Tissue Classification Research Articles

A Workflow for Meaningful Interpretation of Classification Results from Handheld Ambient Mass Spectrometry Analysis Probes.

While untargeted analysis of biological tissues with ambient mass spectrometry analysis probes has been widely reported in the literature, there are currently no guidelines to standardize the workflows for the experimental design, creation, and validation of molecular models that are utilized in these methods to perform class predictions. By drawing parallels with hurdles that are faced in the field of food fraud detection with untargeted mass spectrometry, we provide a stepwise workflow for the creation, refinement, evaluation, and assessment of the robustness of molecular models, aimed at meaningful interpretation of mass spectrometry-based tissue classification results. We propose strategies to obtain a sufficient number of samples for the creation of molecular models and discuss the potential overfitting of data, emphasizing both the need for model validation using an independent cohort of test samples, as well as the use of a fully characterized feature-based approach that verifies the biological relevance of the features that are used to avoid false discoveries. We additionally highlight the need to treat molecular models as "dynamic" and "living" entities and to further refine them as new knowledge concerning disease pathways and classifier feature noise becomes apparent in large(r) population studies. Where appropriate, we have provided a discussion of the challenges that we faced in our development of a 10 s cancer classification method using picosecond infrared laser mass spectrometry (PIRL-MS) to facilitate clinical decision-making at the bedside.

Brain tumor grading diagnosis using transfer learning based on optical coherence tomography.

In neurosurgery, accurately identifying brain tumor tissue is vital for reducing recurrence. Current imaging techniques have limitations, prompting the exploration of alternative methods. This study validated a binary hierarchical classification of brain tissues: normal tissue, primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and low-grade glioma (LGG) using transfer learning. Tumor specimens were measured with optical coherence tomography (OCT), and a MobileNetV2 pre-trained model was employed for classification. Surgeons could optimize predictions based on experience. The model showed robust classification and promising clinical value. A dynamic t-SNE visualized its performance, offering a new approach to neurosurgical decision-making regarding brain tumors.

A dual-camera hyperspectral laparoscopic imaging system.

Minimally invasive surgery (MIS) has expanded broadly in the field of abdominal and pelvic surgery. However, there are still prevalent issues surrounding intracorporeal surgery, such as iatrogenic injury, anastomotic leakage, or the presence of positive tumor margins after resection. Current approaches to address these issues and advance laparoscopic imaging techniques often involve fluorescence imaging agents, such as indocyanine green (ICG), to improve visualization, but these have drawbacks. Hyperspectral imaging (HSI) is an emerging optical imaging modality that takes advantage of spectral characteristics of different tissues. Various applications include tissue classification and digital pathology. In this study, we developed a dual-camera system for high-speed hyperspectral imaging. This includes the development of a custom application interface and corresponding hardware setup. Characterization of the system was performed, including spectral accuracy and spatial resolution, showing little sacrifice in speed for the approximate doubling of the covered spectral range, with our system acquiring 29 spectral images from 460-850 nm. Reference color tiles with various reflectance profiles were imaged and a RMSE of 3.56 ± 1.36% was achieved. Sub-millimeter resolution was shown at 7 cm working distance for both hyperspectral cameras. Finally, we image ex vivo tissues, including porcine stomach, liver, intestine, and kidney with our system and use a high-resolution, radiometrically calibrated spectrometer for comparison and evaluation of spectral fidelity. The dual-camera hyperspectral laparoscopic imaging system can have immediate applications in various surgeries.

A novel visible and near-infrared hyperspectral imaging platform for automated breast-cancer detection

