Automated nuclear segmentation of coherent anti-stokes Raman scattering microscopy images by coupling superpixel context information with artificial neural networks

Ahmad A. Hammoudi, Fuhai Li, Liang Gao, Zhiyong Wang, Michael J. Thrall, Yehia Massoud, Stephen T.C. Wong

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

10 Scopus citations

Abstract

Coherent anti-Stokes Raman scattering (CARS) microscopy is attracting major scientific attention because its high-resolution, label-free properties have great potential for real time cancer diagnosis during an image-guided-therapy process. In this study, we develop a nuclear segmentation technique which is essential for the automated analysis of CARS images in differential diagnosis of lung cancer subtypes. Thus far, no existing automated approaches could effectively segment CARS images due to their low signal-to-noise ratio (SNR) and uneven background. Naturally, manual delineation of cellular structures is time-consuming, subject to individual bias, and restricts the ability to process large datasets. Herein we propose a fully automated nuclear segmentation strategy by coupling superpixel context information and an artificial neural network (ANN), which is, to the best of our knowledge, the first automated nuclear segmentation approach for CARS images. The superpixel technique for local clustering divides an image into small patches by integrating the local intensity and position information. It can accurately separate nuclear pixels even when they possess subtly lower contrast with the background. The resulting patches either correspond to cell nuclei or background. To separate cell nuclei patches from background ones, we introduce the rayburst shape descriptors, and define a superpixel context index that combines information from a given superpixel and it's immediate neighbors, some of which are background superpixels with higher intensity. Finally we train an ANN to identify the nuclear superpixels from those corresponding to background. Experimental validation on three subtypes of lung cancers demonstrates that the proposed approach is fast, stable, and accurate for segmentation of CARS images, the first step in the clinical use of CARS for differential cancer analysis.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings
Pages317-325
Number of pages9
DOIs
StatePublished - Oct 17 2011
Event2nd International Workshop on Machine Learning in Medical Imaging, MLMI 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: Sep 18 2011Sep 18 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7009 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Machine Learning in Medical Imaging, MLMI 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011
CountryCanada
CityToronto, ON
Period09/18/1109/18/11

Keywords

  • Artificial Neural Network (ANN)
  • Coherent anti-Stokes Raman scattering (CARS) microscopy
  • Nuclear segmentation
  • Superpixels

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    Hammoudi, A. A., Li, F., Gao, L., Wang, Z., Thrall, M. J., Massoud, Y., & Wong, S. T. C. (2011). Automated nuclear segmentation of coherent anti-stokes Raman scattering microscopy images by coupling superpixel context information with artificial neural networks. In Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings (pp. 317-325). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7009 LNCS). https://doi.org/10.1007/978-3-642-24319-6_39