Structure with Advancing CIN Grade.

Background: Cervical dysplasia is a precancerous condition, and if left untreated, it may lead to cervical cancer, which is the second most common cancer in women. The purpose of this study was to investigate differences in nuclear properties of the H&E-stained biopsy material between low CIN and high CIN cases and associate those properties with the CIN grade. Methods: The clinical material comprised hematoxylin and eosin- (H&E-) stained biopsy specimens from lesions of 44 patients diagnosed with cervical intraepithelial neoplasia (CIN). Four or five nonoverlapping microscopy images were digitized from each patient's H&E specimens, from regions indicated by the expert physician. Sixty-three textural and morphological nuclear features were generated for each patient's images. The Wilcoxon statistical test and the point biserial correlation were used to estimate each feature's discriminatory power between low CIN and high CIN cases and its correlation with the advancing CIN grade, respectively. Results: Statistical analysis showed 19 features that quantify nuclear shape, size, and texture and sustain statistically significant differences between low CIN and high CIN cases. These findings revealed that nuclei in high CIN cases, as compared to nuclei in low CIN cases, have more irregular shape, are larger in size, are coarser in texture, contain higher edges, have higher local contrast, are more inhomogeneous, and comprise structures of different intensities. Conclusion: A systematic statistical analysis of nucleus features, quantified from the H&E-stained biopsy material, showed that there are significant differences in the shape, size, and texture of nuclei between low CIN and high CIN cases.

Adaptable pattern recognition system for discriminating Melanocytic Nevi from Malignant Melanomas using plain photography images from different image databases.

OBJECTIVE: The aim of this study was to propose features that evaluate pictorial differences between melanocytic nevus (mole) and melanoma lesions by computer-based analysis of plain photography images and to design a cross-platform, tunable, decision support system to discriminate with high accuracy moles from melanomas in different publicly available image databases. MATERIAL AND METHODS: Digital plain photography images of verified mole and melanoma lesions were downloaded from (i) Edinburgh University Hospital, UK, (Dermofit, 330moles/70 melanomas, under signed agreement), from 5 different centers (Multicenter, 63moles/25 melanomas, publicly available), and from the Groningen University, Netherlands (Groningen, 100moles/70 melanomas, publicly available). Images were processed for outlining the lesion-border and isolating the lesion from the surrounding background. Fourteen features were generated from each lesion evaluating texture (4), structure (5), shape (4) and color (1). Features were subjected to statistical analysis for determining differences in pictorial properties between moles and melanomas. The Probabilistic Neural Network (PNN) classifier, the exhaustive search features selection, the leave-one-out (LOO), and the external cross-validation (ECV) methods were used to design the PR-system for discriminating between moles and melanomas. RESULTS: Statistical analysis revealed that melanomas as compared to moles were of lower intensity, of less homogenous surface, had more dark pixels with intensities spanning larger spectra of gray-values, contained more objects of different sizes and gray-levels, had more asymmetrical shapes and irregular outlines, had abrupt intensity transitions from lesion to background tissue, and had more distinct colors. The PR-system designed by the Dermofit images scored on the Dermofit images, using the ECV, 94.1%, 82.9%, 96.5% for overall accuracy, sensitivity, specificity, on the Multicenter Images 92.0%, 88%, 93.7% and on the Groningen Images 76.2%, 73.9%, 77.8% respectively. CONCLUSION: The PR-system as designed by the Dermofit image database could be fine-tuned to classify with good accuracy plain photography moles/melanomas images of other databases employing different image capturing equipment and protocols.

Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research.

Histopathology image processing, analysis and computer-aided diagnosis have been shown as effective assisting tools towards reliable and intra-/inter-observer invariant decisions in traditional pathology. Especially for cancer patients, decisions need to be as accurate as possible in order to increase the probability of optimal treatment planning. In this study, we propose a new image collection library (HICL-Histology Image Collection Library) comprising 3831 histological images of three different diseases, for fostering research in histopathology image processing, analysis and computer-aided diagnosis. Raw data comprised 93, 116 and 55 cases of brain, breast and laryngeal cancer respectively collected from the archives of the University Hospital of Patras, Greece. The 3831 images were generated from the most representative regions of the pathology, specified by an experienced histopathologist. The HICL Image Collection is free for access under an academic license at http://medisp.bme.teiath.gr/hicl/ . Potential exploitations of the proposed library may span over a board spectrum, such as in image processing to improve visualization, in segmentation for nuclei detection, in decision support systems for second opinion consultations, in statistical analysis for investigation of potential correlations between clinical annotations and imaging findings and, generally, in fostering research on histopathology image processing and analysis. To the best of our knowledge, the HICL constitutes the first attempt towards creation of a reference image collection library in the field of traditional histopathology, publicly and freely available to the scientific community.

