Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation

M. Veta, P.J. van Diest, J.P.W. Pluim

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

7 Citations (Scopus)

Abstract

The size of nuclei in histological preparations from excised breast tumors is predictive of patient outcome (large nuclei indicate poor outcome). Pathologists take into account nuclear size when performing breast cancer grading. In addition,the mean nuclear area (MNA) has been shown to have independent prognostic value. The straightforward approach to measuring nuclear size is by performing nuclei segmentation. We hypothesize that given an image of a tumor region with known nuclei locations,the area of the individual nuclei and region statistics such as the MNA can be reliably computed directly from the image data by employing a machine learning model,without the intermediate step of nuclei segmentation. Towards this goal,we train a deep convolutional neural network model that is applied locally at each nucleus location,and can reliably measure the area of the individual nuclei and the MNA. Furthermore,we show how such an approach can be extended to perform combined nuclei detection and measurement,which is reminiscent of granulometry.

LanguageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PublisherSpringer
Pages632-639
Number of pages8
ISBN (Print)9783319467221
DOIs
StatePublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume9901
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Nucleus
Tumors
Segmentation
Learning systems
Statistics
Neural networks
Tumor
Grading
Breast Cancer
Neural Network Model
Preparation
Machine Learning

Keywords

  • Breast cancer
  • Convolutional neural networks
  • Deep learning
  • Histopathology image analysis

Cite this

Veta, M., van Diest, P. J., & Pluim, J. P. W. (2016). Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (pp. 632-639). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9901). Springer. DOI: 10.1007/978-3-319-46723-8_73
Veta, M. ; van Diest, P.J. ; Pluim, J.P.W./ Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer, 2016. pp. 632-639 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "The size of nuclei in histological preparations from excised breast tumors is predictive of patient outcome (large nuclei indicate poor outcome). Pathologists take into account nuclear size when performing breast cancer grading. In addition,the mean nuclear area (MNA) has been shown to have independent prognostic value. The straightforward approach to measuring nuclear size is by performing nuclei segmentation. We hypothesize that given an image of a tumor region with known nuclei locations,the area of the individual nuclei and region statistics such as the MNA can be reliably computed directly from the image data by employing a machine learning model,without the intermediate step of nuclei segmentation. Towards this goal,we train a deep convolutional neural network model that is applied locally at each nucleus location,and can reliably measure the area of the individual nuclei and the MNA. Furthermore,we show how such an approach can be extended to perform combined nuclei detection and measurement,which is reminiscent of granulometry.",
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Veta, M, van Diest, PJ & Pluim, JPW 2016, Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9901, Springer, pp. 632-639. DOI: 10.1007/978-3-319-46723-8_73

Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation. / Veta, M.; van Diest, P.J.; Pluim, J.P.W.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer, 2016. p. 632-639 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9901).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Veta M, van Diest PJ, Pluim JPW. Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer. 2016. p. 632-639. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Available from, DOI: 10.1007/978-3-319-46723-8_73