Automatic tissue segmentation of breast biopsies imaged by QPI

Hassaan Majeed, Tan Nguyen, Mikhail Kandel, Virgilia Marcias, Minh Do, Krishnarao Tangella, Andre Balla, Gabriel Popescu

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

Abstract

The current tissue evaluation method for breast cancer would greatly benefit from higher throughput and less inter-observer variation. Since quantitative phase imaging (QPI) measures physical parameters of tissue, it can be used to find quantitative markers, eliminating observer subjectivity. Furthermore, since the pixel values in QPI remain the same regardless of the instrument used, classifiers can be built to segment various tissue components without need for color calibration. In this work we use a texton-based approach to segment QPI images of breast tissue into various tissue components (epithelium, stroma or lumen). A tissue microarray comprising of 900 unstained cores from 400 different patients was imaged using Spatial Light Interference Microscopy. The training data were generated by manually segmenting the images for 36 cores and labelling each pixel (epithelium, stroma or lumen.). For each pixel in the data, a response vector was generated by the Leung-Malik (LM) filter bank and these responses were clustered using the k-means algorithm to find the centers (called textons). A random forest classifier was then trained to find the relationship between a pixel's label and the histogram of these textons in that pixel's neighborhood. The segmentation was carried out on the validation set by calculating the texton histogram in a pixel's neighborhood and generating a label based on the model learnt during training. Segmentation of the tissue into various components is an important step toward efficiently computing parameters that are markers of disease. Automated segmentation, followed by diagnosis, can improve the accuracy and speed of analysis leading to better health outcomes.

Original languageEnglish (US)
Title of host publicationQuantitative Phase Imaging II
EditorsGabriel Popescu, YongKeun Park
PublisherSPIE
ISBN (Electronic)9781628419528
DOIs
StatePublished - Jan 1 2016
Event2nd Conference on Quantitative Phase Imaging, QPI 2016 - San Francisco, United States
Duration: Feb 14 2016Feb 17 2016

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9718
ISSN (Print)1605-7422

Other

Other2nd Conference on Quantitative Phase Imaging, QPI 2016
CountryUnited States
CitySan Francisco
Period2/14/162/17/16

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Keywords

  • Interferometry
  • SLIM
  • histopathology
  • image segmentation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Cite this

Majeed, H., Nguyen, T., Kandel, M., Marcias, V., Do, M., Tangella, K., Balla, A., & Popescu, G. (2016). Automatic tissue segmentation of breast biopsies imaged by QPI. In G. Popescu, & Y. Park (Eds.), Quantitative Phase Imaging II [971817] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9718). SPIE. https://doi.org/10.1117/12.2209142