title: A fast, automatic segmentation algorithm for locating and delineating touching cell boundaries in imaged histopathology.
abstract: BACKGROUND
  Automated analysis of imaged histopathology specimens could potentially provide support for improved reliability in detection and classification in a range of investigative and clinical cancer applications. Automated segmentation of cells in the digitized tissue microarray (TMA) is often the prerequisite for quantitative analysis. However overlapping cells usually bring significant challenges for traditional segmentation algorithms.
  
  
  OBJECTIVE
  In this paper, we propose a novel, automatic algorithm to separate overlapping cells in stained histology specimens acquired using bright-field RGB imaging.
  
  
  METHODS
  It starts by systematically identifying salient regions of interest throughout the image based upon their underlying visual content. The segmentation algorithm subsequently performs a quick, voting based seed detection. Finally, the contour of each cell is obtained using a repulsive level set deformable model using the seeds generated in the previous step. We compared the experimental results with the most current literature, and the pixel wise accuracy between human experts' annotation and those generated using the automatic segmentation algorithm.
  
  
  RESULTS
  The method is tested with 100 image patches which contain more than 1000 overlapping cells. The overall precision and recall of the developed algorithm is 90% and 78%, respectively. We also implement the algorithm on GPU. The parallel implementation is 22 times faster than its C/C++ sequential implementation.
  
  
  CONCLUSIONS
  The proposed segmentation algorithm can accurately detect and effectively separate each of the overlapping cells. GPU is proven to be an efficient parallel platform for overlapping cell segmentation.
authors.affiliation: 1Department of Pathology and Laboratory Medicine, UMDNJ-Robert Wood Johnson Medical School, Piscataway, NJ 08854, USA. qixi@umdnj.edu
authors.forename: X
authors.initials: X
authors.lastname: Qi
authors.forename: F
authors.initials: F
authors.lastname: Xing
authors.forename: D J
authors.initials: DJ
authors.lastname: Foran
authors.forename: L
authors.initials: L
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis / Q000473:pathology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D008490:Medical Informatics / Q000295:instrumentation* / Q000379:methods; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D014481:United States