title: Supervised learning framework for screening nuclei in tissue sections.
abstract: Accurate segmentation of cell nuclei in microscope images of tissue sections is a key step in a number of biological and clinical applications. Often such applications require analysis of large image datasets for which manual segmentation becomes subjective and time consuming. Hence automation of the segmentation steps using fast, robust and accurate image analysis and pattern classification techniques is necessary for high throughput processing of such datasets. We describe a supervised learning framework, based on artificial neural networks (ANNs), to identify well-segmented nuclei in tissue sections from a multistage watershed segmentation algorithm. The successful automation was demonstrated by screening over 1400 well segmented nuclei from 9 datasets of human breast tissue section images and comparing the results to a previously used stacked classifier based analysis framework.
authors.affiliation: Optical Microscopy and Analysis Laboratory, Advanced Technology Program, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD 21702, USA. nandyk@mail.nih.gov
authors.forename: Kaustav
authors.initials: K
authors.lastname: Nandy
authors.forename: Prabhakar R
authors.initials: PR
authors.lastname: Gudla
authors.forename: Ryan
authors.initials: R
authors.lastname: Amundsen
authors.forename: Karen J
authors.initials: KJ
authors.lastname: Meaburn
authors.forename: Tom
authors.initials: T
authors.lastname: Misteli
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Lockett
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001940:Breast / Q000648:ultrastructure*; D002467:Cell Nucleus / Q000648:ultrastructure*; D002478:Cells, Cultured; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity