Computer-aided identification of prostatic adenocarcinoma: Segmentation of glandular structures

Abstract

Background: Identification of individual prostatic glandular structures is an important prerequisite to quantitative histological analysis of prostate cancer with the aid of a computer. We have developed a computer method to segment individual glandular units and to extract quantitative image features, for computer identification of prostatic adenocarcinoma.

Methods: TWO SETS OF DIGITAL HISTOLOGY IMAGES WERE USED: database I (n = 57) for developing and testing the computer technique, and database II (n = 116) for independent validation. The segmentation technique was based on a k-means clustering and a region-growing method. Computer segmentation results were evaluated subjectively and also compared quantitatively against manual gland outlines, using the Jaccard similarity measure. Quantitative features that were extracted from the computer segmentation results include average gland size, spatial gland density, and average gland circularity. Linear discriminant analysis (LDA) was used to combine quantitative image features. Classification performance was evaluated with receiver operating characteristic (ROC) analysis and the area under the ROC curve (AUC).

Results: Jaccard similarity coefficients between computer segmentation and manual outlines of individual glands were between 0.63 and 0.72 for non-cancer and between 0.48 and 0.54 for malignant glands, respectively, similar to an interobserver agreement of 0.79 for non-cancer and 0.75 for malignant glands, respectively. The AUC value for the features of average gland size and gland density combined via LDA was 0.91 for database I and 0.96 for database II.

Conclusions: Using a computer, we are able to delineate individual prostatic glands automatically and identify prostatic adenocarcinoma accurately, based on the quantitative image features extracted from computer-segmented glandular structures.

Keywords: Computer-aided classification; digital histology images; feature analysis; image segmentation; prostatic adenocarcinoma.

Figure 1

Digital histology images of (a) benign glands and (b) malignant adenocarcinoma glands. L = glandular lumen; S = stroma; and E = prostatic epithelium

Figure 2

Flowchart of computer gland segmentation techniques

Figure 3

Example images of (upper left) original image (after artifact correction), (upper right) computer gland segmentation results, (lower left) researcher A's manual outlines of glands, and (lower right) researcher B's manual outlines of glands

Figure 4

Subjective and qualitative evaluation of computer segmentation results by (left) a pathologist and (right) a researcher. Shown are histograms of (top) overall accuracy, and (bottom) estimates of false-negative and false-positive glands in the computer segmentation results

Figure 5

Comparison of Jaccard coefficients (left) between repeated gland identification by researcher A (intraobserver comparison) and between glands identified by researchers A and B (interobserver comparison); (middle) between repeated gland identification by researcher A (intraobserver comparison) and between glands identified by the computer and researcher B (human-computer comparison); and (right) between glands identified by the computer and both researchers (human-computer comparisons)

Figure 6

Receiver operating characteristic (ROC) curves of the individual and combined glandular features calculated from computer outlines of individual prostatic glands in images of (left) databases I, and (right) II. Area under the ROC curve (AUC) can be interpreted as a summary index of classification performance. An AUC value of 0.5 indicates a ‘random call,’ whereas an AUC value of 1.0 indicates perfect separation of non-cancer and cancer glands