Sonal Kothari

Pathology imaging informatics for quantitative analysis of whole-slide images

Objectives With the objective of bringing clinical decision support systems to reality, this article reviews histopathological whole-slide imaging informatics methods, associated challenges, and future research opportunities. Target audience This review targets pathologists and informaticians who have a limited understanding of the key aspects of whole-slide image (WSI) analysis and/or a limited knowledge of state-of-the-art technologies and analysis methods. Scope First, we discuss the importance of imaging informatics in pathology and highlight the challenges posed by histopathological WSI. Next, we provide a thorough review of current methods for: quality control of histopathological images; feature extraction that captures image properties at the pixel, object, and semantic levels; predictive modeling that utilizes image features for diagnostic or prognostic applications; and data and information visualization that explores WSI for de novo discovery. In addition, we highlight future research directions and discuss the impact of large public repositories of histopathological data, such as the Cancer Genome Atlas, on the field of pathology informatics. Following the review, we present a case study to illustrate a clinical decision support system that begins with quality control and ends with predictive modeling for several cancer endpoints. Currently, state-of-the-art software tools only provide limited image processing capabilities instead of complete data analysis for clinical decision-making. We aim to inspire researchers to conduct more research in pathology imaging informatics so that clinical decision support can become a reality.

Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques

This paper presents a novel, fast and semi-automatic method for accurate cell cluster segmentation and cell counting of digital tissue image samples. In pathological conditions, complex cell clusters are a prominent feature in tissue samples. Segmentation of these clusters is a major challenge for development of an accurate cell counting methodology. We address the issue of cluster segmentation by following a three step process. The first step involves pre-processing required to obtain the appropriate nuclei cluster boundary image from the RGB tissue samples. The second step involves concavity detection at the edge of a cluster to find the points of overlap between two nuclei. The third step involves segmentation at these concavities by using an ellipse-fitting technique. Once the clusters are segmented, individual nuclei are counted to give the cell count. The method was tested on four different types of cancerous tissue samples and shows promising results with a low percentage error, high true positive rate and low false discovery rate.

Automatic batch-invariant color segmentation of histological cancer images

We propose an automatic color segmentation system that (1) incorporates domain knowledge to guide histological image segmentation and (2) normalizes images to reduce sensitivity to batch effects. Color segmentation is an important, yet difficult, component of image-based diagnostic systems. User-interactive guidance by domain experts—i.e., pathologistsߞoften leads to the best color segmentation or “ground truth” regardless of stain color variations in different batches. However, such guidance limits the objectivity, reproducibility and speed of diagnostic systems. Our system uses knowledge from pre-segmented reference images to normalize and classify pixels in patient images. The system then refines the segmentation by re-classifying pixels in the original color space. We test our system on four batches of H&E stained images and, in comparison to a system with no normalization (39% average accuracy), we obtain an average segmentation accuracy of 85%.

Biological interpretation of morphological patterns in histopathological whole-slide images

We propose a framework for studying visual morphological patterns across histopathological whole-slide images (WSIs). Image representation is an important component of computer-aided decision support systems for histopathological cancer diagnosis. Such systems extract hundreds of quantitative image features from digitized tissue biopsy slides and produce models for prediction. The performance of these models depends on the identification of informative features for selection of appropriate regions-of-interest (ROIs) from heterogeneous WSIs and for development of models. However, identification of informative features is hindered by the semantic gap between human interpretation of visual morphological patterns and quantitative image features. We address this challenge by using data mining and information visualization tools to study spatial patterns formed by features extracted from sub-sections of WSIs. Using ovarian serous cystadenocarcinoma (OvCa) WSIs provided by the cancer genome atlas (TCGA), we show that (1) individual and (2) multivariate image features correspond to biologically relevant ROIs, and (3) supervised image feature selection can map histopathology domain knowledge to quantitative image features.

Histological image classification using biologically interpretable shape-based features

BackgroundAutomatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis.MethodsWe examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features.ResultsThe shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors.ConclusionsShape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions.

Extraction of informative cell features by segmentation of densely clustered tissue images

This paper presents a fast methodology for the estimation of informative cell features from densely clustered RGB tissue images. The features estimated include nuclei count, nuclei size distribution, nuclei eccentricity (roundness) distribution, nuclei closeness distribution and cluster size distribution. Our methodology is a three step technique. Firstly, we generate a binary nuclei mask from an RGB tissue image by color segmentation. Secondly, we segment nuclei clusters present in the binary mask into individual nuclei by concavity detection and ellipse fitting. Finally, we estimate informative features for every nuclei and their distribution for the complete image. The main focus of our work is the development of a fast and accurate nuclei cluster segmentation technique for densely clustered tissue images. We also developed a simple graphical user interface (GUI) for our application which requires minimal user interaction and can efficiently extract features from nuclei clusters, making it feasible for clinical applications (less than 2 minutes for a 1.9 megapixel tissue image).

