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rajesh saturi, rajesh .
2019 International Journal of Recent Trends in Engineering and Research  
Processing tissues from histopathological images has become now fully computerized, significantly increasing the speed, the labs can produce tissue slides for viewing images digitally.  ...  The process of examining biological tissue under a microscope for detecting the severity of the disease is called histology, it is an essential technique in biomedical research and clinical practice.  ...  EXTRACTING REGION OF INTEREST (ROI) Recent advancement of digital pathology in medical sciences, mostly influencing the application of digital image analysis.  ... 
doi:10.23883/ijrter.2019.5070.g42hc fatcat:g6aegm7swzbdrjd7a7ayb6mpka

Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review

Xiaoliang Xie, Xulin Wang, Yuebin Liang, Jingya Yang, Yan Wu, Li Li, Xin Sun, Pingping Bing, Binsheng He, Geng Tian, Xiaoli Shi
2021 Frontiers in Oncology  
Many diseases are accompanied by changes in certain biochemical indicators called biomarkers in cells or tissues.  ...  Therefore, it is necessary to summarize the current process of processing pathological images and key steps and methods used in each process, including: (1) pre-processing of pathological images, (2) image  ...  All authors have approved the final version of the manuscript.  ... 
doi:10.3389/fonc.2021.763527 pmid:34900711 pmcid:PMC8660076 fatcat:g7kyyexwo5evxmrlxg3l3hjqci

Exploring automatic prostate histopathology image gleason grading via local structure modeling

Daihou Wang, David J. Foran, Jian Ren, Hua Zhong, Isaac Y. Kim, Xin Qi
2015 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
We use attributed graph to represent the tissue glandular structures in histopathology images; representative sub-graphs features were learned as bags-of-words features from labeled samples of each grades  ...  of the digitized pathology slides.  ...  Acknowledgments This research was funded, in part, by grants from NIH contract 5R01CA156386-10 and NCI contract 5R01CA161375-03, NLM contracts 5R01LM009239-06 and 5R01LM011119-04, NSF Office of Industrial  ... 
doi:10.1109/embc.2015.7318936 pmid:26736836 pmcid:PMC4920598 dblp:conf/embc/WangFRZKQ15 fatcat:rfe5hvxierhq3negcgkwlajite

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

Yawen Wu, Michael Cheng, Shuo Huang, Zongxiang Pei, Yingli Zuo, Jianxin Liu, Kai Yang, Qi Zhu, Jie Zhang, Honghai Hong, Daoqiang Zhang, Kun Huang (+2 others)
2022 Cancers  
With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches.  ...  Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation.  ...  The automatic segmentation of tissues in histology images has been explored by many studies [86, 87] .  ... 
doi:10.3390/cancers14051199 pmid:35267505 pmcid:PMC8909166 fatcat:7tfcfh4z45goxbcgf23sncok5a

Quantitative Histological Assessment of Xenobiotic-Induced Liver Enzyme Induction and Pituitary-Thyroid Axis Stimulation in Rats Using Whole-Slide Automated Image Analysis

Rosario Garrido, Tanja S. Zabka, Jianhua Tao, Mark Fielden, Adrian Fretland, Mudher Albassam
2013 Journal of Histochemistry and Cytochemistry  
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors received employment-related financial support from  ...  HE-stained thyroid gland digital slides were automatically segmented to select glandular follicular regions present in the tissue section, and area measurements corresponding to colloid and follicular  ...  Digital slides of pituitary gland labeled with anti-TSH antibody were automatically segmented at low resolution to specifically select the pars distalis areas for TSH evaluation.  ... 
doi:10.1369/0022155413482926 pmid:23456825 pmcid:PMC3636704 fatcat:4gq5xu7k3rd4lovvo5pj645mni

Representación de imágenes de histopatología utilizada en tareas de análisis automático: estado del arte

John Arevalo, Angel Cruz-Roa, Fabio A. González O
2014 Revista Med  
<p>This paper presents a review of the state-of-the-art in histopathology image representation used in automatic image analysis tasks.  ...  Finally, the paper concludes with current trends of automatic analysis of histopathology images like digital pathology.</p>  ...  Histopathology image representation Automatic Histopathology Image Analysis Process A typical automatic histopathology image workflow is depicted in Fig. 3 .  ... 
doi:10.18359/rmed.1184 fatcat:5qnocl6hibd7hnft5mkbuke234

833 A scalable deep learning framework for rapid automated annotation of histologic and morphologic features from large unlabeled pan-cancer H&E datasets

David Soong, David Soong, David Soong, Anantharaman Muthuswamy, Clifton Drew, Nora Pencheva, Maria Jure-Kunkel, Kate Sasser, Hisham Hamadeh, Suzana Couto, Brandon Higgs
2021 Journal for ImmunoTherapy of Cancer  
This dataset allowed the development of a multi-label deep neural network to segment morphologically distinct regions and detect/quantify histopathological features in WSI.ResultsAs representative image  ...  Here we propose a semi-supervised deep learning framework that automatically annotates biologically relevant image content from hundreds of solid tumor WSI with minimal pathologist intervention, thus improving  ...  This dataset allowed the development of a multi-label deep neural network to segment morphologically distinct regions and detect/quantify histopathological features in WSI.  ... 
doi:10.1136/jitc-2021-sitc2021.833 fatcat:p6pherhg4ngffaziyl5dlfcbpi

