During pregnancy, oxygen diffuses from maternal to fetal blood through villous trees in the placenta. In this paper, we simulate blood flow and oxygen transfer in feto-placental capillaries by converting three-dimensional representations of villous and capillary surfaces, reconstructed from confocal laser scanning microscopy, to finite-element meshes, and calculating values of vascular flow resistance and total oxygen transfer. The relationship between the total oxygen transfer rate and the pressure drop through the capillary is shown to be captured across a wide range of pressure drops by physical scaling laws and an upper bound on the oxygen transfer rate. A regression equation is introduced that can be used to estimate the oxygen transfer in a capillary using the vascular resistance. Two techniques for quantifying the effects of statistical variability, experimental uncertainty and pathological placental structure on the calculated properties are then introduced. First, scaling arguments are used to quantify the sensitivity of the model to uncertainties in the geometry and the parameters. Second, the effects of localized dilations in fetal capillaries are investigated using an idealized axisymmetric model, to quantify the possible effect of pathological placental structure on oxygen transfer. The model predicts how, for a fixed pressure drop through a capillary, oxygen transfer is maximized by an optimal width of the dilation. The results could explain the prevalence of fetal hypoxia in cases of delayed villous maturation, a pathology characterized by a lack of the vasculo-syncytial membranes often seen in conjunction with localized capillary dilations.

• MeSH
• biologické modely * MeSH
• choriové klky embryologie   MeSH
• difuze MeSH
• kapiláry metabolismus   fyziologie   MeSH
• krevní oběh * MeSH
• kyslík metabolismus   MeSH
• lidé MeSH
• placenta krevní zásobení   MeSH
• plod krevní zásobení   MeSH
• těhotenství MeSH
• zobrazování trojrozměrné * MeSH
• Check Tag
• lidé MeSH
• těhotenství MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• kyslík MeSH

BACKGROUND: Percutaneous microwave ablation is a clinically established method for treatment of unresectable lung nodules. When planning the intervention, the size of ablation zone, which should encompass the nodule as well as a surrounding margin of normal tissue, is predicted via manufacturer-provided geometric models, which do not consider patient-specific characteristics. However, the size and shape of ablation is dependent on tissue composition and properties and can vary between patients. PURPOSE: To comparatively assess performance of a computational model-based approach and manufacturer geometric model for predicting extent of ablation zones following microwave lung ablation procedures on a retrospectively collected clinical imaging dataset. METHODS: A retrospective computed-tomography (CT) imaging dataset was assembled of 50 patients treated with microwave ablation of lung tumors at a single institution. For each case, the dataset consisted of a pre-procedure CT acquired without the ablation applicator, a peri-procedure CT scan with the ablation applicator in position, and post-procedure CT scan to assess the ablation zone extent acquired on the first follow-up visit. A physics-based computational model of microwave absorption and bioheat transfer was implemented using the finite element method, with the model geometry incorporating key tissue types within 2 cm of the applicator as informed by imaging data. The model-predicted extent of the ablation zone was estimated using the Arrhenius thermal damage model. The ablation zone predicted by the manufacturer geometric model consisted of an ellipsoid registered with the applicator position and dimensions provided by instructions for use documentation. Both ablation estimates were compared to ground truth ablation zone segmented from post-procedure CT via Dice similarity coefficient (DSC) and average absolute error (AAE). The statistically significant difference at level 0.05 in performance between both ablation prediction methods was tested with permutation test on DSC as well as AAE datasets with applied Bonferroni multiple-comparison correction. RESULTS: Receiver operating characteristic analysis of the predictive power of the volume of insufficient coverage (i.e. tumor volume which did not receive an ablative thermal dose) as an indicator of local tumor recurrence yielded an area under the curve of 0.84, illustrating the clinical significance of accurate prediction of ablation zone extents. Across all cases, AAEs were 3.65 ± 1.12 mm, and 5.11 ± 1.93 mm for patient-specific computational and manufacturer geometric models respectively. Similarly, average DSCs were 0.55 ± 0.14, and 0.46 ± 0.19 for computational and manufacturer geometric models respectively. The manufacturer geometric model overpredicted volume of the ablation zone compared to ground truth by 141% on average, whereas the patient-specific computational model overpredicted ablation zone volumes by 31.5% on average. CONCLUSIONS: Patient-specific physics-based computational models of lung microwave ablation yield improved prediction of microwave ablation extent compared to the manufacturer geometric model.

