A fully automated approach to prostate biopsy segmentation based on level-set and mean filtering

Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

One of the most dangerous diseases that threaten people is cancer. If diagnosed in earlier stages, cancer, with its life-threatening consequences, has the possibility of eradication. In addition, accuracy in prediction plays a significant role. Hence, developing a reliable model that contributes much towards the medical community in the early diagnosis of biopsy images with perfect accuracy comes to the forefront. This article aims to develop better predictive models using multivariate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimisation (SSO) algorithm-tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the neural network classifier by the SSO algorithm. The performance of the proposed strategy is analysed with performance metrics such as accuracy, sensitivity, specificity, and MCC measures, and the attained results are 95.9181%, 94.2515%, 97.125%, and 97.68%, respectively, which shows the effectiveness of the proposed method for cancer disease diagnosis.

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

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.

Machine learning approaches to analyze histological images of tissues from radical prostatectomies

Computerized evaluation of histological preparations of prostate tissues involves identification of tissue components such as stroma (ST), benign/normal epithelium (BN) and prostate cancer (PCa). Image classification approaches have been developed to identify and classify glandular regions in digital images of prostate tissues; however their success has been limited by difficulties in cellular segmentation and tissue heterogeneity. We hypothesized that utilizing image pixels to generate intensity histograms of hematoxylin (H) and eosin (E) stains deconvoluted from H&E images numerically captures the architectural difference between glands and stroma. In addition, we postulated that joint histograms of local binary patterns and local variance (LBPxVAR) can be used as sensitive textural features to differentiate benign/normal tissue from cancer. Here we utilized a machine learning approach comprising of a support vector machine (SVM) followed by a random forest (RF) classifier to digitally stratify prostate tissue into ST, BN and PCa areas. Two pathologists manually annotated 210 images of low- and high-grade tumors from slides that were selected from 20 radical prostatectomies and digitized at high-resolution. The 210 images were split into the training (n=19) and test (n=191) sets. Local intensity histograms of H and E were used to train a SVM classifier to separate ST from epithelium (BN+PCa). The performance of SVM prediction was evaluated by measuring the accuracy of delineating epithelial areas. The Jaccard J=59.5 ± 14.6 and Rand Ri=62.0 ± 7.5 indices reported a significantly better prediction when compared to a reference method (Chen et al., Clinical Proteomics 2013, 10:18) based on the averaged values from the test set. To distinguish BN from PCa we trained a RF classifier with LBPxVAR and local intensity histograms and obtained separate performance values for BN and PCa: JBN=35.2 ± 24.9, OBN=49.6 ± 32, JPCa=49.5 ± 18.5, OPCa=72.7 ± 14.8 and Ri=60.6 ± 7.6 in the test set. Our pixel-based classification does not rely on the detection of lumens, which is prone to errors and has limitations in high-grade cancers and has the potential to aid in clinical studies in which the quantification of tumor content is necessary to prognosticate the course of the disease. The image data set with ground truth annotation is available for public use to stimulate further research in this area.

An optimized color transformation for the analysis of digital images of hematoxylin & eosin stained slides

Hematoxylin and eosin (H&E) staining is ubiquitous in pathology practice and research. As digital pathology has evolved, the reliance of quantitative methods that make use of H&E images has similarly expanded. For example, cell counting and nuclear morphometry rely on the accurate demarcation of nuclei from other structures and each other. One of the major obstacles to quantitative analysis of H&E images is the high degree of variability observed between different samples and different laboratories. In an effort to characterize this variability, as well as to provide a substrate that can potentially mitigate this factor in quantitative image analysis, we developed a technique to project H&E images into an optimized space more appropriate for many image analysis procedures. We used a decision tree-based support vector machine learning algorithm to classify 44 H&E stained whole slide images of resected breast tumors according to the histological structures that are present. This procedure takes an H&E image as an input and produces a classification map of the image that predicts the likelihood of a pixel belonging to any one of a set of user-defined structures (e.g., cytoplasm, stroma). By reducing these maps into their constituent pixels in color space, an optimal reference vector is obtained for each structure, which identifies the color attributes that maximally distinguish one structure from other elements in the image. We show that tissue structures can be identified using this semi-automated technique. By comparing structure centroids across different images, we obtained a quantitative depiction of H&E variability for each structure. This measurement can potentially be utilized in the laboratory to help calibrate daily staining or identify troublesome slides. Moreover, by aligning reference vectors derived from this technique, images can be transformed in a way that standardizes their color properties and makes them more amenable to image processing.

Prostate cancer grading: use of graph cut and spatial arrangement of nuclei

Tissue image grading is one of the most important steps in prostate cancer diagnosis, where the pathologist relies on the gland structure to assign a Gleason grade to the tissue image. In this grading scheme, the discrimination between grade 3 and grade 4 is the most difficult, and receives the most attention from researchers. In this study, we propose a novel method (called nuclei-based method) that 1) utilizes graph theory techniques to segment glands and 2) computes a gland-score (based on the spatial arrangement of nuclei) to estimate how similar a segmented region is to a gland. Next, we create a fusion method by combining this nuclei-based method with the lumen-based method presented in our previous work to improve the performance of grade 3 versus grade 4 classification problem (the accuracy is now improved to 87.3% compared to 81.1% of the lumen-based method alone). To segment glands, we build a graph of nuclei and lumina in the image, and use the normalized cut method to partition the graph into different components, each corresponding to a gland. Unlike most state-of-the-art lumen-based gland segmentation method, the nuclei-based method is able to segment glands without lumen or glands with multiple lumina. Moreover, another important contribution in this research is the development of a set of measures to exploit the difference in nuclei spatial arrangement between grade 3 images (where nuclei form closed chain structure on the gland boundary) and grade 4 image (where nuclei distribute more randomly in the gland). These measures are combined to generate a single gland-score value, which estimates how similar a segmented region (which is a set of nuclei and lumina) is to a gland.

Pathology Imaging Informatics for Clinical Practice and Investigative and Translational Research

Pathologists routinely interpret gross and microscopic specimens to render diagnoses and to engage in a broad spectrum of investigative research. Multiple studies have demonstrated that imaging technologies have progressed to a level at which properly digitized specimens provide sufficient quality comparable to the traditional glass slides examinations. Continued advancements in this area will have a profound impact on the manner in which pathology is conducted from this point on. Several leading institutions have already undertaken ambitious projects directed toward digitally imaging, archiving, and sharing pathology specimens. As a result of these advances, the use of informatics in diagnostic and investigative pathology applications is expanding rapidly. In addition, the advent of novel technologies such as multispectral imaging makes it possible to visualize and analyze imaged specimens using multiple wavelengths simultaneously. As these powerful technologies become increasingly accepted and adopted, the opportunities for gaining new insight into the underlying mechanisms of diseases as well as the potential for discriminating among subtypes of pathologies are growing accordingly.