Computer-based automated estimation of breast vascularity and correlation with breast cancer in DCE-MRI images.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with gadolinium constitutes one of the most promising protocols for boosting up the sensitivity in breast cancer detection. The aim of this study was twofold: first to design an image processing methodology to estimate the vascularity of the breast region in DCE-MRI images and second to investigate whether the differences in the composition/texture and vascularity of normal, benign and malignant breasts may serve as potential indicators regarding the presence of the disease. Clinical material comprised thirty nine cases examined on a 3.0-T MRI system (SIGNA HDx; GE Healthcare). Vessel segmentation was performed using a custom made modification of the Seeded Region Growing algorithm that was designed in order to identify pixels belonging to the breast vascular network. Two families of features were extracted: first, morphological and textural features from segmented images in order to quantify the extent and the properties of the vascular network; second, textural features from the whole breast region in order to investigate whether the nature of the disease causes statistically important changes in the texture of affected breasts. Results have indicated that: (a) the texture of vessels presents statistically significant differences (p<0.001) between normal, benign and malignant cases, (b) the texture of the whole breast region for malignant and non-malignant breasts, produced statistically significant differences (p<0.001), (c) the relative ratios of the texture between the two breasts may be used for the discrimination of non-malignant from malignant patients, and (d) an area under the receiver operating characteristic curve of 0.908 (AUC) was found when features were combined in a logistic regression prediction rule according to ROC analysis.

Microscopy image analysis of p63 immunohistochemically stained laryngeal cancer lesions for predicting patient 5-year survival.

The aim of the present study was to design a microscopy image analysis (MIA) system for predicting the 5-year survival of patients with laryngeal squamous cell carcinoma, employing histopathology images of lesions, which had been immunohistochemically (IHC) stained for p63 expression. Biopsy materials from 42 patients, with verified laryngeal cancer and follow-up, were selected from the archives of the University Hospital of Patras, Greece. Twenty six patients had survived more than 5 years and 16 less than 5 years after the first diagnosis. Histopathology images were IHC stained for p63 expression. Images were first processed by a segmentation method for isolating the p63-expressed nuclei. Seventy-seven features were evaluated regarding texture, shape, and physical topology of nuclei, p63 staining, and patient-specific data. Those features, the probabilistic neural network classifier, the leave-one-out (LOO), and the bootstrap cross-validation methods, were used to design the MIA-system for assessing the 5-year survival of patients with laryngeal cancer. MIA-system accuracy was about 90 % and 85 %, employing the LOO and the Bootstrap methods, respectively. The image texture of p63-expressed nuclei appeared coarser and contained more edges in the 5-year non-survivor group. These differences were at a statistically significant level (p < 0.05). In conclusion, this study has proposed an MIA-system that may be of assistance to physicians, as a second opinion tool in assessing the 5-year survival of patients with laryngeal cancer, and it has revealed useful information regarding differences in nuclei texture between 5-year survivors and non-survivors.

EEG-based investigation of brain connectivity changes in psychotic patients undergoing the primitive expression form of dance therapy: a methodological pilot study.

Primitive expression (PE) is a form of dance therapy (DT) that involves an interaction of ethologically and socially based forms which are supplied for re-enactment. There exist very few studies of DT applications including in their protocol the measurement of neurophysiological parameters. The present pilot study investigates the use of the correlation coefficient (ρ) and mutual information (MI), and of novel measures extracted from ρ and MI, on electroencephalographic (EEG) data recorded in patients with schizophrenia while they undergo PE DT, in order to expand the set of neurophysiology-based approaches for quantifying possible DT effects, using parameters that might provide insights about any potential brain connectivity changes in these patients during the PE DT process. Indication is provided for an acute potentiation effect, apparent at late-stage PE DT, on the inter-hemispheric connectivity in frontal areas, as well as for attenuation of the inter-hemispheric connectivity of left frontal and right central areas and for potentiation of the intra-hemispheric connectivity of frontal and central areas, bilaterally, in the transition from early to late-stage PE DT. This pilot study indicates that by using EEG connectivity measures based on ρ and MI, the set of useful neurophysiology-based approaches for quantifying possible DT effects is expanded. In the framework of the present study, the causes of the observed connectivity changes cannot be attributed with certainty to PE DT, but indications are provided that these measures may contribute to a detailed assessment of neurophysiological mechanisms possibly being affected by this therapeutic process.