Background: Breast cancer is a leading cause of cancer-related deaths among women worldwide. Early and accurate detection is crucial for improving patient outcomes. Our study utilizes Visible and Near-Infrared Hyperspectral Imaging (VIS-NIR HSI), a promising non-invasive technique, to detect cancerous regions in ex-vivo breast specimens based on their hyperspectral response.Methods: In this paper, we present a novel HSI platform integrated with fuzzy c-means clustering for automated breast cancer detection. We acquire hyperspectral data from breast tissue samples, and preprocess it to reduce noise and enhance hyperspectral features. Fuzzy c-means clustering is then applied to segment cancerous regions based on their spectral characteristics.Results: Our approach demonstrates promising results. We evaluated the quality of the clustering using metrics like Silhouette Index (SI), Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The clustering metrics results revealed an optimal number of 6 clusters for breast tissue classification, and the SI values ranged from 0.68 to 0.72, indicating well-separated clusters. Moreover, the CHI values showed that the clusters were well-defined, and the DBI values demonstrated low cluster dispersion. Additionally, the sensitivity, specificity, and accuracy of our system were evaluated on a dataset of breast tissue samples. We achieved an average sensitivity of 96.83%, specificity of 93.39%, and accuracy of 95.12%. These results indicate the effectiveness of our HSI-based approach in distinguishing cancerous and non-cancerous regions.Conclusions: The paper introduces a robust hyperspectral imaging platform coupled with fuzzy c-means clustering for automated breast cancer detection. The clustering metrics results support the reliability of our approach in effectively segmenting breast tissue samples. In addition, the system shows high sensitivity and specificity, making it a valuable tool for early-stage breast cancer diagnosis. This innovative approach holds great potential for improving breast cancer screening and, thereby, enhancing our understanding of the disease and its detection patterns.

Brain MR image simulation for deep learning based medical image analysis networks

Background and ObjectiveAs large sets of annotated MRI data are needed for training and validating deep learning based medical image analysis algorithms, the lack of sufficient annotated data is a critical problem. A possible solution is the generation of artificial data by means of physics-based simulations. Existing brain simulation data is limited in terms of anatomical models, tissue classes, fixed tissue characteristics, MR sequences and overall realism. MethodsWe propose a realistic simulation framework by incorporating patient-specific phantoms and Bloch equations-based analytical solutions for fast and accurate MRI simulations. A large number of labels are derived from open-source high-resolution T1w MRI data using a fully automated brain classification tool. The brain labels are taken as ground truth (GT) on which MR images are simulated using our framework. Moreover, we demonstrate that the T1w MR images generated from our framework along with GT annotations can be utilized directly to train a 3D brain segmentation network. To evaluate our model further on larger set of real multi-source MRI data without GT, we compared our model to existing brain segmentation tools, FSL-FAST and SynthSeg. ResultsOur framework generates 3D brain MRI for variable anatomy, sequence, contrast, SNR and resolution. The brain segmentation network for WM/GM/CSF trained only on T1w simulated data shows promising results on real MRI data from MRBrainS18 challenge dataset with a Dice scores of 0.818/0.832/0.828. On OASIS data, our model exhibits a close performance to FSL, both qualitatively and quantitatively with a Dice scores of 0.901/0.939/0.937. ConclusionsOur proposed simulation framework is the initial step towards achieving truly physics-based MRI image generation, providing flexibility to generate large sets of variable MRI data for desired anatomy, sequence, contrast, SNR, and resolution. Furthermore, the generated images can effectively train 3D brain segmentation networks, mitigating the reliance on real 3D annotated data.

A metabolic map and artificial intelligence-aided identification of nasopharyngeal carcinoma via a single-cell Raman platform.

Nasopharyngeal carcinoma (NPC) is a complex cancer influenced by various factors. This study explores the use of single-cell Raman spectroscopy as a potential diagnostic tool for investigating biomolecular changes associated with NPC carcinogenesis. Seven NPC cell lines, one immortalised nasopharyngeal epithelial cell line, six nasopharyngeal mucosa tissues and seven NPC tissue samples were analysed by performing confocal Raman spectroscopic measurements and imaging. The single-cell Raman spectral dataset was used to quantify relevant biomolecules and build machine learning classification models. Metabolomic profiles were investigated using ultra-performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS). By generating a metabolic map of seven NPC cell lines, we identified an interplay of altered metabolic processes involving nucleic acids, amino acids, lipids and sugars. The results from spatially resolved Raman maps and UPLC-MS/MS metabolomics were consistent, revealing an increase of unsaturated fatty acids in cancer cells, particularly in highly metastatic 5-8F and poorly differentiated CNE2 cells. The classification model achieved a nearly perfect classification when identifying NPC and non-NPC cells with an ROC-AUC of 0.99 and a value of 0.97 when identifying 13 tissue samples. This study unveils a complex interplay of metabolic network and highlights the potential roles of unsaturated fatty acids in NPC progression and metastasis. This renders further research to provide deeper insights into NPC pathogenesis, identify new metabolic targets and improve the efficacy of targeted therapies in NPC. Artificial intelligence-aided analysis of single-cell Raman spectra has achieved high accuracies in the classification of both cancer cells and patient tissues, paving the way for a simple, less invasive and accurate diagnostic test.