• Klíčová slova
• image‐based modeling, lung ablation, microwave ablation, treatment planning,
• Publikační typ
• časopisecké články MeSH

Social networks have greatly expanded in the last ten years the need for sharing multimedia data. However, on open networks such as the Internet, where security is frequently compromised, it is simple for eavesdroppers to approach the actual contents without much difficulty. Researchers have created a variety of encryption methods to strengthen the security of this transmission and make it difficult for eavesdroppers to get genuine data. However, these conventional approaches increase computing costs and communication overhead and do not offer protection against fresh threats. The problems with current algorithms encourage academics to further investigate the subject and suggest new algorithms that are more effective than current methods, that reduce overhead, and which are equipped with features needed by next-generation multimedia networks. In this paper, a genetic operator-based encryption method for multimedia security is proposed. It has been noted that the proposed algorithm produces improved key strength results. The investigations using attacks on data loss, differential assaults, statistical attacks, and brute force attacks show that the encryption technique suggested has improved security performance. It focuses on two techniques, bitplane slicing and followed by block segmentation and scrambling. The suggested method first divides the plaintext picture into several blocks, which is then followed by block swapping done by the genetic operator used to combine the genetic information of two different images to generate new offspring. The key stream is produced from an iterative chaotic map with infinite collapse (ICMIC). Based on a close-loop modulation coupling (CMC) approach, a three-dimensional hyperchaotic ICMIC modulation map is proposed. By using a hybrid model of multidirectional circular permutation with this map, a brand-new colour image encryption algorithm is created. In this approach, a multidirectional circular permutation is used to disrupt the image's pixel placements, and genetic operations are used to replace the pixel values. According to simulation findings and security research, the technique can fend off brute-force, statistical, differential, known-plaintext, and chosen-plaintext assaults, and has a strong key sensitivity.

• Klíčová slova
• CCI map, bitplane slicing, block swapping, logistic map, mutation, scrambling,
• MeSH
• algoritmy MeSH
• operátorové oblasti (genetika) MeSH
• počítačová simulace MeSH
• počítačové zpracování obrazu * metody   MeSH
• zabezpečení počítačových systémů * MeSH
• Publikační typ
• časopisecké články MeSH

Colposcopy is a well-established method to detect and diagnose intraepithelial lesions and uterine cervical cancer in early stages. During the exam color and texture changes are induced by the application of a contrast agent (e.g.3-5% acetic acid solution or iodine). Our aim is to densely quantify the change in the acetowhite decay level for a sequence of images captured during a colposcopy exam to help the physician in his diagnosis providing new tools that overcome subjectivity and improve reproducibility. As the change in acetowhite decay level must be calculated from the same tissue point in all images, we present an elastic image registration scheme able to compensate patient, camera and tissue movement robustly in cervical images. The image registration is based on a novel multi-feature entropy similarity criterion. Temporal features are then extracted using the color properties of the aligned image sequence and a dual compartment tissue model of the cervix. An example of the use of the temporal features for pixel-wise classification is presented and the results are compared against ground truth histopathological annotations.

• MeSH
• algoritmy MeSH
• cervix uteri patologie   MeSH
• databáze faktografické MeSH
• diagnóza počítačová metody   MeSH
• dospělí MeSH
• kolposkopie metody   MeSH
• kyselina octová chemie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• nádory děložního čípku diagnóza   MeSH
• počítačové zpracování obrazu metody   MeSH
• reprodukovatelnost výsledků MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• kyselina octová MeSH

The paper describes a set of approaches and routines designed to improve results in CT based 3D subtractive angiography of lower extremities via better global locally defined image data registration. Starting from the generic concept of 3D disparity-based flexible registration, modifications of this idea are made founded on prior anatomical knowledge, as segmentation into individual bone areas, their rigid registration followed by constrained flexible registration, and flexible registration of soft tissue volumes. After final subtraction, fusion of the individually derived volumes into the full volume of extremities provides the medically assessable results. The level of detail in minor vessels, and continuity of vessels including those in direct contact with the bones, have been found much better clinically than those achieved by standard contemporary commercial software.