Dynamics of regional brain activity in epilepsy: a cross-disciplinary study on both intracranial and scalp-recorded epileptic seizures.

OBJECTIVE: Recent cross-disciplinary literature suggests a dynamical analogy between earthquakes and epileptic seizures. This study extends the focus of inquiry for the applicability of models for earthquake dynamics to examine both scalp-recorded and intracranial electroencephalogram recordings related to epileptic seizures. APPROACH: First, we provide an updated definition of the electric event in terms of magnitude and we focus on the applicability of (i) a model for earthquake dynamics, rooted in a nonextensive Tsallis framework, (ii) the traditional Gutenberg and Richter law and (iii) an alternative method for the magnitude-frequency relation for earthquakes. Second, we apply spatiotemporal analysis in terms of nonextensive statistical physics and we further examine the behavior of the parameters included in the nonextensive formula for both types of electroencephalogram recordings under study. MAIN RESULTS: We confirm the previously observed power-law distribution, showing that the nonextensive formula can adequately describe the sequences of electric events included in both types of electroencephalogram recordings. We also show the intermittent behavior of the epileptic seizure cycle which is analogous to the earthquake cycles and we provide evidence of self-affinity of the regional electroencephalogram epileptic seizure activity. SIGNIFICANCE: This study may provide a framework for the analysis and interpretation of epileptic brain activity and other biological phenomena with similar underlying dynamical mechanisms.

Computer based correlation of the texture of P63 expressed nuclei with histological tumour grade, in laryngeal carcinomas.

BACKGROUND: P63 immunostaining has been considered as potential prognostic factor in laryngeal cancer. Considering that P63 is mainly nuclear stain, a possible correlation between the texture of P63-stained nuclei and the tumor's grade could be of value to diagnosis, since this may be related to biologic information imprinted as texture on P63 expressed nuclei. OBJECTIVE: To investigate the association between P63 stained nuclei and histologic grade in laryngeal tumor lesions. METHODS: Biopsy specimens from laryngeal tumour lesions of 55 patients diagnosed with laryngeal squamous cell carcinomas were immunohistochemically (IHC) stained for P63 expression. Four images were digitized from each patient's IHC specimens. P63 positively expressed nuclei were identified, the percentage of P63 expressed nuclei was computed, and 118 textural, morphological, shape, and architectural features were calculated from each one of the 55 laryngeal lesions. Data were split into the low grade (21 grade I lesions) and high grade (34 grade II and grade III lesions) classes for statistical analysis. RESULTS: With advancing grade, P63 expression decreased, P63 stained nuclei appeared of lower image intensity, more inhomogeneous, of higher local contrast, contained smaller randomly distributed dissimilar structures and had irregular shape. CONCLUSION: P63 expressed nuclei contain important information related to histologic grade.

Computer-based image analysis system designed to differentiate between low-grade and high-grade laryngeal cancer cases.

OBJECTIVE: To design a pattern recognition (PR) system for discriminating between low- and high-grade laryngeal cancer cases, employing immunohistochemically stained, for p63 expression, histopathology images. STUDY DESIGN: The PR system was designed to assist in the physician's diagnosis for improving patient survival. The material comprised 55 verified cases of laryngeal cancer, 21 of low-grade and 34 of high-grade malignancy. Histopathology images were first processed for automatically segmenting p63 expressed nuclei. Fifty-two features were next extracted from the segmented nuclei, concerning nuclei texture, shape, and physical topology in the image. Those features and the Probabilistic Neural Network classifier were used to design the PR system on the multiprocessors of the Nvidia 580 GTX graphics processing unit (GPU) card using the Compute Unified Device Architecture parallel programming model and C++ programming language. RESULTS: PR system performance in classifying laryngeal cancer cases as low grade and high grade was 85.7% and 94.1%, respectively. The system's overall accuracy was 90.9%, using 7 features, and its estimated accuracy to "unseen" by the system cases was 80%. CONCLUSION: Optimum system design was feasible after employing parallel processing techniques and GPU technology. The proposed system was structured so as to function in a clinical environment, as a research tool, and with the capability of being redesigned on site when new verified cases are added to its repository.

Real time decision support system for diagnosis of rare cancers, trained in parallel, on a graphics processing unit.

