title: Separation of benign and malignant glands in prostatic adenocarcinoma.
abstract: This paper presents an analysis of the high resolution histopathology images of the prostate with a focus on the evolution of morphological gland features in prostatic adenocarcinoma. Here we propose a novel technique of labeling individual glands as malignant or benign. In the first step, the gland and nuclei objects of the images are automatically segmented. Individual gland units are segmented out by consolidating their lumina with the surrounding layers of epithelium and nuclei. The nuclei objects are segmented by using a marker controlled watershed algorithm. Two new features, Number of Nuclei Layer (N(NL)) and Ratio of Epithelial layer area to Lumen area (R(EL)) have been extracted from the segmented units. The main advantage of this approach is that it can detect individual malignant gland units, irrespective of neighboring histology and/or the spatial extent of the cancer. The proposed algorithm has been tested on 40 histopathology scenes taken from 10 high resolution whole mount images and achieved a sensitivity of 0.83 and specificity of 0.81 in a leave-75%-out cross-validation.
authors: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
authors: Sabrina
authors: S
authors: Rashid
authors: The Vancouver Prostate Center, University of British Columbia, Vancouver, BC, Canada.
authors: Ladan
authors: L
authors: Fazli
authors: Kingston General Hospital, Kingston, ON, Canada.
authors: Alexander
authors: A
authors: Boag
authors: Kingston General Hospital, Kingston, ON, Canada.
authors: Robert
authors: R
authors: Siemens
authors: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
authors: Purang
authors: P
authors: Abolmaesumi
authors: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
authors: Septimiu E
authors: SE
authors: Salcudean
mesh_terms: D000230:Adenocarcinoma / Q000473:pathology*; D000465:Algorithms; D001185:Artificial Intelligence; D002467:Cell Nucleus / Q000473:pathology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008297:Male; D008853:Microscopy / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011471:Prostatic Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D060388:Support Vector Machine