Integrated segmentation of cellular structures

Peter Ajemba, Yousef Al-Kofahi, Richard Scott, Michael Donovan, Gerardo Fernandez

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

2 Scopus citations

Abstract

Automatic segmentation of cellular structures is an essential step in image cytology and histology. Despite substantial progress, better automation and improvements in accuracy and adaptability to novel applications are needed. In applications utilizing multi-channel immuno-fluorescence images, challenges include misclassification of epithelial and stromal nuclei, irregular nuclei and cytoplasm boundaries, and over and under-segmentation of clustered nuclei. Variations in image acquisition conditions and artifacts from nuclei and cytoplasm images often confound existing algorithms in practice. In this paper, we present a robust and accurate algorithm for jointly segmenting cell nuclei and cytoplasm using a combination of ideas to reduce the aforementioned problems. First, an adaptive process that includes top-hat filtering, Eigenvalues-of-Hessian blob detection and distance transforms is used to estimate the inverse illumination field and correct for intensity non-uniformity in the nuclei channel. Next, a minimum-error-thresholding based binarization process and seed-detection combining Laplacian-of-Gaussian filtering constrained by a distance-map-based scale selection is used to identify candidate seeds for nuclei segmentation. The initial segmentation using a local maximum clustering algorithm is refined using a minimum-error-thresholding technique. Final refinements include an artifact removal process specifically targeted at lumens and other problematic structures and a systemic decision process to reclassify nuclei objects near the cytoplasm boundary as epithelial or stromal. Segmentation results were evaluated using 48 realistic phantom images with known ground-truth. The overall segmentation accuracy exceeds 94%. The algorithm was further tested on 981 images of actual prostate cancer tissue. The artifact removal process worked in 90% of cases. The algorithm has now been deployed in a high-volume histology analysis application.

Original languageEnglish
Title of host publicationMedical Imaging 2011
Subtitle of host publicationImage Processing
DOIs
StatePublished - 2011
Externally publishedYes
EventMedical Imaging 2011: Image Processing - Lake Buena Vista, FL, United States
Duration: 14 Feb 201116 Feb 2011

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7962
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2011: Image Processing
Country/TerritoryUnited States
CityLake Buena Vista, FL
Period14/02/1116/02/11

Keywords

  • Cell nuclei
  • cellular structures
  • cytology
  • histology
  • histopathology
  • segmentation

Fingerprint

Dive into the research topics of 'Integrated segmentation of cellular structures'. Together they form a unique fingerprint.

Cite this