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

23 Citations (Scopus)
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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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
Number of pages8
ISBN (Print)9783319467221
Publication statusPublished - 2016

Publication series

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


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


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