Automated analysis of necrosis and steatosis in histological images : Practical solutions for coping with heterogeneity and variability

Pathological examination of histological tissue sections is essential for the diagnosis of many life-threatening diseases. Demographic change and the growing importance of precision medicine require pathology to become more efficient, reproducible and quantitative. Automated histological image analysis is an important tool to meet these demands. This thesis is based on five research papers that consider specific problems in histological image analysis. The problems are related either to the quantification of necrosis or to the quantification of steatosis in histological sections of liver tissue. Both are typical applications in which tissue structures or cellular structures must be identified and quantitatively analyzed. In this context, the papers address important general challenges in histological image analysis and present broadly applicable solutions. One challenge is spatial heterogeneity of tissue properties, which can make their quantification sensitive to tissue sampling and image analysis errors. As a solution, the papers present novel scores that enable reliable measurement of heterogeneously distributed tissue properties. Another challenge is the huge variability of histological images, which can make machine learning-based analysis methods require large amounts of training data to work robustly. As a solution, the papers show how interactive training can produce accurate results with little training effort. Finally, a practical challenge is achieving a good trade-off between accuracy, efficiency, and simplicity. In this regard, the papers describe pragmatic approaches to enable accurate and fast analysis of gigapixel images on standard computers

Digitization of Pathology Labs: A Review of Lessons Learned

Pathology laboratories are increasingly using digital workflows. This has the potential of increasing lab efficiency, but the digitization process also involves major challenges. Several reports have been published describing the individual experiences of specific laboratories with the digitization process. However, a comprehensive overview of the lessons learned is still lacking. We provide an overview of the lessons learned for different aspects of the digitization process, including digital case management, digital slide reading, and computer-aided slide reading. We also cover metrics used for monitoring performance and pitfalls and corresponding values observed in practice. The overview is intended to help pathologists, IT decision-makers, and administrators to benefit from the experiences of others and to implement the digitization process in an optimal way to make their own laboratory future-proof.Comment: 22 pages, 1 figur

The NCI Imaging Data Commons as a platform for reproducible research in computational pathology

Background and Objectives: Reproducibility is a major challenge in developing machine learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging Data Commons (IDC) provides >120 cancer image collections according to the FAIR principles and is designed to be used with cloud ML services. Here, we explore its potential to facilitate reproducibility in CompPath research. Methods: Using the IDC, we implemented two experiments in which a representative ML-based method for classifying lung tumor tissue was trained and/or evaluated on different datasets. To assess reproducibility, the experiments were run multiple times with separate but identically configured instances of common ML services. Results: The AUC values of different runs of the same experiment were generally consistent. However, we observed small variations in AUC values of up to 0.045, indicating a practical limit to reproducibility. Conclusions: We conclude that the IDC facilitates approaching the reproducibility limit of CompPath research (i) by enabling researchers to reuse exactly the same datasets and (ii) by integrating with cloud ML services so that experiments can be run in identically configured computing environments.Comment: 13 pages, 5 figures; improved manuscript, new experiments with P100 GP

Focused scores enable reliable discrimination of small differences in steatosis

Background: Automated image analysis enables quantitative measurement of steatosis in histological images. However, spatial heterogeneity of steatosis can make quantitative steatosis scores unreliable. To improve the reliability, we have developed novel scores that are “focused” on steatotic tissue areas. Methods: Focused scores use concepts of tile-based hotspot analysis in order to compute statistics about steatotic tissue areas in an objective way. We evaluated focused scores on three data sets of images of rodent liver sections exhibiting different amounts of dietary-induced steatosis. The same evaluation was conducted with the standard steatosis score computed by most image analysis methods. Results: The standard score reliably discriminated large differences in steatosis (intraclass correlation coefficient ICC = 0.86), but failed to discriminate small (ICC = 0.54) and very small (ICC = 0.14) differences. With an appropriate tile size, mean-based focused scores reliably discriminated large (ICC = 0.92), small (ICC = 0.86) and very small (ICC = 0.83) differences. Focused scores based on high percentiles showed promise in further improving the discrimination of very small differences (ICC = 0.93). Conclusions: Focused scores enable reliable discrimination of small differences in steatosis in histological images. They are conceptually simple and straightforward to use in research studies

Data-Driven Discovery of Immune Contexture Biomarkers

Background: Features characterizing the immune contexture (IC) in the tumor microenvironment can be prognostic and predictive biomarkers. Identifying novel biomarkers can be challenging due to complex interactions between immune and tumor cells and the abundance of possible features.Methods: We describe an approach for the data-driven identification of IC biomarkers. For this purpose, we provide mathematical definitions of different feature classes, based on cell densities, cell-to-cell distances, and spatial heterogeneity thereof. Candidate biomarkers are ranked according to their potential for the predictive stratification of patients.Results: We evaluated the approach on a dataset of colorectal cancer patients with variable amounts of microsatellite instability. The most promising features that can be explored as biomarkers were based on cell-to-cell distances and spatial heterogeneity. Both the tumor and non-tumor compartments yielded features that were potentially predictive for therapy response and point in direction of further exploration.Conclusion: The data-driven approach simplifies the identification of promising IC biomarker candidates. Researchers can take guidance from the described approach to accelerate their biomarker research

A fast and robust hepatocyte quantification algorithm including vein processing

<p>Abstract</p> <p>Background</p> <p>Quantification of different types of cells is often needed for analysis of histological images. In our project, we compute the relative number of proliferating hepatocytes for the evaluation of the regeneration process after partial hepatectomy in normal rat livers.</p> <p>Results</p> <p>Our presented automatic approach for hepatocyte (HC) quantification is suitable for the analysis of an entire digitized histological section given in form of a series of images. It is the main part of an automatic hepatocyte quantification tool that allows for the computation of the ratio between the number of proliferating HC-nuclei and the total number of all HC-nuclei for a series of images in one processing run. The processing pipeline allows us to obtain desired and valuable results for a wide range of images with different properties without additional parameter adjustment. Comparing the obtained segmentation results with a manually retrieved segmentation mask which is considered to be the ground truth, we achieve results with sensitivity above 90% and false positive fraction below 15%.</p> <p>Conclusions</p> <p>The proposed automatic procedure gives results with high sensitivity and low false positive fraction and can be applied to process entire stained sections.</p

Automatisierte Analyse von Nekrose und Steatose in histologischen Bildern

Pathological examination of histological tissue sections is essential for the diagnosis of many life-threatening diseases. Demographic change and the growing importance of precision medicine require pathology to become more efficient, reproducible and quantitative. Automated histological image analysis is an important tool to meet these demands. This thesis is based on five research papers that consider specific problems in histological image analysis. The problems are related either to the quantification of necrosis or to the quantification of steatosis in histological sections of liver tissue. Both are typical applications in which tissue structures or cellular structures must be identified and quantitatively analyzed. In this context, the papers address important general challenges in histological image analysis and present broadly applicable solutions. One challenge is spatial heterogeneity of tissue properties, which can make their quantification sensitive to tissue sampling and image analysis errors. As a solution, the papers present novel scores that enable reliable measurement of heterogeneously distributed tissue properties. Another challenge is the huge variability of histological images, which can make machine learning-based analysis methods require large amounts of training data to work robustly. As a solution, the papers show how interactive training can produce accurate results with little training effort. Finally, a practical challenge is achieving a good trade-off between accuracy, efficiency, and simplicity. In this regard, the papers describe pragmatic approaches to enable accurate and fast analysis of gigapixel images on standard computers

Training Nuclei Detection Algorithms with Simple Annotations

Background: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. Methods: We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images. Results: A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality. Conclusions: With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets