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Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images

Muhammad Shahzad, Arif Iqbal Umar, Muazzam A. Khan, Syed Hamad Shirazi, Zakir Khan, Waqas Yousaf
2020 Computational and Mathematical Methods in Medicine  
Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation.  ...  In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach.  ...  We perform semantic segmentation on the whole-slide blood cell image.  ... 
doi:10.1155/2020/4015323 pmid:32411282 pmcid:PMC7201460 fatcat:26ta7vkpjba7hnk6s4qlzhd77i

Automated screening of sickle cells using a smartphone-based microscope and deep learning [article]

Kevin de Haan, Hatice Ceylan Koydemir, Yair Rivenson, Derek Tseng, Elizabeth Van Dyne, Lissette Bakic, Doruk Karinca, Kyle Liang, Megha Ilango, Esin Gumustekin, Aydogan Ozcan
2019 arXiv   pre-print
The second network acts on the output of the first image enhancement neural network and is used to perform the semantic segmentation between healthy and sickle cells within a blood smear.  ...  With its high accuracy, this mobile and cost-effective method has the potential to be used as a screening tool for SCD and other blood cell disorders in resource-limited settings.  ...  Semantic segmentation A second deep neural network is used to perform semantic segmentation of the blood cells where M and N are the number of pixels in an image, and i, and j are the pixel indices as  ... 
arXiv:1912.05155v1 fatcat:kgvclyz6qvhznnhcagqm57syiy

Automated screening of sickle cells using a smartphone-based microscope and deep learning

Kevin de Haan, Hatice Ceylan Koydemir, Yair Rivenson, Derek Tseng, Elizabeth Van Dyne, Lissette Bakic, Doruk Karinca, Kyle Liang, Megha Ilango, Esin Gumustekin, Aydogan Ozcan
2020 npj Digital Medicine  
The second network acts on the output of the first image enhancement neural network and is used to perform the semantic segmentation between healthy and sickle cells within a blood smear.  ...  With its high accuracy, this mobile and cost-effective method has the potential to be used as a screening tool for SCD and other blood cell disorders in resource-limited settings.  ...  Jonathan Armstrong of UCLA Health for their help labeling sickle cells.  ... 
doi:10.1038/s41746-020-0282-y pmid:32509973 pmcid:PMC7244537 fatcat:h4vythi2ujgh3kzpqpqd5zjlnm

Deep Learning for Semantic Segmentation vs. Classification in Computational Pathology: Application to Mitosis Analysis in Breast Cancer Grading

Gabriel Jiménez, Daniel Racoceanu
2019 Frontiers in Bioengineering and Biotechnology  
The results show the potential of deep learning in the analysis of Whole Slide Images (WSI) and its integration to computer-aided systems.  ...  The first method consists of two parts, entailing a preprocessing of the digital histological image and a free-handcrafted-feature Convolutional Neural Network (CNN) used for binary classification.  ...  AUTHOR CONTRIBUTIONS All authors contributed to the methodology proposal, analysis of results, writing, and review of the manuscript.  ... 
doi:10.3389/fbioe.2019.00145 pmid:31281813 pmcid:PMC6597878 fatcat:vym45szyanbhhafrjfno2vm3si

WBC-based segmentation and classification on microscopic images: a minor improvement

Xin-Hui Lam, Kok-Why Ng, Yih-Jian Yoong, Seng-Beng Ng
2021 F1000Research  
At present, there is no robust automated method to segment and classify WBCs images with high accuracy. This paper aims to improve on WBCs image segmentation and classification method.  ...  Microscope images of captured WBCs for processing and analysis are important to interpret the body condition.  ...  Acknowledgements We thank the anonymous reviewers for their careful reading of our manuscript and their insightful comments and suggestions.  ... 
doi:10.12688/f1000research.73315.1 pmid:35399225 pmcid:PMC8976187 fatcat:7ljufzfhzvhopexrczzuzpan6q

Semantic interpretation of robust imaging features for Fuhrman grading of renal carcinoma

Andrew Champion, Guolan Lu, Marcus Walker, Sonal Kothari, Adeboye O. Osunkoya, May D. Wang
2014 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
Although previous work suggests several informative imaging features for pattern recognition, there exists a semantic gap between characteristics of these features and pathologists' interpretation of histopathological  ...  We provide a semantic interpretation for the imaging features used in these models by linking features to pathologists' grading criteria.  ...  Stokes for his assistance in image data acquisition.  ... 
doi:10.1109/embc.2014.6945104 pmid:25571472 pmcid:PMC4983417 dblp:conf/embc/ChampionLWKOW14 fatcat:latxvp4hbjg4zdhrux3k2ymtz4

Segmentation methods of H&E-stained histological images of lymphoma: A review

Thaína A. Azevedo Tosta, Leandro A. Neves, Marcelo Z. do Nascimento
2017 Informatics in Medicine Unlocked  
This study presents the progress of recent advances in methods for segmentation of chronic lymphocytic leukemia, follicular lymphoma and mantle cell lymphoma images.  ...  This study presents the most often used segmentation techniques for these images segmentation, such as thresholding, region-based methods and K-means clustering algorithm.  ...  With the introduction of whole-slide scanners, CAD systems became popular, even for processing of whole-slides lymphoma images (WSI).  ... 
doi:10.1016/j.imu.2017.05.009 fatcat:kn26dm5ysfft7g6pi5p6xlob7u

TADOC : Tool for Automated Detection of Oral Cancer

Khalid Nazim Abdul Sattar
2020 International Journal of Advanced Computer Science and Applications  
from the large whole slide images and use of carefully designed post-processing methods for the slide-based classification [39] .  ...  In this paper an automated learning-based system for detection of oral cancer from Whole Slide Images (WSI) has been designed.  ...  Image Segmentation The area of interest through different methods from an image viz. cell, nuclei or tumor can be obtained by Image segmentation [18] .  ... 
doi:10.14569/ijacsa.2020.0110364 fatcat:js2vngwc6jgofas6mkzsirbvvm

