309 research outputs found
Automated Discrimination of Pathological Regions in Tissue Images: Unsupervised Clustering vs Supervised SVM Classification
Recognizing and isolating cancerous cells from non pathological tissue areas (e.g. connective stroma) is crucial for fast and objective immunohistochemical analysis of tissue images. This operation allows the further application of fully-automated techniques for quantitative evaluation of protein activity, since it avoids the necessity of a preventive manual selection of the representative pathological areas in the image, as well as of taking pictures only in the pure-cancerous portions of the tissue. In this paper we present a fully-automated method based on unsupervised clustering that performs tissue segmentations highly comparable with those provided by a skilled operator, achieving on average an accuracy of 90%. Experimental results on a heterogeneous dataset of immunohistochemical lung cancer tissue images demonstrate that our proposed unsupervised approach overcomes the accuracy of a theoretically superior supervised method such as Support Vector Machine (SVM) by 8%
Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers
Background: Colour is the most important feature used in quantitative immunohisto- chemistry (IHC) image analysis; IHC is used to provide information relating to aetiology and to con rm malignancy.
Methods: Statistical modelling is a technique widely used for colour detection in computer vision. We have developed a statistical model of colour detection applicable to detection of stain colour in digital IHC images. Model was rst trained by massive colour pixels collected semi-automatically. To speed up the training and detection processes, we removed luminance channel, Y channel of YCbCr colour space and chose 128 histogram bins which is the optimal number. A maximum likelihood classi- er is used to classify pixels in digital slides into positively or negatively stained pixels automatically. The model-based tool was developed within ImageJ to quantify targets identi ed using IHC and histochemistry.
Results: The purpose of evaluation was to compare the computer model with human evaluation. Several large datasets were prepared and obtained from human oesopha- geal cancer, colon cancer and liver cirrhosis with di erent colour stains. Experimental results have demonstrated the model-based tool achieves more accurate results than colour deconvolution and CMYK model in the detection of brown colour, and is comparable to colour deconvolution in the detection of pink colour. We have also demostrated the proposed model has little inter-dataset variations.
Conclusions: A robust and e ective statistical model is introduced in this paper. The model-based interactive tool in ImageJ, which can create a visual representation of the statistical model and detect a speci ed colour automatically, is easy to use and avail- able freely at http://rsb.info.nih.gov/ij/plugins/ihc-toolbox/index.html. Testing to the tool by di erent users showed only minor inter-observer variations in results
Automated segmentation of tissue images for computerized IHC analysis
This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie
Achieving the Way for Automated Segmentation of Nuclei in Cancer Tissue Images through Morphology-Based Approach: a Quantitative Evaluation
In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contoursâ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations
Diverse types of expertise in facial recognition
Facial recognition errors can jeopardize national security, criminal justice, public safety and civil rights. Here, we compare the most accurate humans and facial recognition technology in a detailed lab-based evaluation and international proficiency test for forensic scientists involving 27 forensic departments from 14 countries. We find striking cognitive and perceptual diversity between naturally skilled super-recognizers, trained forensic examiners and deep neural networks, despite them achieving equivalent accuracy. Clear differences emerged in super-recognizersâ and forensic examinersâ perceptual processing, errors, and response patterns: super-recognizers were fast, biased to respond âsame personâ and misidentified people with extreme confidence, whereas forensic examiners were slow, unbiased and strategically avoided misidentification errors. Further, these human experts and deep neural networks disagreed on the similarity of faces, pointing to differences in their representations of faces. Our findings therefore reveal multiple types of facial recognition expertise, with each type lending itself to particular facial recognition roles in operational settings. Finally, we show that harnessing the diversity between individual experts provides a robust method of maximizing facial recognition accuracy. This can be achieved either via collaboration between experts in forensic laboratories, or most promisingly, by statistical fusion of match scores provided by different types of expert
Therapeutic potential of erythropoietin in cardiovascular disease:Erythropoiesis and beyond
Erythropoietin (EPO) is a glycoprotein hormone implicated in the regutation of red blood cell production. Anemia is common in chronic heart failure (CHF) patients and associated with an inappropriately low EPO-production, suggesting a role for its recombinant human form (rhEPO) in treatment. Although safety concerns have been raised regarding treatment with rhEPO in patients with chronic kidney disease, treatment with rhEPO in patients with CHF has so far been safe and well tolerated. The effect of rhEPO on outcome in anemic CHF patients is under investigation in a phase III clinical trial. In addition to its erythropoietic effects, EPO has been detected in the cardiovascular system, fueling intense research into possible non-hematopoietic effects. EPO has been shown to exert protective effects on the heart during acute myocardial ischemia and improve cardiac function in experimental CHF. Acute protection is mediated through reduction of apoptotic cell death. Improvement of cardiac function in CHF is related to myocardial neovascularization. EPO exhibits a vast array of beneficial effects in cardiovascular disease. In addition to the correction of anemia in CHF, rhEPO might benefit patients with cardiovascular disease
Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques
A new approach for the segmentation of gland units in histological images is proposed with the aim of contributing to the improvement of the prostate cancer diagnosis. Clustering methods on several
colour spaces are applied to each sample in order to generate a binary
mask of the different tissue components. From the mask of lumen candidates, the Locally Constrained Watershed Transform (LCWT) is applied
as a novel gland segmentation technique never before used in this type
of images. 500 random gland candidates, both benign and pathological,
are selected to evaluate the LCWT technique providing results of Dice
coefficient of 0.85. Several shape and textural descriptors in combination
with contextual features and a fractal analysis are applied, in a novel
way, on different colour spaces achieving a total of 297 features to discern between artefacts and true glands. The most relevant features are
then selected by an exhaustive statistical analysis in terms of independence between variables and dependence with the class. 3.200 artefacts,
3.195 benign glands and 3.000 pathological glands are obtained, from a
data set of 1468 images at 10x magnification. A careful strategy of data
partition is implemented to robustly address the classification problem
between artefacts and glands. Both linear and non-linear approaches are
considered using machine learning techniques based on Support Vector
Machines (SVM) and feedforward neural networks achieving values of
sensitivity, specificity and accuracy of 0.92, 0.97 and 0.95, respectivelyThis work has been funded by the Ministry of Economy, Industry and Competitiveness under the SICAP project (DPI2016-77869-C2-1-R). The work
of AdriÂŽan Colomer has been supported by the Spanish FPI Grant BES-2014-067889.
