Segmentation of Variants of Nuclei on Whole Slide Images by Using Radiomic Features.

The histopathological segmentation of nuclear types is a challenging task because nuclei exhibit distinct morphologies, textures, and staining characteristics. Accurate segmentation is critical because it affects the diagnostic workflow for patient assessment. In this study, a framework was proposed for segmenting various types of nuclei from different organs of the body. The proposed framework improved the segmentation performance for each nuclear type using radiomics. First, we used distinct radiomic features to extract and analyze quantitative information about each type of nucleus and subsequently trained various classifiers based on the best input sub-features of each radiomic feature selected by a LASSO operator. Second, we inputted the outputs of the best classifier to various segmentation models to learn the variants of nuclei. Using the MoNuSAC2020 dataset, we achieved state-of-the-art segmentation performance for each category of nuclei type despite the complexity, overlapping, and obscure regions. The generalized adaptability of the proposed framework was verified by the consistent performance obtained in whole slide images of different organs of the body and radiomic features.

Detection of brain regions responsible for chronic pain in osteoarthritis: an fMRI-based neuroimaging study using deep learning.

Introduction: Chronic pain is a multifaceted condition that has yet to be fully comprehended. It is frequently linked with a range of disorders, particularly osteoarthritis (OA), which arises from the progressive deterioration of the protective cartilage that cushions the bone endings over time. Methods: In this paper, we examine the impact of chronic pain on the brain using advanced deep learning (DL) algorithms that leverage resting-state functional magnetic resonance imaging (fMRI) data from both OA pain patients and healthy controls. Our study encompasses fMRI data from 51 pain patients and 20 healthy subjects. To differentiate chronic pain-affected OA patients from healthy controls, we introduce a DL-based computer-aided diagnosis framework that incorporates Multi-Layer Perceptron and Convolutional Neural Networks (CNN), separately. Results: Among the examined algorithms, we discovered that CNN outperformed the others and achieved a notable accuracy rate of nearly 85%. In addition, our investigation scrutinized the brain regions affected by chronic pain and successfully identified several regions that have not been mentioned in previous literature, including the occipital lobe, the superior frontal gyrus, the cuneus, the middle occipital gyrus, and the culmen. Discussion: This pioneering study explores the applicability of DL algorithms in pinpointing the differentiating brain regions in OA patients who experience chronic pain. The outcomes of our research could make a significant contribution to medical research on OA pain patients and facilitate fMRI-based pain recognition, ultimately leading to enhanced clinical intervention for chronic pain patients.

Unsupervised Learning Based on Multiple Descriptors for WSIs Diagnosis.

An automatic pathological diagnosis is a challenging task because histopathological images with different cellular heterogeneity representations are sometimes limited. To overcome this, we investigated how the holistic and local appearance features with limited information can be fused to enhance the analysis performance. We propose an unsupervised deep learning model for whole-slide image diagnosis, which uses stacked autoencoders simultaneously feeding multiple-image descriptors such as the histogram of oriented gradients and local binary patterns along with the original image to fuse the heterogeneous features. The pre-trained latent vectors are extracted from each autoencoder, and these fused feature representations are utilized for classification. We observed that training with additional descriptors helps the model to overcome the limitations of multiple variants and the intricate cellular structure of histopathology data by various experiments. Our model outperforms existing state-of-the-art approaches by achieving the highest accuracies of 87.2 for ICIAR2018, 94.6 for Dartmouth, and other significant metrics for public benchmark datasets. Our model does not rely on a specific set of pre-trained features based on classifiers to achieve high performance. Unsupervised spaces are learned from the number of independent multiple descriptors and can be used with different variants of classifiers to classify cancer diseases from whole-slide images. Furthermore, we found that the proposed model classifies the types of breast and lung cancer similar to the viewpoint of pathologists by visualization. We also designed our whole-slide image processing toolbox to extract and process the patches from whole-slide images.

Enhanced solar-light-driven photocatalytic properties of novel Z-scheme binary BiPO4 nanorods anchored onto NiFe2O4 nanoplates: Efficient removal of toxic organic pollutants.