• MeSH
• algoritmy MeSH
• artefakty * MeSH
• biologické modely MeSH
• digitální subtrakční angiografie metody   MeSH
• lidé MeSH
• počítačová rentgenová tomografie metody   MeSH
• rentgenový obraz - interpretace počítačová metody   MeSH
• reprodukovatelnost výsledků MeSH
• rozpoznávání automatizované metody   MeSH
• senzitivita a specificita MeSH
• subtrakční technika MeSH
• vylepšení rentgenového snímku metody   MeSH
• zobrazování trojrozměrné metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• hodnotící studie MeSH
• práce podpořená grantem MeSH

MOTIVATION: Whole genome expression profiling of large cohorts of different types of cancer led to the identification of distinct molecular subcategories (subtypes) that may partially explain the observed inter-tumoral heterogeneity. This is also the case of colorectal cancer (CRC) where several such categorizations have been proposed. Despite recent developments, the problem of subtype definition and recognition remains open, one of the causes being the intrinsic heterogeneity of each tumor, which is difficult to estimate from gene expression profiles. However, one of the observations of these studies indicates that there may be links between the dominant tumor morphology characteristics and the molecular subtypes. Benefiting from a large collection of CRC samples, comprising both gene expression and histopathology images, we investigated the possibility of building image-based classifiers able to predict the molecular subtypes. We employed deep convolutional neural networks for extracting local descriptors which were then used for constructing a dictionary-based representation of each tumor sample. A set of support vector machine classifiers were trained to solve different binary decision problems, their combined outputs being used to predict one of the five molecular subtypes. RESULTS: A hierarchical decomposition of the multi-class problem was obtained with an overall accuracy of 0.84 (95%CI=0.79-0.88). The predictions from the image-based classifier showed significant prognostic value similar to their molecular counterparts. CONTACT: popovici@iba.muni.cz. AVAILABILITY AND IMPLEMENTATION: Source code used for the image analysis is freely available from https://github.com/higex/qpath . SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

• MeSH
• kolorektální nádory diagnóza   genetika   metabolismus   patologie   MeSH
• lidé MeSH
• nádorové biomarkery * MeSH
• neuronové sítě * MeSH
• počítačové zpracování obrazu metody   MeSH
• prognóza MeSH
• regulace genové exprese u nádorů MeSH
• support vector machine MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• nádorové biomarkery * MeSH

The performance of a pharmaceutical formulation, such as the drug (API) release rate, is significantly influenced by the properties of the materials used, the composition of the final product and the tablet compression process parameters. However, in some cases, the knowledge of these input parameters does not necessarily provide a reliable description or prediction of tablet performance. Therefore, the knowledge of tablet microstructure is desirable to understand such formulations. Commonly used analytical techniques, such as X-ray tomography and intrusion mercury porosimetry, are not widely used in pharmaceutical companies due to their price and/or toxicity, and therefore, efforts are made to develop a tool for fast and easy microstructure description. In this work, we have developed an image-based method for microstructure description and applied it to a model system consisting of ibuprofen and CaHPO4∙2H2O (API and excipient with different deformability). The obtained parameter, the quadratic mean of the equivalent diameter of the non-deformable, brittle excipient CaHPO4∙2H2O, was correlated with tablet composition, compression pressure and API release rate. The obtained results demonstrate the possibility of describing the tablet dissolution performance in the presented model system based on the microstructural parameter, providing a possible model system for compressed solid dosage forms in which a plastic component is present and specific API release is required.