Machine Learning Methods for Histopathological Image Analysis

Daisuke Komura, Shumpei Ishikawa
2018 Computational and Structural Biotechnology Journal  
Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques.  ...  In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions  ...  Acknowledgement This study was supported by JSPS Grant-in-Aid for Scientific Research (A), No. 25710020 (SI).  ... 
doi:10.1016/j.csbj.2018.01.001 pmid:30275936 pmcid:PMC6158771 fatcat:lei72yiayzclfgmiaiullzdkt4

Automatic quantification of liver fibrosis: design and validation of a new image analysis method: comparison with semi-quantitative indexes of fibrosis

Marco Masseroli, Trinidad Caballero, Francisco O'Valle, Raimundo M.G.Del Moral, Alejandro Pérez-Milena, Raimundo G.Del Moral
2000 Journal of Hepatology  
Methods: The implemented image-processing algorithms automatically segment interstitial fibrosis areas, while extraction of portal-periportal and septal region is carried out with an automatic algorithm  ...  This paper describes the design and validation of an original image analysis-based ap plication, FibroQuant, for automatically and rapidly quantifying perisinusoidal, perivenular and portalperiportal and  ...  Automatic segmentation of portal vessel and biliary duct lumina To evaluate the automatic segmentation of the most significant portal vessel and biliary duct lumina, we used the same 30 images as in the  ... 
doi:10.1016/s0168-8278(00)80397-9 pmid:10735616 fatcat:qpap57kzzjdgxn4lb4ekc3vwem

Automatic Nuclei Segmentation in H&E Stained Breast Cancer Histopathology Images

Mitko Veta, Paul J. van Diest, Robert Kornegoor, André Huisman, Max A. Viergever, Josien P. W. Pluim, Konradin Metze
2013 PLoS ONE  
Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images.  ...  We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E) stained breast cancer histopathology images, which represent regions of whole digital slides.  ...  Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images.  ... 
doi:10.1371/journal.pone.0070221 pmid:23922958 pmcid:PMC3726421 fatcat:jrgjprwofvachca3qkm55grm7y

Automatic computational labeling of glomerular textural boundaries

Brandon Ginley, John E. Tomaszewski, Pinaki Sarder, Metin N. Gurcan, John E. Tomaszewski
2017 Medical Imaging 2017: Digital Pathology  
The largest obstacle to computational quantification of renal tissue is the ability to recognize complex glomerular textural boundaries automatically.  ...  Automatic quantification of glomeruli will streamline structural analysis in clinic, and can help realize real time diagnoses and interventions.  ...  Donna Carapetyan (Pathology & Anatomical Sciences, University at Buffalo), for performing histopathological slicing and staining of tissues.  ... 
doi:10.1117/12.2254517 dblp:conf/midp/GinleyTS17 fatcat:tq2gt33xaje3bkpyxx6euq4fq4

Upgraded Segmentation of Histopathological Images for Classification of Intraductal Breast Lesions

2019 International journal of recent technology and engineering  
For the analysis of histopathological images, the automatic dissection of cell nuclei is an important stage.  ...  Cancer among women and the second most common cancer in the world is Breast Cancer(BC). This type of cancer-initiating from breast tissue, mostly from the inner region of milk ducts.  ...  With the recent advent of whole slide digital scanners and inexpensive large size storage media, the tissue histopathology slides stored in digital image form.  ... 
doi:10.35940/ijrte.b1460.078219 fatcat:yjewo76hirgspcir3yfukoo5ae

Automatic histopathology image analysis with CNNs

Le Hou, Kunal Singh, Dimitris Samaras, Tahsin M. Kurc, Yi Gao, Roberta J. Seidman, Joel H. Saltz
2016 2016 New York Scientific Data Summit (NYSDS)  
In this paper, we use Convolutional Neural Networks (CNN) for automatic recognition of nuclear morphological attributes in histopathology images of glioma, the most common malignant brain tumor.  ...  We define Pathomics as the process of high throughput generation, interrogation, and mining of quantitative features from high-resolution histopathology tissue images.  ...  To automatically segment nuclei, the color of a tissue image is normalized to a Hematoxylin and Eosin stained template image in the L*a*b color space.  ... 
doi:10.1109/nysds.2016.7747812 fatcat:3t7xv6d44jcivkacteynag52om


Douglas Mesadri GEWEHR, Allan Fernando GIOVANINI, Sofia Inez MUNHOZ, Seigo NAGASHIMA, Andressa de Souza BERTOLDI, Ana Cristina Lira SOBRAL, Fernando Bermudez KUBRUSLY, Luiz Fernando KUBRUSLY
2021 ABCD: Arquivos Brasileiros de Cirurgia Digestiva  
Aim: To create a semi-automatic computerized protocol to quantify any amount of centrilobular fibrosis and sinusoidal dilatation in liver Masson's Trichrome-stained specimen.  ...  After, a random selection of the regions of interest (ROI's) was conducted. The data were subjected to software-assisted image analysis (ImageJ®).  ...  Centrilobular fibrosis segmentation A MT-stained image of liver tissue specimen is shown in Figures 2 A, B and C .  ... 
doi:10.1590/0102-672020210002e1608 pmid:34669894 fatcat:qf2ygjeimffd7eiybvjtxrtcvi

Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis [article]

Linhao Qu, Siyu Liu, Xiaoyu Liu, Manning Wang, Zhijian Song
2022 arXiv   pre-print
have gradually become the mainstream in the field of digital pathology.  ...  These new methods have led a new wave of automatic pathological image diagnosis and analysis targeted at annotation efficiency.  ...  Acknowledgments This work was supported by National Natural Science Foundation of China under Grant 82072021.  ... 
arXiv:2208.08789v2 fatcat:5ehpmg34jjfuzpbckdgj5fqjjm
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