Gland segmentation in prostate histopathological images

Abstract

Glandular structural features are important for the tumor pathologist in the assessment of cancer malignancy of prostate tissue slides. The varying shapes and sizes of glands combined with the tedious manual observation task can result in inaccurate assessment. There are also discrepancies and low-level agreement among pathologists, especially in cases of Gleason pattern 3 and pattern 4 prostate adenocarcinoma. An automated gland segmentation system can highlight various glandular shapes and structures for further analysis by the pathologist. These objective highlighted patterns can help reduce the assessment variability. We propose an automated gland segmentation system. Forty-three hematoxylin and eosin-stained images were acquired from prostate cancer tissue slides and were manually annotated for gland, lumen, periacinar retraction clefting, and stroma regions. Our automated gland segmentation system was trained using these manual annotations. It identifies these regions using a combination of pixel and object-level classifiers by incorporating local and spatial information for consolidating pixel-level classification results into object-level segmentation. Experimental results show that our method outperforms various texture and gland structure-based gland segmentation algorithms in the literature. Our method has good performance and can be a promising tool to help decrease interobserver variability among pathologists.

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

“…Thus, the whole histopathology image is often divided into partial regions of about 1024 × 1024 pixels called patches, where each patch is examined apart, such as detecting region-of-interests [ 56 ]. Thus, many studies such as [ 16 , 24 , 25 , 26 , 27 , 48 , 57 , 58 ] presented in this survey, especially those dealing with deep learning applied patching technique to overcome the extremely large histopathological images.…”

“…Conventional machine learning techniques applied in HI analysis typically involve several preprocessing steps, including feature selection, image segmentation and classification. ML techniques have been reviewed extensively in the literature, for instance in [ 2 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. In the last decade, researchers have turned their focus towards the development of new deep learning techniques as they outperform conventional machine learning techniques in diverse fields and not only HI image analysis.…”

Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard

“…Singh et al . 10 employed a multi-step approach based on logistic regression to segment epithelium, distinguishing between glands, lumen, peri-acinar retraction clefting and stroma. Both Gertych et al .…”

Glandular Segmentation of Prostate Cancer: An Illustration of How the Choice of Histopathological Stain Is One Key to Success for Computational Pathology

“…Nguyen et al (2012) also start with the lumen and grow that structure to include the epithelial nuclei. Singh et al (2017) manually annotate gland, lumen, periacinar refraction, and stroma in H&E-stained tissue images, and train a segmentation algorithm on these manual annotations using standard machine learning techniques. The segmentation process continues by region-growing from a seed inside the glands toward the epithelial nuclei.…”