Towards a general-purpose foundation model for computational pathology.

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.

SpaceANOVA: Spatial Co-occurrence Analysis of Cell Types in Multiplex Imaging Data Using Point Process and Functional ANOVA.

Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or the tumor microenvironment. Exploring the potential variations in the spatial co-occurrence or colocalization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process and functional analysis of variance. Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered due to data-collection complexities. We demonstrate the superior statistical power and robustness of the method in comparison with existing approaches through realistic simulation studies. Furthermore, we apply the method to three real data sets on different diseases collected using different imaging platforms. In particular, one of these data sets reveals novel insights into the spatial characteristics of various types of colorectal adenoma.

Endoscopic Hyperspectral Imaging System to Discriminate Tissue Characteristics in Tissue Phantom and Orthotopic Mouse Pancreatic Tumor Model.

In this study, we developed an endoscopic hyperspectral imaging (eHSI) system and evaluated its performance in analyzing tissues within tissue phantoms and orthotopic mouse pancreatic tumor models. Our custom-built eHSI system incorporated a liquid crystal tunable filter. To assess its tissue discrimination capabilities, we acquired images of tissue phantoms, distinguishing between fat and muscle regions. The system underwent supervised training using labeled samples, and this classification model was then applied to other tissue phantom images for evaluation. In the tissue phantom experiment, the eHSI effectively differentiated muscle from fat and background tissues. The precision scores regarding fat tissue classification were 98.3% for the support vector machine, 97.7% for the neural network, and 96.0% with a light gradient-boosting machine algorithm, respectively. Furthermore, we applied the eHSI system to identify tumors within an orthotopic mouse pancreatic tumor model. The F-score of each pancreatic tumor-bearing model reached 73.1% for the KPC tumor model and 63.1% for the Pan02 tumor models. The refined imaging conditions and optimization of the fine-tuning of classification algorithms enhance the versatility and diagnostic efficacy of eHSI in biomedical applications.

Real-Time Tissue Classification Using a Novel Optical Needle Probe for Biopsy.

Core needle biopsy is a part of the histopathological process, which is required for cancerous tissue examination. The most common method to guide the needle inside of the body is ultrasound screening, which in greater part is also the only guidance method. Ultrasound screening requires user experience. Furthermore, patient involuntary movements such as breathing might introduce artifacts and blur the screen. Optically enhanced core needle biopsy probe could potentially aid interventional radiologists during this procedure, providing real-time information on tissue properties close to the needle tip, while it is advancing inside of the body. In this study, we used diffuse optical spectroscopy in a custom-made core needle probe for real-time tissue classification. Our aim was to provide initial characteristics of the smart needle probe in the differentiation of tissues and validate the basic purpose of the probe of informing about breaking into a desired organ. We collected optical spectra from rat blood, fat, heart, kidney, liver, lungs, and muscle tissues. Gathered data were analyzed for feature extraction and evaluation of two machine learning-based classifiers: support vector machine and k-nearest neighbors. Their performances on training data were compared using subject-independent k-fold cross-validation. The best classifier model was chosen and its feasibility for real-time automated tissue recognition and classification was then evaluated. The final model reached nearly 80% of correct real-time classification of rat organs when using the needle probe during real-time classification.

Are Artificial Intelligence-Assisted Three-Dimensional Histological Reconstructions Reliable for the Assessment of Trabecular Microarchitecture?

