Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Improving Accuracy of Nuclei Segmentation by Reducing Histological Image Variability Read chapter Read first chapter

2018 | OriginalPaper | Chapter

DeepCerv: Deep Neural Network for Segmentation Free Robust Cervical Cell Classification

Authors : O. U. Nirmal Jith, K. K. Harinarayanan, Srishti Gautam, Arnav Bhavsar, Anil K. Sao

Published in: Computational Pathology and Ophthalmic Medical Image Analysis

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Automated classification of cervical cancer cells has the potential to reduce high mortality rates due to cervical cancer in developing countries. However traditional algorithms for the same depend on accurate segmentation of cells, which in itself is an open problem. Often the algorithms are also not evaluated by considering the huge inter-observer variability in ground truth labels. We propose a new deep learning algorithm that does not depend on accurate segmentation by directly classifying image patches with cells. We evaluate the proposed algorithm on the popular Herlev dataset and show that it achieves state of the art accuracy while being extremely fast. The experimental results are also demonstrated using AIndra dataset collected by us, which also captures the inter observer variability.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Evaluating Out-of-the-Box Methods for the Classification of Hematopoietic Cells in Images of Stained Bone Marrow
next chapter Whole Slide Image Registration for the Study of Tumor Heterogeneity
Footnotes
1
Presence of neutrophils signifies inflammation in that region.
 
Literature
1.
Bengtsson, E., Malm, P.: Screening for Cervical Cancer Using Automated Analysis of PAP-Smears. Computational and Mathematical Methods in Medicine 2014, 1–12 (2014)CrossRef
2.
Bora, K., Chowdhury, M., Mahanta, L.B., Kundu, M.K., Das, A.K.: Automated classification of Pap smear images to detect cervical dysplasia. Computer Methods and Programs in Biomedicine 138, 31–47 (2017)CrossRef
3.
Chankong, T., Theera-Umpon, N., Auephanwiriyakul, S.: Automatic cervical cell segmentation and classification in Pap smears. Computer Methods and Programs in Biomedicine 113(2), 539–556 (2014)CrossRef
4.
Cohen, J.: A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20(1), 37–46 (1960)CrossRef
5.
Elsheikh, T.M., Austin, R.M., Chhieng, D.F., Miller, F.S., Moriarty, A.T., Renshaw, A.A.: American Society of Cytopathology: American Society of Cytopathology workload recommendations for automated Pap test screening: Developed by the productivity and quality assurance in the era of automated screening task force. Diagnostic Cytopathology 41(2), 174–178 (2013)CrossRef
6.
Kitchener, H.C., Castle, P.E., Cox, J.T.: Chapter 7: achievements and limitations of cervical cytology screening. Vaccine 24, S63–S70 (2006)CrossRef
7.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems. pp. 1097–1105 (2012)
8.
Lu, Z., Carneiro, G., Bradley, A.P., Ushizima, D., Nosrati, M.S., Bianchi, A.G.C., Carneiro, C.M., Hamarneh, G.: Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells. IEEE Journal of Biomedical and Health Informatics 21(2), 441–450 (2017)CrossRef
9.
Marinakis, Y., Dounias, G., Jantzen, J.: Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification. Computers in Biology and Medicine 39(1), 69–78 (2009)CrossRef
10.
11.
Nanni, L., Lumini, A., Brahnam, S.: Local binary patterns variants as texture descriptors for medical image analysis. Artificial Intelligence in Medicine 49(2), 117–125 (2010)CrossRef
12.
Norup, J.: Classification of Pap-Smear Data by Tranduction Neuro-Fuzzy Methods. masterThesis, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark (2005)
13.
Phoulady, H.A., Zhou, M., Goldgof, D.B., Hall, L.O., Mouton, P.R.: Automatic quantification and classification of cervical cancer via adaptive nucleus shape modeling. In: Image Processing (ICIP), 2016 IEEE International Conference On. pp. 2658–2662. IEEE (2016)
14.
Sokouti, B., Haghipour, S., Tabrizi, A.D.: A framework for diagnosing cervical cancer disease based on feedforward MLP neural network and ThinPrep histopathological cell image features. Neural Computing and Applications 24(1), 221–232 (2014)CrossRef
15.
Solomon, D.: The 2001 Bethesda SystemTerminology for Reporting Results of Cervical Cytology. JAMA 287(16), 2114 (2002)CrossRef
16.
Song, Y., Zhang, L., Chen, S., Ni, D., Lei, B., Wang, T.: Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning. IEEE transactions on bio-medical engineering 62(10), 2421–2433 (2015)CrossRef
17.
Young, N.A., Naryshkin, S., Atkinson, B.F., Ehya, H., Gupta, P.K., Kline, T.S., Luff, R.D.: Interobserver variability of cervical smears with squamous-cell abnormalities: A philadelphia study. Diagnostic cytopathology 11(4), 352–357 (1994)CrossRef
18.
Zhang, L., Kong, H., Liu, S., Wang, T., Chen, S., Sonka, M.: Graph-based segmentation of abnormal nuclei in cervical cytology. Computerized Medical Imaging and Graphics 56, 38–48 (2017)CrossRef
19.
Zhang, L., Kong, H., Ting Chin, C., Liu, S., Fan, X., Wang, T., Chen, S.: Automation-assisted cervical cancer screening in manual liquid-based cytology with hematoxylin and eosin staining. Cytometry. Part A: The Journal of the International Society for Analytical Cytology 85(3), 214–230 (Mar 2014)CrossRef
20.
Zhang, L., Lu, L., Nogues, I., Summers, R.M., Liu, S., Yao, J.: DeepPap: Deep Convolutional Networks for Cervical Cell Classification. IEEE Journal of Biomedical and Health Informatics 21(6), 1633–1643 (2017)CrossRef
21.
Zhu, X., Yao, J., Zhu, F., Huang, J.: Wsisa: Making survival prediction from whole slide histopathological images. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 7234–7242 (2017)
Metadata
Title
DeepCerv: Deep Neural Network for Segmentation Free Robust Cervical Cell Classification
Authors
O. U. Nirmal Jith
K. K. Harinarayanan
Srishti Gautam
Arnav Bhavsar
Anil K. Sao
Copyright Year
2018
Book
Computational Pathology and Ophthalmic Medical Image Analysis

Print ISBN: 978-3-030-00948-9

Electronic ISBN: 978-3-030-00949-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00949-6_11

Premium Partner

    Image Credits