Region Detection and Segmentation of Brain Hemorrhage using Algorithmic Approach of Image Processing

Brain is one of the most important part of the body. Brain Hemorrhage is a severe head injury that deteriorates the performance and function of an individual. Brain Hemorrhage can be detected through CT (Computer Tomography) scan of the brain. CT scan uses narrow X-ray beam which rotates around the part of the body and provides a set of images from different angles and the computer creates a cross-sectional view. It is challenging to detect and segment the region of the brain having Hemorrhage. Hence an automated system would be handy at those times. In the proposed work an attempt has been made to segment and identify the hemorrhaged region of the brain in the CT scan slices of the image. Brain hemorrhage segmentation helps to identify the region of brain hemorrhage which in turn helps to treat the patients at an early stage. The region of brain hemorrhage is appropriately identified from the proposed algorithm.

Image-to-Image Translation as a Pretext for Unsupervised Detection of Cancerous Regions in Histology Imagery

Detection of visual anomalies refers to the problem of finding patterns in different imaging data that do not conform to the expected visual appearance, and is a widely studied problem in different domains. Due to the nature of anomaly occurrences and underlying generating processes, it is hard to characterize them and obtain labelled data. Obtaining labelled data is especially difficult in biomedical applications, where only trained domain experts can provide labels, which are often diverse and complex to a large degree. The recently presented approaches for unsupervised detection of visual anomalies omit the need for labelled data and demonstrate promising results in domains where anomalous samples significantly deviate from the normal appearance. Despite promising results, the performance of such approaches still lags behind supervised approaches and does not provide a universal solution. In this work, we present an image-to-image translation-based framework that significantly surpasses the performance of existing unsupervised methods and approaches the performance of supervised methods in a challenging domain of cancerous region detection in histology imagery.

A Design of Multi-Purpose Image-Based QR Code

A multi-purpose image-based QR code is designed in this paper. There are four purposes for the generated image-based QR code. In the first purpose, the basic image-based QR code with the look of a host image is with an ingenious layout to be identified easier. In the second one, a saliency region detection method is adopted for enhancing the quality of the image-based QR code. In the third one, the host image is embedded into the image-based QR code for further access to the host image; Finally, the visual cryptography-based watermarking method is applied to the host image embedded image-based QR code. In the case that the specific users need verification from the image-based QR code, the binary verified image can be retrieved when the public share is available. The experimental results demonstrate that the generated image-based QR code not only looked better than some previous works but also had high quality host image embedded and identification ability.

New record of Gymnoascus udagawae associated with Clathria sp. sponge from Indonesia and the potency as anti-Candida

Marine sponges are associated with marine fungi. The associated fungi produce secondary metabolites for sponge survival in extreme habitats. Despite the important role of the associated fungi on their host, the research on marine fungi however has not been studied well. This study aimed to isolate sponge-associated marine fungi and analyze the potency of fungal secondary metabolites against Candida albicans. The sponge used was Clathria sp. collected from Pramuka Island, Indonesia. Fungal isolation used the direct inoculation method. Fungal identification was done by morphological and molecular characteristics of ITS rDNA region. Detection of anti-Candida used the well diffusion method. The isolate has typical morphological characteristics of the genus Gymnoascus with noduled chlamydospore and arthroconidia. The isolated fungus was identified as Gymnoascus udagawae based on morphological and molecular analysis. This is the first record of marine fungi G. udagawae from Indonesia. The ethyl acetate extract of fungal filtrate showed 1.4 cm inhibition diameter of 500 mg/μL extract. The inhibition is moderate category compared to that of clotrimazole a drug commonly used for candidiasis as the positive control with showed 2.8 cm inhibition diameter at 100 mg/μL. The fungus is a potential source of the secondary metabolite active against C. albicans.

Identification of spatial co-expression patterns and intra-tissue heterogeneity in spatially resolved transcriptomics by region-specific denoising

We are pleased to introduce a first-of-its-kind tool that combines in-silico region detection and missing value estimation for spatially resolved transcriptomics. Spatial transcriptomics by 10X Visium (ST) is a new technology used to dissect gene and cell spatial organization. Analyzing this new type of data has two main challenges: automatically annotating the major tissue regions and excessive zero values of gene expression due to high dropout rates. We developed a computational tool—MIST—that addresses both challenges by automatically identifying tissue regions and estimating missing gene-expression values for each detected region. We validated MIST detected regions across multiple datasets using manual annotation on the histological staining images as references. We also demonstrated that MIST can accurately recover ST’s missing values through hold-out experiments. Furthermore, we showed that MIST could identify intra-tissue heterogeneity and recover spatial gene-gene co-expression signals. We therefore strongly encourage using MIST before downstream ST analysis because it provides unbiased region annotations and enables accurately denoised spatial gene-expression profiles.

CST: A Multitask Learning Framework for Colorectal Cancer Region Mining Based on Transformer

Colorectal cancer is a high death rate cancer until now; from the clinical view, the diagnosis of the tumour region is critical for the doctors. But with data accumulation, this task takes lots of time and labor with large variances between different doctors. With the development of computer vision, detection and segmentation of the colorectal cancer region from CT or MRI image series are a great challenge in the past decades, and there still have great demands on automatic diagnosis. In this paper, we proposed a novel transfer learning protocol, called CST, that is, a union framework for colorectal cancer region detection and segmentation task based on the transformer model, which effectively constructs the cancer region detection and its segmentation jointly. To make a higher detection accuracy, we incorporate an autoencoder-based image-level decision approach that leverages the image-level decision of a cancer slice. We also compared our framework with one-stage and two-stage object detection methods; the results show that our proposed method achieves better results on detection and segmentation tasks. And this proposed framework will give another pathway for colorectal cancer screen by way of artificial intelligence.