• Klíčová slova
• SEM image analysis, USP4 dissolution, deformability, flow-through dissolution, tablet microstructure,
• MeSH
• biologické modely * MeSH
• ibuprofen chemie   MeSH
• pomocné látky * chemie   MeSH
• příprava léků MeSH
• tablety chemie   MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• ibuprofen MeSH
• pomocné látky * MeSH
• tablety MeSH

A key requirement for precision medicine is the accurate identification of patients that would respond to a specific treatment or those that represent a high-risk group, and a plethora of molecular biomarkers have been proposed for this purpose during the last decade. Their application in clinical settings, however, is not always straightforward due to relatively high costs of some tests, limited availability of the biological material and time, and procedural constraints. Hence, there is an increasing interest in constructing tissue-based surrogate biomarkers that could be applied with minimal overhead directly to histopathology images and which could be used for guiding the selection of eventual further molecular tests. In the context of colorectal cancer, we present a method for constructing a surrogate biomarker that is able to predict with high accuracy whether a sample belongs to the "BRAF-positive" group, a high-risk group comprising V600E BRAF mutants and BRAF-mutant-like tumors. Our model is trained to mimic the predictions of a 64-gene signature, the current definition of BRAF-positive group, thus effectively identifying histopathology image features that can be linked to a molecular score. Since the only required input is the routine histopathology image, the model can easily be integrated in the diagnostic workflow.

• MeSH
• individualizovaná medicína metody   MeSH
• kolorektální nádory diagnóza   genetika   metabolismus   MeSH
• lidé MeSH
• mutace genetika   MeSH
• nádorové biomarkery genetika   metabolismus   MeSH
• protoonkogenní proteiny B-Raf genetika   metabolismus   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• BRAF protein, human MeSH Prohlížeč
• nádorové biomarkery MeSH
• protoonkogenní proteiny B-Raf MeSH

Rapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound breast images may impair image quality and impact observation and diagnosis. It is crucial to remove localized noise from images. In the article, we have used the hybrid deep learning technique to remove local speckle noise from breast ultrasound images. The contrast of ultrasound breast images was first improved using logarithmic and exponential transforms, and then guided filter algorithms were used to enhance the details of the glandular ultrasound breast images. In order to finish the pre-processing of ultrasound breast images and enhance image clarity, spatial high-pass filtering algorithms were used to remove the extreme sharpening. In order to remove local speckle noise without sacrificing the image edges, edge-sensitive terms were eventually added to the Logical-Pool Recurrent Neural Network (LPRNN). The mean square error and false recognition rate both fell below 1.1% at the hundredth training iteration, showing that the LPRNN had been properly trained. Ultrasound images that have had local speckle noise destroyed had signal-to-noise ratios (SNRs) greater than 65 dB, peak SNR ratios larger than 70 dB, edge preservation index values greater than the experimental threshold of 0.48, and quick destruction times. The time required to destroy local speckle noise is low, edge information is preserved, and image features are brought into sharp focus.

• Klíčová slova
• glandular ultrasound image, hybrid deep learning technique, local speckle noise destruction, logical-pool recurrent neural network, signal-to-noise ratio, spatial high-pass filter,
• MeSH
• algoritmy MeSH
• deep learning * MeSH
• lidé MeSH
• neuronové sítě MeSH
• poměr signál - šum MeSH
• ultrasonografie prsů MeSH
• ultrasonografie metody   MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

The main aim of this study was to develop a new objective method for evaluating the impacts of different diets on the live fish skin using image-based features. In total, one-hundred and sixty rainbow trout (Oncorhynchus mykiss) were fed either a fish-meal based diet (80 fish) or a 100% plant-based diet (80 fish) and photographed using consumer-grade digital camera. Twenty-three colour features and four texture features were extracted. Four different classification methods were used to evaluate fish diets including Random forest (RF), Support vector machine (SVM), Logistic regression (LR) and k-Nearest neighbours (k-NN). The SVM with radial based kernel provided the best classifier with correct classification rate (CCR) of 82% and Kappa coefficient of 0.65. Although the both LR and RF methods were less accurate than SVM, they achieved good classification with CCR 75% and 70% respectively. The k-NN was the least accurate (40%) classification model. Overall, it can be concluded that consumer-grade digital cameras could be employed as the fast, accurate and non-invasive sensor for classifying rainbow trout based on their diets. Furthermore, these was a close association between image-based features and fish diet received during cultivation. These procedures can be used as non-invasive, accurate and precise approaches for monitoring fish status during the cultivation by evaluating diet's effects on fish skin.

• Klíčová slova
• image colour properties, image processing, image texture properties, machine vision system, supervised classification,
• MeSH
• dieta MeSH
• logistické modely MeSH
• Oncorhynchus mykiss MeSH
• support vector machine * MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• srovnávací studie MeSH