Computer aided analysis of prostate histopathology images to support a refined Gleason grading system

Abstract

The Gleason grading system used to render prostate cancer diagnosis has recently been updated to allow more accurate grade stratification and higher prognostic discrimination when compared to the traditional grading system. In spite of progress made in trying to standardize the grading process, there still remains approximately a 30% grading discrepancy between the score rendered by general pathologists and those provided by experts while reviewing needle biopsies for Gleason pattern 3 and 4, which accounts for more than 70% of daily prostate tissue slides at most institutions. We propose a new computational imaging method for Gleason pattern 3 and 4 classification, which better matches the newly established prostate cancer grading system. The computer-aided analysis method includes two phases. First, the boundary of each glandular region is automatically segmented using a deep convolutional neural network. Second, color, shape and texture features are extracted from superpixels corresponding to the outer and inner glandular regions and are subsequently forwarded to a random forest classifier to give a gradient score between 3 and 4 for each delineated glandular region. The F ₁ score for glandular segmentation is 0.8460 and the classification accuracy is 0.83±0.03.

Keywords: Gleason grading; convolutional neural network; histopathology segmentation; random forest; regression.

Fig. 1

The architecture of the semantic segmentation network

Fig. 2

Each test image is mirrored by four boundary sub-images in order to retain the boundary information. And each test image is cropped into several sub-images. Only the center of each predicted sub-image mask is kept to form the preliminary mask

Fig. 3

(a) original image; (b) superpixel segmentation on the original image; (c) distance map of the original image; (d) image contains boundary information; (e) image contains center information

Fig. 4

Results are shown for different methods. The approach in this article performs better than segment using structure and context[17] and segment using region-based nuclei approach[18]. A score is given for each gland after segmentation