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:
Bio-inspired Attentive Segmentation of Retinal OCT Imaging Read chapter Read first chapter

2020 | OriginalPaper | Chapter

An Interactive Approach to Region of Interest Selection in Cytologic Analysis of Uveal Melanoma Based on Unsupervised Clustering

Authors : Haomin Chen, T. Y. Alvin Liu, Zelia Correa, Mathias Unberath

Published in: 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

Facilitating quantitative analysis of cytology images of fine needle aspirates of uveal melanoma is important to confirm diagnosis and inform management decisions. Extracting high-quality regions of interest (ROIs) from cytology whole slide images is a critical first step. To the best of our knowledge, we describe the first unsupervised clustering-based method for fine needle aspiration cytology (FNAC) that automatically suggests high-quality ROIs. Our method is integrated in a graphical user interface that allows for interactive refinement of ROI suggestions to tailor analysis to any specific specimen. We show that the proposed approach suggests ROIs that are in very good agreement with expert-extracted regions and demonstrate that interactive refinement results in the extraction of more high-quality regions compared to purely algorithmic extraction alone.

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 Multi-level Light U-Net and Atrous Spatial Pyramid Pooling for Optic Disc Segmentation on Fundus Image
next chapter Retinal OCT Denoising with Pseudo-Multimodal Fusion Network
Literature
1.
Amrehn, M., Gaube, S., Unberath, M., et al.: UI-NET: interactive artificial neural networks for iterative image segmentation based on a user model (2017)
2.
Aresta, G., et al.: iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network. Sci. Rep. 9, 11591 (2019)CrossRef
3.
Barker, J., Hoogi, A., Depeursinge, A., Rubin, D.L.: Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles. Med. Image Anal. 30, 60–71 (2016)CrossRef
4.
Brendel, W., Bethge, M.: Approximating CNNs with bag-of-local-features models works surprisingly well on ImageNet. In: International Conference on Learning Representations (2019)
5.
6.
Chang, L., Zhang, M., Li, W.: A coarse-to-fine approach for medical hyperspectral image classification with sparse representation. In: Yu, J., et al. (eds.) AOPC 2017: Optical Spectroscopy and Imaging, vol. 10461, pp. 136–144. International Society for Optics and Photonics, SPIE (2017)
7.
Corrêa, Z., Augsburger, J.: Sufficiency of FNAB aspirates of posterior uveal melanoma for cytologic versus GEP classification in 159 patients, and relative prognostic significance of these classifications. Graefe’s archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie 252 (2013)
8.
Dov, D., Kovalsky, S.Z., Cohen, J., Range, D.E., Henao, R., Carin, L.: A deep-learning algorithm for thyroid malignancy prediction from whole slide cytopathology images (2019)
9.
Folberg, R., Augsburger, J.J., Gamel, J.W., Shields, J.A., Lang, W.R.: Fine-needle aspirates of uveal melanomas and prognosis. Am. J. Ophthalmol. 100(5), 654–657 (1985)CrossRef
10.
Garud, H., et al.: High-magnification multi-views based classification of breast fine needle aspiration cytology cell samples using fusion of decisions from deep convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 828–833, July 2017
11.
Girard, N., Zhygallo, A., Tarabalka, Y.: ClusterNet: unsupervised generic feature learning for fast interactive satellite image segmentation. In: Image and Signal Processing for Remote Sensing XXV, vol. 11155, pp. 244–254. SPIE (2019)
12.
Grossmann, P., Stringfield, O., El-Hachem, N., et al.: Defining the biological basis of radiomic phenotypes in lung cancer. In: eLife (2017)
13.
14.
Li, J., Li, W., Gertych, A., Knudsen, B.S., Speier, W., Arnold, C.W.: An attention-based multi-resolution model for prostate whole slide imageclassification and localization. CoRR abs/1905.13208 (2019)
15.
Lin, H., Chen, H., Dou, Q., Wang, L., Qin, J., Heng, P.: ScanNet: a fast and dense scanning framework for metastastic breast cancer detection from whole-slide image. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 539–546 (2018)
16.
17.
Liu, T.A., Zhu, H., Chen, H., et al.: Gene expression profile prediction in uveal melanoma using deep learning: a pilot study for development of an alternative survival prediction tool. Ophthalmol. Retina S2468-6530, 30251–30257 (2020)
18.
Paszke, A., Gross, S., Chintala, S., et al.: Automatic differentiation in PyTorch (2017)
19.
Roullier, V., Lézoray, O., Ta, V.T., Elmoataz, A.: Multi-resolution graph-based analysis of histopathological whole slide images: application to mitotic cell extraction and visualization. Computer. Med. Imaging Graph. 35(7), 603–615 (2011). Whole Slide Image Process
20.
Saikia, A.R., Bora, K., Mahanta, L.B., Das, A.K.: Comparative assessment of CNN architectures for classification of breast FNAC images. Tissue Cell 57, 8–14 (2019). eM in cell and tissues
21.
Singh, A.D., Turell, M.E., Topham, A.K.: Uveal melanoma: Trends in incidence, treatment, and survival. Ophthalmology 118(9), 1881–1885 (2011)CrossRef
22.
Wang, G., Li, W., Zuluaga, M.A., et al.: Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans. Med. Imaging 37(7), 1562–1573 (2018)CrossRef
23.
Xu, N., Price, B., Cohen, S., Yang, J., Huang, T.S.: Deep interactive object selection. In: IEEE Conference on Computer Vision and Pattern Recognition, June 2016
24.
Yang, B., Fu, X., Sidiropoulos, N., Hong, M.: Towards k-means-friendly spaces: simultaneous deep learning and clustering. In: 34th International Conference on Machine Learning, ICML 2017, pp. 5888–5901 (2017)
25.
Yang, B., Fu, X., Sidiropoulos, N., Hong, M.: Towards k-means-friendly spaces: simultaneous deep learning and clustering. In: 34th International Conference on Machine Learning, ICML 2017, pp. 5888–5901 (2017)
26.
Zhu, Z., Xia, Y., Shen, W., Fishman, E., Yuille, A.: A 3D coarse-to-fine framework for volumetric medical image segmentation. In: 2018 International Conference on 3D Vision (3DV), pp. 682–690, September 2018
27.
Zhu, Z., Xia, Y., Xie, L., Fishman, E.K., Yuille, A.L.: Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma. CoRR abs/1807.02941 (2018)
Metadata
Title
An Interactive Approach to Region of Interest Selection in Cytologic Analysis of Uveal Melanoma Based on Unsupervised Clustering
Authors
Haomin Chen
T. Y. Alvin Liu
Zelia Correa
Mathias Unberath
Copyright Year
2020
Book
Ophthalmic Medical Image Analysis

Print ISBN: 978-3-030-63418-6

Electronic ISBN: 978-3-030-63419-3

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-63419-3_12

Premium Partner

    Image Credits