Global environmental problems have been increasing with the growth of the world economy and have become a crucial issue. To replace fossil fuels, sustainable and eco-friendly catalysts are required for the removal of organic pollutants. In this study, nickel ferrite (NiFe2O4) was prepared using a simple wet-chemical synthesis, followed by calcination; bismuth phosphate (BiPO4) was also prepared using a hydrothermal method. Further, NiFe2O4/BiPO4 nanocomposites were prepared using a hydrothermal technique. Numerous characterization studies, such as structural, morphology, surface area, optical, photoluminescence, and photoelectrochemical investigations, were used to analyze NiFe2O4/BiPO4 nanocomposites. The morphology analysis indicated a successful decoration of BiPO4 nanorods on the surface of NiFe2O4 nanoplate. Further, the bandgap of the NiFe2O4/BiPO4 nanocomposites was modified owing to the formation of a heterostructure. The as-prepared NiFe2O4/BiPO4 nanocomposite exhibited promising properties to be used as a novel heterostructure for tetracycline (TC) and Rhodamine B (RhB) removal. The NiFe2O4/BiPO4 nanocomposite degrades TC (98%) and RhB (99%) pollutants upon solar-light irradiation within 100 and 60 min, respectively. Moreover, the trapping experiments confirmed the Z-scheme approach of the prepared nanocomposites. The efficient separation and transfer of photogenerated electron-hole pairs rendered by the heterostructure were confirmed by utilizing electrochemical impedance spectroscopy, photocurrent experiments, and photoluminescence. Mott-Schottky measurements were used determine the positions of the conduction and valence bands of the samples, and the detailed mechanism of photocatalytic degradation of toxic pollutants was projected and discussed.

Vanadium-doped graphitic carbon nitride for multifunctional applications: Photoelectrochemical water splitting and antibacterial activities.

Bulk graphitic carbon nitride (g-C3N4) exhibits limited water splitting efficiency due todrawbacks including high charge recombination rate, low electrical conductivity, poor quantum efficiency, and few adsorption and active catalytic sites. Herein, we report V-doped g-C3N4 nanoarchitectures prepared via direct calcination of urea and ammonium metavanadate. The obtained V-doped g-C3N4 nanostructures not only improved the visible light absorption property but also increased the charge separation and transportation, resulting in extremely enhanced water splitting activity. The structural, morphological, and optical analysis results confirmed the successful incorporation of V into the host g-C3N4 material, and electrochemical impedance spectroscopy measurements revealed the charge carrier dynamics. Compared to the pristine g-C3N4 photoelectrode, the optimized 0.3 mol% V-doped g-C3N4 photoelectrode showed a considerably higher photocurrent density (0.80 mA cm-2). The enhancement of the catalytic performance could be attributed to the synergistic effects of prolonged light absorption, improved transfer of electrons and holes, and extra active catalytic sites for water splitting. Further, the optimized 0.3 mol% V-doped g-C3N4 sample showed an antibacterial activity higher than that of the undoped photocatalyst.

Histopathological Classification of Breast Cancer Images Using a Multi-Scale Input and Multi-Feature Network.

Diagnosis of pathologies using histopathological images can be time-consuming when many images with different magnification levels need to be analyzed. State-of-the-art computer vision and machine learning methods can help automate the diagnostic pathology workflow and thus reduce the analysis time. Automated systems can also be more efficient and accurate, and can increase the objectivity of diagnosis by reducing operator variability. We propose a multi-scale input and multi-feature network (MSI-MFNet) model, which can learn the overall structures and texture features of different scale tissues by fusing multi-resolution hierarchical feature maps from the network's dense connectivity structure. The MSI-MFNet predicts the probability of a disease on the patch and image levels. We evaluated the performance of our proposed model on two public benchmark datasets. Furthermore, through ablation studies of the model, we found that multi-scale input and multi-feature maps play an important role in improving the performance of the model. Our proposed model outperformed the existing state-of-the-art models by demonstrating better accuracy, sensitivity, and specificity.

ZnO nanosheets-decorated Bi2WO6 nanolayers as efficient photocatalysts for the removal of toxic environmental pollutants and photoelectrochemical solar water oxidation.

Herein we report the fabrication of novel Bi2WO6/ZnO heterostructured hybrids for organic contaminant degradation from wastewater and photoelectrochemical (PEC) water splitting upon solar illumination. The Bi2WO6/ZnO photocatalysts were synthesized using a simple and eco-friendly hydrothermal process without the support of any surfactants. From the photocatalytic experiments, heterostructured Bi2WO6/ZnO nanohybrid catalysts exhibited considerably better photocatalytic performance for rhodamine B (RhB) degradation under solar illumination. The BWZ-20 nanocomposite demonstrated superior photodegradation of RhB dye up to 99% in about 50 min. Furthermore, BWZ-20 photoelectrode showeda lower charge-transfer resistance than other samples prepared, suggesting its suitability for PEC water splitting. The photocurrent densities of Bi2WO6/ZnO photoelectrodes were evaluated under the solar irradiation. The BWZ-20 photoelectrode exhibited a significant photocurrent density (0.45 × 10-3A/cm2) at +0.3 V vs. Ag/AgCl, which was~1036-times higher than that of pure Bi2WO6, and ~4.8-times greater than the pure ZnO. Such improved photocatalytic and PEC activities are mainly attributed to the formation of an interface between ZnO and Bi2WO6, superior light absorption ability, low charge-transfer resistance, remarkable production of charge carriers, easy migration of charges, and suppression of the recombination of photogenerated charge carriers.