In the present study a new strategy is introduced for designing and developing of an efficient dynamic Decision Support System (DSS) for supporting rare cancers decision making. The proposed DSS operates on a Graphics Processing Unit (GPU) and it is capable of adjusting its design in real time based on user-defined clinical questions in contrast to standard CPU implementations that are limited by processing and memory constrains. The core of the proposed DSS was a Probabilistic Neural Network classifier and was evaluated on 140 rare brain cancer cases, regarding its ability to predict tumors' malignancy, using a panel of 20 morphological and textural features Generalization was estimated using an external 10-fold cross-validation. The proposed GPU-based DSS achieved significantly higher training speed, outperforming the CPU-based system by a factor that ranged from 267 to 288 times. System design was optimized using a combination of 4 textural and morphological features with 78.6% overall accuracy, whereas system generalization was 73.8%±3.2%. By exploiting the inherently parallel architecture of a consumer level GPU, the proposed approach enables real time, optimal design of a DSS for any user-defined clinical question for improving diagnostic assessments, prognostic relevance and concordance rates for rare cancers in clinical practice.

A wavelet-based Markov random field segmentation model in segmenting microarray experiments.

In the present study, an adaptation of the Markov Random Field (MRF) segmentation model, by means of the stationary wavelet transform (SWT), applied to complementary DNA (cDNA) microarray images is proposed (WMRF). A 3-level decomposition scheme of the initial microarray image was performed, followed by a soft thresholding filtering technique. With the inverse process, a Denoised image was created. In addition, by using the Amplitudes of the filtered wavelet Horizontal and Vertical images at each level, three different Magnitudes were formed. These images were combined with the Denoised one to create the proposed SMRF segmentation model. For numerical evaluation of the segmentation accuracy, the segmentation matching factor (SMF), the Coefficient of Determination (r(2)), and the concordance correlation (p(c)) were calculated on the simulated images. In addition, the SMRF performance was contrasted to the Fuzzy C Means (FCM), Gaussian Mixture Models (GMM), Fuzzy GMM (FGMM), and the conventional MRF techniques. Indirect accuracy performances were also tested on the experimental images by means of the Mean Absolute Error (MAE) and the Coefficient of Variation (CV). In the latter case, SPOT and SCANALYZE software results were also tested. In the former case, SMRF attained the best SMF, r(2), and p(c) (92.66%, 0.923, and 0.88, respectively) scores, whereas, in the latter case scored MAE and CV, 497 and 0.88, respectively. The results and support the performance superiority of the SMRF algorithm in segmenting cDNA images.

Quantitative combination of volumetric MR imaging and MR spectroscopy data for the discrimination of meningiomas from metastatic brain tumors by means of pattern recognition.

The analysis of information derived from magnetic resonance imaging (MRI) and spectroscopy (MRS) has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to investigate the efficiency of the combination of textural MRI features and MRS metabolite ratios by means of a pattern recognition system in the task of discriminating between meningiomas and metastatic brain tumors. The data set consisted of 40 brain MR image series and their corresponding spectral data obtained from patients with verified tumors. The pattern recognition system was designed employing the support vector machines classifier with radial basis function kernel; the system was evaluated using an external cross validation process to render results indicative of the generalization performance to "unknown" cases. The combination of MR textural and spectroscopic features resulted in 92.15% overall accuracy in discriminating meningiomas from metastatic brain tumors. The fusion of the information derived from MRI and MRS data might be helpful in providing clinicians a useful second opinion tool for accurate characterization of brain tumors.

Development and evaluation of a PDA-based teleradiology terminal in thyroid nodule diagnosis.

We developed a wireless personal digital assistant (PDA)-based teleradiology terminal which allowed a secure connection to the hospital's Picture Archiving and Communication System (PACS) through the DICOM protocol. Ten members of the hospital's medical staff completed a questionnaire about its mobility, usability, stability, performance and diagnostic efficiency in a real health-care environment. There was a high degree of satisfaction with the system's mobility (mean score 4.1, SD 1.0, on a five-point scale), usability (mean score 4.2, SD 1.1), stability (mean score 3.9, SD 0.4) and performance (mean score 4.2, SD 0.6). The system was evaluated as a tool for providing assistance in diagnosing thyroid nodules from ultrasound images. A total of 144 ultrasound images with thyroid nodules were assessed by an expert. Six image quality attributes were evaluated. The physician concluded that the ultrasound thyroid images on the PDA screen were of similar quality to those displayed on a diagnostic visual display unit screen. However, the expert found difficulties in diagnosing microcalcification, internal echo texture and vascularity. The PDA terminal provided rapid, secure and convenient portable access to PACS images and the image quality was sufficient for diagnostic interpretation of ultrasound images of the thyroid.