Quick Creation of Dual purpose Self-Assembling Peptides regarding Incorporation as well as Visual images inside Hydrogel Biomaterials.

Accurate detection of macro and microvesicles in rat models of fatty liver disease is crucial in evaluating the progression of liver disease and identifying potential hepatotoxic findings during drug development. In this paper, we present a deep-learning-based framework for the segmentation of vacuoles in liver images of Wistar rat and study the correlation of automated quantification with expert pathologist’s manual evaluation. To address the issue of misclassification of lumina (vascular and bile duct) as large vacuoles, we propose a selective tiling technique to generate tiles that include complete lumina and large vacuoles. A binary encoder-decoder convolution neural network is trained to detect individual vacuoles. We report a sensitivity of 85% and specificity of 98%. Furthermore, the diameter and roundness of the segmented vacuoles are estimated with an error of less than 8%, which supports the high potential of our method in drug development process.A recursive additive complement network (RacNet) is introduced to segment cell membranes in histological images as closed lines. Segmenting cell membranes as closed lines is necessary to calculate cell areas and to estimate N/C ratio, which is useful to diagnose early hepatocellular carcinoma. The RacNet is composed of a complement network and an element-wise maximization (EWM) process and is recursively applied to the network output. The complement network complements the lacking parts of cell membranes. The network, however, has a tendency to mistakenly delete some parts of the segmented cell membranes. The EWM process eliminates this unwanted effect.Experiments carried out using unstained hepatic sections showed that the accuracy for segmenting cell membranes as closed lines was significantly improved by using the RacNet.Three imaging methods, bright-field, dark-field, and phase-contrast, were used, as unstained sections show very low contrast in the bright-field imaging commonly used in pathological diagnosis. These imaging methods are available in optical microscopes used by pathologists. Among the three methods, phase-contrast imaging showed the highest accuracy.This study reports on the development of a high-resolution 4K multispectral camera designed to enhance telepathology support systems for remote gross-pathological diagnosis. We experimentally examine and evaluate the camera’s effectiveness in three subjects the reconstruction of precise color images, the emphasis of cancerous tissue areas, and pre-fixed image reproduction from fixed images. The evaluation results of the first and second subjects showed that the camera and supporting methods could be effectively used in gross pathology diagnosis. The images obtained in the third subject received positive evaluations from some pathologists, but others expressed reservations as to its utility.Survival analysis is a valid solution for cancer treatments and outcome evaluations. Due to the wide application of medical imaging and genome technology, computer-aided survival analysis has become a popular and promising area, from which we can get relatively satisfactory results. Although there are already some impressive technologies in this field, most of them make some recommendations using single-source medical data and have not combined multi-level and multi-source data efficiently. Oxaliplatin cost In this paper, we propose a novel pathological images and gene expression data fusion framework to perform the survival prediction. Different from previous methods, our framework can extract correlated multi-scale deep features from whole slide images (WSIs) and dimensionality reduced gene expression data respectively for jointly survival analysis. The experiment results demonstrate that the integrated multi-level image and genome features can achieve higher prediction accuracy compared with single-source features.Gleason scoring for prostate cancer grading is a subjective examination and suffers from suboptimal interobserver and intraobserver variability. To overcome these limitations, we have developed an automated system to grade prostate biopsies. We present a novel deep learning architecture Carcino-Net, which improves semantic segmentation performance. The proposed network is a modified FCN8s with ResNet50 backbone. Using Carcino-Net, we not only report best performance in separating the different grades, we also offer greater accuracy over other state-of-the-art frameworks. The proposed system could expedite the pathology workflow in diagnostic laboratories by triaging high-grade biopsies.Clinical relevance- Carcinoma of the prostate is the second most common cancer diagnosed in men, with approximately one in nine men diagnosed in their lifetime. Oxaliplatin cost The tumor staging via Gleason score is the most powerful prognostic predictor for prostate cancer patients.In this paper, we present a framework to address the augmentation of images for the rare and minor appearance of mitotic type staining patterns, for Human Epithelium Type2 (HEp-2) cell images. The identification of mitotic patterns among non-mitotic/interphase patterns is important in the process of diagnosis of various autoimmune disorders. This task leads to a pattern classification problem between mitotic v/s interphase. However, among the two classes, typically, the number of mitotic cells are relatively very less. Thus, in this work, we propose to generate synthetic mitotic samples, which can be used to augment the number of mitotic samples and balance the samples of mitotic and interphase patterns in classification paradigm. An effective feature representation is used, to validate the usefulness of the synthetic samples in classification task, along with a subjective validation done by a medical expert. The results demonstrate that the approach of generating and mingling synthetic samples with existing training data works well and yields good performance, with 0.98 balanced class accuracy (BcA) in one case, over a public dataset, i.e., UQ-SNP I3A Task-3 mitotic cell identification dataset.Classification of normal lung tissue, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by pathological images is significant for clinical diagnosis and treatment. Due to the large scale of pathological images and the absence of definitive morphological features between LUAD and LUSC, it is time-consuming, laborious and challenging for pathologists to analyze the microscopic histopathology slides by visual observation. In this paper, a pixel-level annotation-free framework was proposed to classify normal tissue, LUAD and LUSC slides. This framework can be divided into two stages tumor classification and localization, and subtype classification. In the first stage, EM-CNN was utilized to distinguish tumor slides from normal tissue slides and locate the discriminative regions for subsequent analysis with only image-level labels provided. In the second stage, a multi-scale network was proposed to improve the accuracy of subtype classification. This method achieved an AUC of 0.9978 for tumor classification and an AUC of 0.