Multivariate discriminant analysis for branching classification of colonic tubular adenoma glands.

BACKGROUND: Many morphologic findings of histology can be translated into mathematically computerized data, and identifying important parameters is primarily pathologists' task as users. Shape-specific parameters based on computational geometry properties of glands can be used in the field of pathology. We evaluated the diagnostic utility of three shape-specific parameters: the chord intersection ratio, convexity ratio, and maximum concave area ratio for branching classification of glands. METHODS: Seven cases of tubular adenoma were studied. After image analysis, segmented neoplastic glands were classified into nonbranching, mild branching, and moderate branching. Using image analysis formulae for the three shape-specific parameters, we compared the values of the parameters with the branching classification results for colonic tubular adenoma. RESULTS: Multivariate discriminant analysis was used to classify the branching groups. Classification accuracies of nonbranching, mild branching, and moderate branching group based on the three shape-specific parameters were 98, 94, and 95%, respectively. More branching growth exhibited a higher chord intersection ratio and maximum concave area ratio but lower convexity ratio. We found a statistically significant difference in chord intersection ratio, maximum concave area ratio, and convexity ratio between mild, moderate, and nonbranching groups. Among the three features, the chord intersection ratio was the most significant parameter. CONCLUSIONS: Shape-based parameters of chord intersection ratio, convexity ratio, and maximum concave area ratio are valid assessment parameters for irregular branching structures. For the understanding of spatial relationships of histology, the holistic geometric approach using shape-based parameters can be useful.

Evaluation of nuclear chromatin using grayscale intensity and thresholded percentage area in liquid-based cervical cytology.

OBJECTIVES: Development of computerized image analysis techniques has opened up the possibility for the quantitative analysis of nuclear chromatin in pathology. We hypothesized that the features extracted from digital images could be used to determine specific cytomorphological findings for nuclear chromatin that may be applicable for establishing a medical diagnosis. METHODS: Three parameters were evaluated from nuclear chromatin images obtained from the liquid-based cervical cytology samples of patients with biopsy-proven high-grade squamous intraepithelial lesion (HGSIL), and compared between non-neoplastic squamous epithelia and dysplastic epithelia groups: (1) standard deviation (SD) of the grayscale intensity; (2) difference between the maximum and minimum grayscale intensity (M-M); and (3) thresholded area percentage. Each parameter was evaluated at the mean, mean-1SD, and mean-2SD thresholding intensity levels. RESULTS: Between the mean and mean-1SD levels, the thresholded nuclear chromatin pattern was most similar to the chromatin granularity of the unthresholded grayscale images. The SD of the gray intensity and the thresholded area percentage differed significantly between the non-neoplastic squamous epithelia and dysplastic epithelia of HGSIL images at all three thresholding intensity levels (mean, mean-1SD, and mean-2SD). However, the M-M significantly differed between the two sample types for only two of the thresholding intensity levels (mean-1SD and mean-2SD). CONCLUSIONS: The digital parameters SD and M-M of the grayscale intensity, along with the thresholded area percentage could be useful in automated cytological evaluations. Further studies are needed to identify more valuable parameters for clinical application.

A method to determine quasi-steady state in constant voltage mode isotachophoresis.

Identification of the steady state is very challenging in isotachophoresis (ITP); especially in complex microgeometries, such as dog-leg channels or cross-channel junctions. In this work, an elastic matching method is applied to determine the quasi-steady state in microscale ITP. In the elastic matching method, the similarity between two profiles is calculated by comparing intensity distribution of two images or profiles. To demonstrate this similarity-based analysis technique for ITP, a constant voltage mode ITP model is developed and applied to a five-component ITP system. Hydrochloric acid and caproic acid are used as the leader and terminator, respectively, while histidine is used as the counter-ion. Two sample components, acetic acid and benzoic acid, are separated under the action of an applied electric field in both straight and dog-leg microchannels. This analysis shows that conductivity profiles provide a better measure to determine the quasi-steady state in an ITP process. For a straight microchannel, the quasi-steady state is achieved in less than a minute with a total potential drop of 100 V in a 2 cm long channel. In a straight channel, a true steady state can be achieved for ITP with appropriate countercurrent flow where stationary zones are formed, but the time it takes to reach the steady state is much longer than the without counter flow case. The numerical results indicate that a steady state cannot be reached in a dog-leg microchannel because of sample dispersion and refocusing at and near the intersections and at the branch channels. However, the elastic matching method can be used to determine the quasi-steady state in a dog-leg microchannel.