36,132 research outputs found

    Nuclei Detection Using Mixture Density Networks

    Full text link
    Nuclei detection is an important task in the histology domain as it is a main step toward further analysis such as cell counting, cell segmentation, study of cell connections, etc. This is a challenging task due to the complex texture of histology image, variation in shape, and touching cells. To tackle these hurdles, many approaches have been proposed in the literature where deep learning methods stand on top in terms of performance. Hence, in this paper, we propose a novel framework for nuclei detection based on Mixture Density Networks (MDNs). These networks are suitable to map a single input to several possible outputs and we utilize this property to detect multiple seeds in a single image patch. A new modified form of a cost function is proposed for training and handling patches with missing nuclei. The probability maps of the nuclei in the individual patches are next combined to generate the final image-wide result. The experimental results show the state-of-the-art performance on complex colorectal adenocarcinoma dataset.Comment: 8 pages, 3 figure

    Design of decorated self-assembling peptide hydrogels as architecture for mesenchymal stem cells

    Get PDF
    Hydrogels from self-assembling ionic complementary peptides have been receiving a lot of interest from the scientific community as mimetic of the extracellular matrix that can offer three-dimensional supports for cell growth or can become vehicles for the delivery of stem cells, drugs or bioactive proteins. In order to develop a 3D "architecture" for mesenchymal stem cells, we propose the introduction in the hydrogel of conjugates obtained by chemoselective ligation between a ionic-complementary self-assembling peptide (called EAK) and three different bioactive molecules: an adhesive sequence with 4 Glycine-Arginine-Glycine-Aspartic Acid-Serine-Proline (GRGDSP) motifs per chain, an adhesive peptide mapped on h-Vitronectin and the growth factor Insulin-like Growth Factor-1 (IGF-1). The mesenchymal stem cell adhesion assays showed a significant increase in adhesion and proliferation for the hydrogels decorated with each of the synthesized conjugates; moreover, such functionalized 3D hydrogels support cell spreading and elongation, validating the use of this class of self-assembly peptides-based material as very promising 3D model scaffolds for cell cultures, at variance of the less realistic 2D ones. Furthermore, small amplitude oscillatory shear tests showed that the presence of IGF-1-conjugate did not alter significantly the viscoelastic properties of the hydrogels even though differences were observed in the nanoscale structure of the scaffolds obtained by changing their composition, ranging from long, well-defined fibers for conjugates with adhesion sequences to the compact and dense film for the IGF-1-conjugate

    Histopathological image analysis : a review

    Get PDF
    Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

    Basement membrane-rich Organoids with functional human blood vessels are permissive niches for human breast cancer metastasis

    Get PDF
    Metastasic breast cancer is the leading cause of death by malignancy in women worldwide. Tumor metastasis is a multistep process encompassing local invasion of cancer cells at primary tumor site, intravasation into the blood vessel, survival in systemic circulation, and extravasation across the endothelium to metastasize at a secondary site. However, only a small percentage of circulating cancer cells initiate metastatic colonies. This fact, together with the inaccessibility and structural complexity of target tissues has hampered the study of the later steps in cancer metastasis. In addition, most data are derived from in vivo models where critical steps such as intravasation/extravasation of human cancer cells are mediated by murine endothelial cells. Here, we developed a new mouse model to study the molecular and cellular mechanisms underlying late steps of the metastatic cascade. We have shown that a network of functional human blood vessels can be formed by co-implantation of human endothelial cells and mesenchymal cells, embedded within a reconstituted basement membrane-like matrix and inoculated subcutaneously into immunodeficient mice. The ability of circulating cancer cells to colonize these human vascularized organoids was next assessed in an orthotopic model of human breast cancer by bioluminescent imaging, molecular techniques and immunohistological analysis. We demonstrate that disseminated human breast cancer cells efficiently colonize organoids containing a functional microvessel network composed of human endothelial cells, connected to the mouse circulatory system. Human breast cancer cells could be clearly detected at different stages of the metastatic process: initial arrest in the human microvasculature, extravasation, and growth into avascular micrometastases. This new mouse model may help us to map the extravasation process with unprecedented detail, opening the way for the identification of relevant targets for therapeutic intervention

    An automated pattern recognition system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies

    Get PDF
    This paper presents an automated system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies. Initially, features are extracted from colour-corrected biopsy images at positions of interest identified by adaptive thresholding and clump decomposition. A sequential floating search method and principal component analysis are used to reduce dimensionality. Manually annotated training images allow supervised training. The performance of Gaussian parametric and mixture models is compared when used to classify regions as either inflammatory or healthy. The system is optimized using a response surface method that maximises the area under the receiver operating characteristic curve. This system is then tested on images previously ranked by a number of observers with varying levels of expertise. These results are compared to the automated system using Spearman rank correlation. Results show that this system can rank 15 test images, with varying degrees of inflammation, in strong agreement with five expert pathologists
    • 

    corecore