Prostate Histopathology: Learning Tissue Component Histograms for Cancer Detection and Classification

Abstract

Abstract-Radical prostatectomy is performed on approximately 40% of men with organ-confined prostate cancer. Pathologic information obtained from the prostatectomy specimen provides important prognostic information and guides recommendations for adjuvant treatment. The current pathology protocol in most centers involves primarily qualitative assessment. In this paper, we describe and evaluate our system for automatic prostate cancer detection and grading on hematoxylin & eosin-stained tissue images. Our approach is intended to address the dual challenges of large data size and the need for high-level tissue information about the locations and grades of tumors. Our system uses two stages of AdaBoost-based classification. The first provides high-level tissue component labeling of a superpixel image partitioning. The second uses the tissue component labeling to provide a classification of cancer versus noncancer, and low-grade versus high-grade cancer. We evaluated our system using 991 sub-images extracted from digital pathology images of 50 whole-mount tissue sections from 15 prostatectomy patients. We measured accuracies of 90% and 85% for the cancer versus noncancer and high-grade versus low-grade classification tasks, respectively. This system represents a first step toward automated cancer quantification on prostate digital histopathology imaging, which could pave the way for more accurately informed postprostatectomy patient care.

Review on Brain Tumor Segmentation and Classification Techniques

“…They evaluated their system using 991 sub-images extracted from digital pathology images of 50 whole-mount tissue sections from 15 prostatectomy patients. They measured accuracies of 90% and 85% for the cancer versus non-cancer and high-grade versus low-grade classification tasks, respectively [21]. M. G. Kounelakis et.al.…”

Detecting and Classifying Nuclei on a Budget

“…For instance, the amount of different types of cells in prostate tissue (epithelial, fibroblast, etc.) strongly correlates to the Gleason grade of prostate cancer [3,5]. Lee et al [7] show that nuclei orientation entropy in prostatectomies is a predictor of biochemical recurrence in cancer patients.…”

Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens

“…In addition to the morphology of nuclei, tissue structure features are important in histopathology diagnosis. Structural features such as the gland or lumen are used in the automated image analysis of prostate and breast cancers; [6][7][8] however, they are also important in the diagnosis of liver cancer. 9,10 In HCC diagnosis, 11,12 the cord-like structure of the hepatocytes called the trabecula is of high importance.…”