A U-net based approach to epidermal tissue segmentation in whole slide histopathological images

Kay R. J. Oskal, Martin Risdal, Emilius A. M. Janssen, Erling S. Undersrud, Thor O. Gulsrud
2019 SN Applied Sciences  
Histopathological examination of hematoxylin and eosin stained tissue biopsies under a light microscope is currently the gold standard for diagnosis.  ...  With more pathology departments starting to convert conventional glass slides into digital resources, a Computer Aided Diagnostic (CAD) system that can automate part of the diagnostic process will help  ...  Conflict of interest The authors declares that they have no conflict of interests.  ... 
doi:10.1007/s42452-019-0694-y fatcat:hzudnoeom5ga7hdhthajnlqixq

Detection and Classification of Overlapping Cell Nuclei in Cytology Effusion Images Using a Double-Strategy Random Forest

Khin Win, Somsak Choomchuay, Kazuhiko Hamamoto, Manasanan Raveesunthornkiat
2018 Applied Sciences  
This paper presents a method for the automated detection and classification of overlapping nuclei from single nuclei appearing in cytology pleural effusion (CPE) images.  ...  The proposed algorithm can serve as a new supportive tool in the automated diagnosis of cancer cells from cytology images.  ...  We also immensely grateful to Department of Pathology, Faculty of Medicine, Srinakharinwirot University, Thailand, for providing the datasets and insight and expertise that greatly assisted the research  ... 
doi:10.3390/app8091608 fatcat:akwccppqjjd55okrkdmizycr3e

Label-free imaging and classification of live P. falciparum enables high performance parasitemia quantification without fixation or staining

Paul Lebel, Rebekah Dial, Venkata N. P. Vemuri, Valentina Garcia, Joseph DeRisi, Rafael Gómez-Sjöberg, Delmiro Fernandez-Reyes
2021 PLoS Computational Biology  
Manual microscopic inspection of fixed and stained blood smears has remained the gold standard for Plasmodium parasitemia analysis for over a century.  ...  Here, we demonstrate that deep learning enables both life stage classification and accurate parasitemia quantification of ordinary brightfield microscopy images of live, unstained red blood cells.  ...  Acknowledgments The authors would like to acknowledge Shalin Mehta, Loic Royer, Bin Yang, and Kevin Yamauchi for useful discussions related to construction of the microscope and its software PLOS COMPUTATIONAL  ... 
doi:10.1371/journal.pcbi.1009257 pmid:34370724 pmcid:PMC8376094 fatcat:skjzel3aovb5jcz7ohm7epopze

Semantic Focusing Allows Fully Automated Single-Layer Slide Scanning of Cervical Cytology Slides

Bernd Lahrmann, Nektarios A. Valous, Urs Eisenmann, Nicolas Wentzensen, Niels Grabe, Paul van Diest
2013 PLoS ONE  
In summary, the integration of semantic focus information into whole-slide imaging allows automatic high-quality imaging of LBC slides and subsequent biomarker analysis.  ...  On an independent validation set of 3,232 cell images we achieved an accuracy of 94.8% for classifying images as focused.  ...  Liquidbased cytology is a method of preparing cytological samples for microscopic examination.  ... 
doi:10.1371/journal.pone.0061441 pmid:23585899 pmcid:PMC3621829 fatcat:3ktn6mwahbhuhfr23nsj7yugde

AI and Medical Imaging Informatics: Current Challenges and Future Directions

Andreas S. Panayides, Amir Amini, Nenad Filipovic, Ashish Sharma, Sotirios Tsaftaris, Alistair Young, David J. Foran, Nhan Do, Spyretta Golemati, Tahsin Kurc, Kun Huang, Konstantina S. Nikita (+4 others)
2020 IEEE journal of biomedical and health informatics  
It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already  ...  This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice  ...  These challenges have motivated many efforts for the development of image analysis methods to automate whole slide image pathology interpretation.  ... 
doi:10.1109/jbhi.2020.2991043 pmid:32609615 pmcid:PMC8580417 fatcat:dcaefxwwqjfwla5asin34x2hxm

Deep Learning for Computational Cytology: A Survey [article]

Hao Jiang, Yanning Zhou, Yi Lin, Ronald CK Chan, Jiang Liu, Hao Chen
2022 arXiv   pre-print
Computational cytology is a critical, rapid-developing, yet challenging topic in the field of medical image computing which analyzes the digitized cytology image by computer-aided technologies for cancer  ...  To investigate the advanced methods and comprehensive applications, we survey more than 120 publications of DL-based cytology image analysis in this article.  ...  Acknowledgments This work was supported by Beijing Institute of Collaborative Innovation Program (No. BICI22EG01).  ... 
arXiv:2202.05126v2 fatcat:d5ockk4ofjgv3oyxnuce4hmxpu

Automated Histology Analysis: Opportunities for signal processing

Michael T McCann, John A. Ozolek, Carlos A. Castro, Bahram Parvin, Jelena Kovacevic
2015 IEEE Signal Processing Magazine  
, allowing for visualization of whole cells and all tissue components.  ...  These methods could also automate tasks that are prohibitively time-consuming for humans, e.g. discovering new disease markers from hundreds of whole-slide images (WSIs) or precisely quantifying tissues  ...  Kovacevic is the Fellow of the IEEE and was the Editor-in-Chief of the IEEE Transactions on Image Processing.  ... 
doi:10.1109/msp.2014.2346443 fatcat:ao6rczso3ndj7j2tw5p2vdywm4
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