We gratefully acknowledge the support of NVIDIA Corporation with the donation of
the Titan Xp GPU used for this researchGarcĂa-Pardo, JG.; Colomer, A.; Naranjo Ornedo, V.; Peñaranda, F.; Sales, MĂ. (2018). Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques. En Intelligent Data Engineering and Automated Learning â IDEAL 2018. Springer. 642-650. https://doi.org/10.1007/978-3-030-03493-1_67S642650Gleason, D.F.: Histologic grading and clinical staging of prostatic carcinoma. In: Urologic Pathology (1977)Naik, S., Doyle, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: Gland segmentation and computerized gleason grading of prostate histology by integrating low-, high-level and domain specific information. In: MIAAB Workshop, pp. 1â8 (2007)Nguyen, K., Sabata, B., Jain, A.K.: Prostate cancer grading: gland segmentation and structural features. Pattern Recogn. Lett. 33(7), 951â961 (2012)Kwak, J.T., Hewitt, S.M.: Multiview boosting digital pathology analysis of prostate cancer. Comput. Methods Programs Biomed. 142, 91â99 (2017)Ren, J., Sadimin, E., Foran, D.J., Qi, X.: Computer aided analysis of prostate histopathology images to support a refined gleason grading system. In: SPIE Medical Imaging, International Society for Optics and Photonics, p. 101331V (2017)Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Berlin (2013)Nguyen, K., Sarkar, A., Jain, A.K.: Structure and context in prostatic gland segmentation and classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 115â123. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33415-3_15Beare, R.: A locally constrained watershed transform. IEEE Trans. Pattern Anal. Mach. Intell. 28(7), 1063â1074 (2006)Gertych, A., et al.: Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Comput. Med. Imaging Graph. 46, 197â208 (2015)Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971â987 (2002)Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657â1663 (2010)Huang, P., Lee, C.: Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans. Med. Imaging 28(7), 1037â1050 (2009)Ruifrok, A.C., Johnston, D.A., et al.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23(4), 291â299 (2001
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
Breast cancer is one of the main causes of cancer death worldwide. Early
diagnostics significantly increases the chances of correct treatment and
survival, but this process is tedious and often leads to a disagreement between
pathologists. Computer-aided diagnosis systems showed potential for improving
the diagnostic accuracy. In this work, we develop the computational approach
based on deep convolution neural networks for breast cancer histology image
classification. Hematoxylin and eosin stained breast histology microscopy image
dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast
Cancer Histology Images. Our approach utilizes several deep neural network
architectures and gradient boosted trees classifier. For 4-class classification
task, we report 87.2% accuracy. For 2-class classification task to detect
carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity
96.5/88.0% at the high-sensitivity operating point. To our knowledge, this
approach outperforms other common methods in automated histopathological image
classification. The source code for our approach is made publicly available at
https://github.com/alexander-rakhlin/ICIAR2018Comment: 8 pages, 4 figure
Heart failure-associated anemia: bone marrow dysfunction and response to erythropoietin
Heart failure (HF)-associated anemia is common and has a poor outcome. Because bone marrow (BM) dysfunction may contribute to HF-associated anemia, we first investigated mechanisms of BM dysfunction in an established model of HF, the transgenic REN2 rat, which is characterized by severe hypertrophy and ventricular dilatation and SD rats as controls. Secondly, we investigated whether stimulation of hematopoiesis with erythropoietin (EPO) could restore anemia and BM dysfunction. After sacrifice, erythropoietic precursors (BFU-E) were isolated from the BM and cultured for 10 days. BFU-E were quantified and transcript abundance of genes involved in erythropoiesis were assayed. Number of BFU-E were severely decreased in BM of REN2 rats compared to SD rats (50â±â6.2 vs. 6.4â±â1.7, pâ<â0.01). EPO treatment increased hematocrit in the SD-EPO group (after 6 weeks, 49â±â1 vs. 58â±â1%, pâ<â0.01); however, in the mildly anemic REN2 rats, there was no effect (43â±â1 vs. 44â±â1%). This was paralleled by a 67% decrease in BFU-E in BM of REN2 rats compared to SD (pâ<â0.01). EPO significantly improved BFU-E in both SD and REN2 but could not restore this to control levels in the REN2 rats. Expression of several genes involved in differentiation (LMO2), mobilization (SDF-1), and iron incorporation (transferrin receptor) of the BM were differentially expressed in REN2 rats compared to SD rats, and EPO did not normalize this. Altogether, these results suggest that BM dysfunction is an important contributor to HF-associated anemia and that EPO is not an effective agent to treat HF-associated anemia
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