Computer aided analysis of prostate histopathology images Gleason grading especially for Gleason score 7

Abstract

Clinically, prostate adenocarcinoma is diagnosed by recognizing certain morphology on histology. While the Gleason grading system has been shown to be the strongest prognostic factor for men with prostrate adenocarcinoma, there is a significant intra and interobserver variability between pathologists in assigning this grading system. In this study, we present a new method for prostate gland segmentation from which we then utilize to develop a computer aided Gleason grading. The novelty of our method is a region-based nuclei segmentation to get individual gland without using lumen as prior information. Because each gland region is surrounded by nuclei, individual gland can be segmented by using the structure features and Delaunay Triangulation. The precision, recal and F1 of this approach are 0.94±0.11, 0.60±0.23 and 0.70±0.19 respectively. Our method achieves a high accuracy for prostate gland segmentation with less computation time.

Predicting cancer outcomes from histology and genomics using convolutional networks

“…However, human assessments of histology are highly subjective and not repeatable, hence computational analysis of histology imaging has received significant attention. Aided by advances in slide scanning microscopes and computing, a number of image analysis algorithms have been developed for grading (1)(2)(3)(4), classification (5)(6)(7)(8)(9)(10), and prediction of future metastasis (11) in multiple cancer types.…”

Predicting cancer outcomes from histology and genomics using convolutional networks

“…However, human assessments of histology are highly subjective and are not repeatable; hence, computational analysis of histology imaging has received significant attention. Aided by advances in slide scanning microscopes and computing, a number of image analysis algorithms have been developed for grading ( 1 – 4 ), classification ( 5 – 10 ), and identification of lymph node metastases ( 11 ) in multiple cancer types.…”

Informatics Approaches to Address New Challenges in the Classification of Lymphoid Malignancies

“…Advances in digital pathology-based informatics hold promise to facilitate more accurate and objective classification and grading of malignancies, as reflected in studies of cancers from many primary sites, including brain, 7–9 renal, 10 prostate, 11 breast, 12–15 and lung. Computer-assisted image analysis is emerging as an important tool in evaluating lymphoid neoplasms, and although many strides have been made in assessing this disease, additional improvements are essential.…”