Objectives: This study aimed to create a three-dimensional histological reconstruction through the AI-assisted classification of tissues and the alignment of serial sections. The secondary aim was to evaluate if the novel technique for histological reconstruction accurately replicated the trabecular microarchitecture of bone. This was performed by conducting micromorphometric measurements on the reconstruction and comparing the results obtained with those of microCT reconstructions. Methods: A bone biopsy sample was harvested upon re-entry following sinus floor augmentation. Following microCT scanning and histological processing, a modified version of the U-Net architecture was trained to categorize tissues on the sections. Detector-free local feature matching with transformers was used to create the histological reconstruction. The micromorphometric parameters were calculated using Bruker's CTAn software (version 1.18.8.0, Bruker, Kontich, Belgium) for both histological and microCT datasets. Results: Correlation coefficients calculated between the micromorphometric parameters measured on the microCT and histological reconstruction suggest a strong linear relationship between the two with p-values of 0.777, 0.717, 0.705, 0.666, and 0.687 for BV/TV, BS/TV, Tb.Pf Tb.Th, and Tb.Sp, respectively. Bland-Altman and mountain plots suggest good agreement between BV/TV measurements on the two reconstruction methods. Conclusions: This novel method for three-dimensional histological reconstruction provides researchers with a tool that enables the assessment of accurate trabecular microarchitecture and histological information simultaneously.

Effects of various cross-linked collagen scaffolds on wound healing in rats model by deep-learning CNN

Scar tissue is connective tissue formed on the wound during the wound-healing process. The most significant distinction between scar tissue and normal tissue is the appearance of covalent cross-linking and the amount of collagen fibers in the tissue. This study investigates the efficacy of four types of collagen scaffolds in promoting wound healing and regeneration in a Sprague-Dawley murine model—the histomorphology analysis of collagen scaffolds and developing a deep learning model for accurate tissue classification. Four female rats (n = 24) groups received collagen scaffolds prepared through physical and chemical crosslinking. Wound healing progress was evaluated by monitoring granulation tissue formation, collagen matrix organization, and collagen fiber deposition, with histological scoring for quantification—the EDC and HA groups demonstrated enhanced tissue regeneration. The EDC and HA groups observed significant differences in wound regeneration outcomes. Deep-learning CNN models with data augmentation techniques were used for image analysis to enhance objectivity. The CNN architecture featured pre-trained VGG16 layers and global average pooling (GAP) layers. Feature visualization using Grad-CAM heatmaps provided insights into the neural network’s focus on specific wound features. The model’s AUC score of 0.982 attests to its precision. In summary, collagen scaffolds can promote wound healing in mice, and the deep learning image analysis method we proposed may be a new method for wound healing assessment.

Optical Emission Spectroscopy for the Real-Time Identification of Malignant Breast Tissue

Breast conserving resection with free margins is the gold standard treatment for early breast cancer recommended by guidelines worldwide. Therefore, reliable discrimination between normal and malignant tissue at the resection margins is essential. In this study, normal and abnormal tissue samples from breast cancer patients were characterized ex vivo by optical emission spectroscopy (OES) based on ionized atoms and molecules generated during electrosurgical treatment. The aim of the study was to determine spectroscopic features which are typical for healthy and neoplastic breast tissue allowing for future real-time tissue differentiation and margin assessment during breast cancer surgery. A total of 972 spectra generated by electrosurgical sparking on normal and abnormal tissue were used for support vector classifier (SVC) training. Specific spectroscopic features were selected for the classification of tissues in the included breast cancer patients. The average classification accuracy for all patients was 96.9%. Normal and abnormal breast tissue could be differentiated with a mean sensitivity of 94.8%, a specificity of 99.0%, a positive predictive value (PPV) of 99.1% and a negative predictive value (NPV) of 96.1%. For 66.6% patients all classifications reached 100%. Based on this convincing data, a future clinical application of OES-based tissue differentiation in breast cancer surgery seems to be feasible.

Learning From Incorrectness: Active Learning With Negative Pre-Training and Curriculum Querying for Histological Tissue Classification.

Patch-level histological tissue classification is an effective pre-processing method for histological slide analysis. However, the classification of tissue with deep learning requires expensive annotation costs. To alleviate the limitations of annotation budgets, the application of active learning (AL) to histological tissue classification is a promising solution. Nevertheless, there is a large imbalance in performance between categories during application, and the tissue corresponding to the categories with relatively insufficient performance are equally important for cancer diagnosis. In this paper, we propose an active learning framework called ICAL, which contains Incorrectness Negative Pre-training (INP) and Category-wise Curriculum Querying (CCQ) to address the above problem from the perspective of category-to-category and from the perspective of categories themselves, respectively. In particular, INP incorporates the unique mechanism of active learning to treat the incorrect prediction results that obtained from CCQ as complementary labels for negative pre-training, in order to better distinguish similar categories during the training process. CCQ adjusts the query weights based on the learning status on each category by the model trained by INP, and utilizes uncertainty to evaluate and compensate for query bias caused by inadequate category performance. Experimental results on two histological tissue classification datasets demonstrate that ICAL achieves performance approaching that of fully supervised learning with less than 16% of the labeled data. In comparison to the state-of-the-art active learning algorithms, ICAL achieved better and more balanced performance in all categories and maintained robustness with extremely low annotation budgets. The source code will be released at https://github.com/LactorHwt/ICAL.

