A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes. (September 2019)

• Record Type: Journal Article
• Authors: Esteban, Ángel E.; López-Pérez, Miguel; Colomer, Adrián; Sales, María A.; Molina, Rafael; Naranjo, Valery
• Is Part Of: Computer methods and programs in biomedicine. Volume 178(2019)
• Page Start: 303
• Page End: 317
• Publication Date: 2019-09
• Subjects: Prostate cancer -- Histopathological images -- Gaussian processes -- Variational inference -- Granulometries -- Deep Gaussian processes; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.07.003
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A public database of annotated prostate cancer images from the Clinical Hospital of Valencia. We demonstrate the importance of optical density images in color deconvolution to encode the relevant features of prostate cancer. We formulate a novel morphological descriptor based on granulometries for prostate cancer classification. We introduce probabilistic models based on shallow and deep Gaussian Processes to address the discrimination between healthy and tumoral prostate tissue. A fast and automatic method that detects almost perfectly prostate cancer on Whole Slide Images providing a useful tool to pathologists. Abstract: Background and objective: Prostate cancer is one of the most common male tumors. The increasing use of whole slide digital scanners has led to an enormous interest in the application of machine learning techniques to histopathological image classification. Here we introduce a novel family of morphological descriptors which, extracted in the appropriate image space and combined with shallow and deep Gaussian process based classifiers, improves early prostate cancer diagnosis. Method: We decompose the acquired RGB image in its RGB and optical density hematoxylin and eosin components. Then, we define two novel granulometry-based descriptors which work in both, RGB and optical density, spaces but perform better when used on the latter. In this space they clearly encapsulate knowledge used by pathologists to identify cancer lesions. The obtained features become the inputs to shallow and deep Gaussian process classifiers which achieve an accurate prediction of cancer. Results: We have used a real and unique dataset. The dataset is composed of 60 Whole Slide Images. For a five fold cross validation, shallow and deep Gaussian Processes obtain area under ROC curve values higher than 0.98. They outperform current state of the art patch based shallow classifiers and are very competitive to the best performing deep learning method. Models were also compared on 17 Whole Slide test Images using the FROC curve. With the cost of one false positive, the best performing method, the one layer Gaussian process, identifies 83.87% (sensitivity) of all annotated cancer in the Whole Slide Image. This result corroborates the quality of the extracted features, no more than a layer is needed to achieve excellent generalization results. Conclusion: Two new descriptors to extract morphological features from histological images have been proposed. They collect very relevant information for cancer detection. From these descriptors, shallow and deep Gaussian Processes are capable of extracting the complex structure of prostate histological images. The new space/descriptor/classifier paradigm outperforms state-of-art shallow classifiers. Furthermore, despite being much simpler, it is competitive to state-of-art CNN architectures both on the proposed SICAPv1 database and on an external database.