Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology

Abstract

Automated detection and segmentation of nuclear and glandular structures is critical for classification and grading of prostate and breast cancer histopathology. In this paper, we present a methodology for automated detection and segmentation of structures of interest in digitized histopathology images. The scheme integrates image information from across three different scales: (1) lowlevel information based on pixel values, (2) high-level information based on relationships between pixels for object detection, and (3) domain-specific information based on relationships between histological structures. Low-level information is utilized by a Bayesian classifier to generate a likelihood that each pixel belongs to an object of interest. High-level information is extracted in two ways: (i) by a level-set algorithm, where a contour is evolved in the likelihood scenes generated by the Bayesian classifier to identify object boundaries, and (ii) by a template matching algorithm, where shape models are used to identify glands and nuclei from the low-level likelihood scenes. Structural constraints are imposed via domainspecific knowledge in order to verify whether the detected objects do indeed belong to structures of interest. In this paper we demonstrate the utility of our glandular and nuclear segmentation algorithm in accurate extraction of various morphological and nuclear features for automated grading of (a) prostate cancer, (b) breast cancer, and (c) distinguishing between cancerous and benign breast histology specimens. The efficacy of our segmentation algorithm is evaluated by comparing breast and prostate cancer grading and benign vs. cancer discrimination accuracies with corresponding accuracies obtained via manual detection and segmentation of glands and nuclei.

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

“…Accuracy of the model is stable over much of the parameter range, with lower accuracy only occurring where lambda forces few to no features to be included in the model. Dundar et al, 2011Dundar et al, , 2010Esgiar et al, 2002;Farjam et al, 2007;Fatima et al, 2014;Glotsos et al, 2008;Gunduz et al, 2004;Gupta et al, 2001;Hall et al, 2008;Huang and Lai, 2010;Huang and Lee, 2009;Jafari-Khouzani and Soltanian-Zadeh, 2003;Monaco and Tomaszewski, 2008;Kong et al, 2007;Kwak et al, 2011;Land et al, 2008;Lessmann et al, 2007;Meng et al, 2010;Naik et al, 2008Naik et al, , 2007Ozolek et al, 2014;Petushi et al, 2006;Qureshi et al, 2008Qureshi et al, , 2007Sboner et al, 2003;Schnorrenberg et al, 1997;Sertel et al, 2010;Sparks and Madabhushi, 2013;Sudbø et al, 2000;Tabesh and Teverovskiy, 2006;Tabesh et al, 2007Tabesh et al, , 2005Tahir and Bouridane, 2006;Tasoulis et al, 2003;Teverovskiy et al, 2004;Thiran and Macq, 1996;Tsai et al, 2009;Xu et al 2014aXu et al , 2014bYang et al, 2009). These approache...…”

“…Since these methods are automatic, they are not subjective and do not require a time commitment from an individual with pathology expertise. This method has most commonly been applied to assigning a Gleason grade to prostate cancer samples (Doyle et al, 2012b;Monaco et al, 2008;Monaco et al, 2010;Naik et al, 2008Naik et al, , 2007Sparks and Madabhushi, 2013) though it has found applications in other cancers as well (Petushi et al, 2006). The Gleason scoring system is particularly well suited to this type of analysis because the system characterizes how ordered the glands are in a prostate tumor.…”

A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis

“…This unique advantage allows the system to develop multiple color and pixel identifiers from digital images of HE stained pancreatic sections of uninjured and injured tissue. Similar technology has recently been used in several other pathology applications, including the identification of tumor cell nuclei in breast and prostate cancer [10,29,30] and apoptosis in cell lines [31].…”

“…Recently, computer driven segmentation algorithms have arisen to help automate the analysis of digital images [9,10,11]. Several commercial applications are available (i.e.…”

Whole slide image cytometry: a novel method to detect abnormal DNA content in Barrett's esophagus

“…Many nuclei segmentation methods from tissue sections are available in the literature, but they are largely focused on the processing of H&E-stained tissue for, e.g., prostate, 47 neuroblastoma, 48 and laryngeal tissue. 49 Given the morphological characteristics of Feulgen-stained esophageal tissue, we developed a marker-controlled watershed-based nuclei segmentation algorithm using the Matlab (version 8) software.…”