Partitioning intensity inhomogeneity colour images via Saliency-based active contour

Partitioning or segmenting intensity inhomogeneity colour images is a challenging problem in computer vision and image shape analysis. Given an input image, the active contour model (ACM) which is formulated in variational framework is regularly used to partition objects in the image. A selective type of variational ACM approach is better than a global approach for segmenting specific target objects, which is useful for applications such as tumor segmentation or tissue classification in medical imaging. However, the existing selective ACMs yield unsatisfactory outcomes when performing the segmentation for colour (vector-valued) with intensity variations. Therefore, our new approach incorporates both local image fitting and saliency maps into a new variational selective ACM to tackle the problem. The euler-lagrange (EL) equations were presented to solve the proposed model. Thirty combinations of synthetic and medical images were tested. The visual observation and quantitative results show that the proposed model outshines the other existing models by average, with the accuracy of 2.23% more than the compared model and the Dice and Jaccard coefficients which were around 12.78% and 19.53% higher, respectively, than the compared model.

Automated size-specific dose estimates framework in thoracic CT using convolutional neural network based on U-Net model.

This study aimed to develop an automated method that uses a convolutional neural network (CNN) for calculating size-specific dose estimates (SSDEs) based on the corrected effective diameter (Deff corr ) in thoracic computed tomography (CT). Transaxial images obtained from 108 adult patients who underwent non-contrast thoracic CT scans were analyzed. To calculate the Deff corr according to Mihailidis etal., the average relative electron densities for lung, bone, and other tissues were used to correct the lateral and anterior-posterior dimensions. The CNN architecture based on the U-Net algorithm was used for automated segmentation of three classes of tissues and the background region to calculate dimensions and Deff corr values. Then, 108 thoracic CT images and generated segmentation masks were used for network training. The water-equivalent diameter (Dw ) was determined according to the American Association of Physicists in Medicine Task Group 220. Linear regression and Bland-Altman analysis were performed to determine the correlations between SSDEDeff corr(automated) , SSDEDeff corr(manual) , and SSDEDw . High agreement was obtained between the manual and automated methods for calculating the Deff corr SSDE. The mean values for the SSDEDeff corr(manual) , SSDEDw , and SSDEDeff corr(automated) were 14.3±2.1 mGy, 14.6±2.2 mGy, and 14.5±2.4 mGy, respectively. The U-Net model was successfully trained and used to accurately predict SSDEs, with results comparable to manual-labeling results. The proposed automated framework using a CNN offers a reliable and efficient solution for determining the Deff corr SSDE in thoracic CT.

Resolution-enhanced multi-core fiber imaging learned on a digital twin for cancer diagnosis.

Deep learning enables label-free all-optical biopsies and automated tissue classification. Endoscopic systems provide intraoperative diagnostics to deep tissue and speed up treatment without harmful tissue removal. However, conventional multi-core fiber (MCF) endoscopes suffer from low resolution and artifacts, which hinder tumor diagnostics. We introduce a method to enable unpixelated, high-resolution tumor imaging through a given MCF with a diameter of around 0.65mm and arbitrary core arrangement and inhomogeneous transmissivity. Image reconstruction is based on deep learning and the digital twin concept of the single-reference-based simulation with inhomogeneous optical properties of MCF and transfer learning on a small experimental dataset of biological tissue. The reference provided physical information about the MCF during the training processes. For the simulated data, hallucination caused by the MCF inhomogeneity was eliminated, and the averaged peak signal-to-noise ratio and structural similarity were increased from 11.2dB and 0.20 to 23.4dB and 0.74, respectively. By transfer learning, the metrics of independent test images experimentally acquired on glioblastoma tissue ex vivo can reach up to 31.6dB and 0.97 with 14fps computing speed. With the proposed approach, a single reference image was required in the pre-training stage and laborious acquisition of training data was bypassed. Validation on glioblastoma cryosections with transfer learning on only 50 image pairs showed the capability for high-resolution deep tissue retrieval and high clinical feasibility.

