3-D density kernel estimation for counting in microscopy image volumes using 3-D image filters and random decision trees

Dominic Waithe*, Martin Hailstone, Mukesh Kumar Lalwani, Richard Parton, Lu Yang, Roger Patient, Christian Eggeling, Ilan Davis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We describe a means through which cells can be accurately counted in 3-D microscopy image data, using only weakly annotated images as input training material. We update an existing 2-D density kernel estimation approach into 3-D and we introduce novel 3-D features which encapsulate the 3-D neighbourhood surrounding each voxel. The proposed 3-D density kernel estimation (DKE-3-D) method, which utilises an ensemble of random decision trees, is computationally efficient and achieves state-of-the-art performance. DKE-3-D avoids the problem of discrete object identification and segmentation, common to many existing 3-D counting techniques, and we show that it outperforms other methods when quantification of densely packed and heterogeneous objects is desired. In this article we successfully apply the technique to two simulated and to two experimentally derived datasets and show that DKE-3-D has great potential in the biomedical sciences and any field where volumetric datasets are used.

Original languageEnglish
Title of host publicationComputer Vision - ECCV 2016 Workshops, Proceedings
EditorsGang Hua, Hervé Jégou
Number of pages12
ISBN (Print)9783319466033
Publication statusPublished - 18 Sept 2016
EventComputer Vision - ECCV 2016 Workshops, Proceedings - Amsterdam, Netherlands
Duration: 8 Oct 201616 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9913 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceComputer Vision - ECCV 2016 Workshops, Proceedings

Keywords / Materials (for Non-textual outputs)

  • 3-D
  • Counting
  • Density kernel estimation
  • Microscopy
  • Random decision trees


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