Magnetic Resonance Imaging Tissue Signatures Associated With White Matter Changes Due to Sporadic Cerebral Small Vessel Disease Indicate That White Matter Hyperintensities Can Regress.

White matter hyperintensities (WMHs) might regress and progress contemporaneously, but we know little about underlying mechanisms. We examined WMH change and underlying quantitative magnetic resonance imaging tissue measures over 1 year in patients with minor ischemic stroke with sporadic cerebral small vessel disease. We defined areas of stable normal-appearing white matter, stable WMHs, progressing and regressing WMHs based on baseline and 1-year brain magnetic resonance imaging. In these areas we assessed tissue characteristics with quantitative T1, fractional anisotropy (FA), mean diffusivity (MD), and neurite orientation dispersion and density imaging (baseline only). We compared tissue signatures cross-sectionally between areas, and longitudinally within each area. WMH change masks were available for N=197. Participants' mean age was 65.61 years (SD, 11.10), 59% had a lacunar infarct, and 68% were men. FA and MD were available for N=195, quantitative T1 for N=182, and neurite orientation dispersion and density imaging for N=174. Cross-sectionally, all 4 tissue classes differed for FA, MD, T1, and Neurite Density Index. Longitudinally, in regressing WMHs, FA increased with little change in MD and T1 (difference estimate, 0.011 [95% CI, 0.006-0.017]; -0.002 [95% CI, -0.008 to 0.003] and -0.003 [95% CI, -0.009 to 0.004]); in progressing and stable WMHs, FA decreased (-0.022 [95% CI, -0.027 to -0.017] and -0.009 [95% CI, -0.011 to -0.006]), whereas MD and T1 increased (progressing WMHs, 0.057 [95% CI, 0.050-0.063], 0.058 [95% CI, 0.050 -0.066]; stable WMHs, 0.054 [95% CI, 0.045-0.063], 0.049 [95% CI, 0.039-0.058]); and in stable normal-appearing white matter, MD increased (0.004 [95% CI, 0.003-0.005]), whereas FA and T1 slightly decreased and increased (-0.002 [95% CI, -0.004 to -0.000] and 0.005 [95% CI, 0.001-0.009]). Quantitative magnetic resonance imaging shows that WMHs that regress have less abnormal microstructure at baseline than stable WMHs and follow trajectories indicating tissue improvement compared with stable and progressing WMHs.

An efficient method for MRI brain tumor tissue segmentation and classification using an optimized support vector machine

An efficient method for MRI brain tumor tissue segmentation and classification using an optimized support vector machine

Tissue science and histology in the system of biomedical scientific and educational disciplines

In this article, the authors analyze the role and significance of histology in biomedical scientific and educational disciplines (history, present condition, and controversial issues). This study discusses the formation and development of histology—the science of tissues—and the emergence of notions about tissues and tissue classification. When describing the microscopic structure of plant organs in 1671, the English botanist and physician N. Grew (1641–1712) used the term “tissue” for the first time. In the mid-19th century, German histologists R.A. Kölliker (1817–1905) and F. Leydig (1821–1908) established the present scientific classification of tissues. These authors classified tissues into four main groups: epithelium, connective tissue and blood, nervous tissue, and muscle tissue. Russian scientists A.A. Zavarzin and N.G. Khlopin made significant contributions to developing tissue classification and evolution problems. It is worth noting that the term “tissue,” which was initially interpreted as purely morphological, has gained physiological content; that is, the idea of “tissue” has become a morphofunctional concept. The idea of tissue stability during the ontogenesis stages of organisms is one of the significant paradigms of histology. Fabric variability is allowed within certain limits within the tissue group to which the fabric belongs. There is no compelling evidence of tissue transition from one tissue group to another. The first histology departments appeared in European higher educational institutions in the middle of the 19th century and Russian higher educational institutions in the late 1960s. As a scientific discipline, histology has not exhausted its capabilities; therefore, excluding histology from the nomenclature of scientific specialization is incorrect.