Eliminating tissue-fold artifacts in histopathological whole-slide images for improved image-based prediction of cancer grade

Background: Analysis of tissue biopsy whole-slide images (WSIs) depends on effective detection and elimination of image artifacts. We present a novel method to detect tissue-fold artifacts in histopathological WSIs. We also study the effect of tissue folds on image features and prediction models. Materials and Methods: We use WSIs of samples from two cancer endpoints - kidney clear cell carcinoma (KiCa) and ovarian serous adenocarcinoma (OvCa) - publicly available from The Cancer Genome Atlas. We detect tissue folds in low-resolution WSIs using color properties and two adaptive connectivity-based thresholds. We optimize and validate our tissue-fold detection method using 105 manually annotated WSIs from both cancer endpoints. In addition to detecting tissue folds, we extract 461 image features from the high-resolution WSIs for all samples. We use the rank-sum test to find image features that are statistically different among features extracted from the same set of WSIs with and without folds. We then use features that are affected by tissue folds to develop models for predicting cancer grades. Results: When compared to the ground truth, our method detects tissue folds in KiCa with 0.50 adjusted Rand index (ARI), 0.77 average true rate (ATR), 0.55 true positive rate (TPR), and 0.98 true negative rate (TNR); and in OvCa with 0.40 ARI, 0.73 ATR, 0.47 TPR, and 0.98 TNR. Compared to two other methods, our method is more accurate in terms of ARI and ATR. We found that 53 and 30 image features were significantly affected by the presence of tissue-fold artifacts (detected using our method) in OvCa and KiCa, respectively. After eliminating tissue folds, the performance of cancer-grade prediction models improved by 5% and 1% in OvCa and KiCa, respectively. Conclusion: The proposed connectivity-based method is more effective in detecting tissue folds compared to other methods. Reducing tissue-fold artifacts will increase the performance of cancer-grade prediction models.

Removing Batch Effects From Histopathological Images for Enhanced Cancer Diagnosis

Researchers have developed computer-aided decision support systems for translational medicine that aim to objectively and efficiently diagnose cancer using histopathological images. However, the performance of such systems is confounded by nonbiological experimental variations or “batch effects” that can commonly occur in histopathological data, especially when images are acquired using different imaging devices and patient samples. This is even more problematic in large-scale studies in which cross-laboratory sharing of large volumes of data is necessary. Batch effects can change quantitative morphological image features and decrease the prediction performance. Using four batches of renal tumor images, we compare one image-level and five feature-level batch effect removal methods. Principal component variation analysis shows that batch is a large source of variance in image features. Results show that feature-level normalization methods reduce batch-contributed variance to almost zero. Moreover, feature-level normalization, especially ComBatN, improves cross-batch and combined-batch prediction performance. Compared to no normalization, ComBatN improves performance in 83% and 90% of cross-batch and combined-batch prediction models, respectively.

Comparison of normalization algorithms for cross-batch color segmentation of histopathological images

Automated processing of digital histopathology slides has the potential to streamline patient care and provide new tools for cancer classification and grading. Before automatic analysis is possible, quality control procedures are applied to ensure that each image can be read consistently. One important quality control step is color normalization of the slide image, which adjusts for color variances (batch-effects) caused by differences in stain preparation and image acquisition equipment. Color batch-effects affect color-based features and reduce the performance of supervised color segmentation algorithms on images acquired separately. To identify an optimal normalization technique for histopathological color segmentation applications, five color normalization algorithms were compared in this study using 204 images from four image batches. Among the normalization methods, two global color normalization methods normalized colors from all stain simultaneously and three stain color normalization methods normalized colors from individual stains extracted using color deconvolution. Stain color normalization methods performed significantly better than global color normalization methods in 11 of 12 cross-batch experiments (p<;0.05). Specifically, the stain color normalization method using k-means clustering was found to be the best choice because of high stain segmentation accuracy and low computational complexity.

Scale normalization of histopathological images for batch invariant cancer diagnostic models

Histopathological images acquired from different experimental set-ups often suffer from batch-effects due to color variations and scale variations. In this paper, we develop a novel scale normalization model for histopathological images based on nuclear area distributions. Results indicate that the normalization model closely fits empirical values for two renal tumor datasets. We study the effect of scale normalization on classification of renal tumor images. Scale normalization improves classification performance in most cases. However, performance decreases in a few cases. In order to understand this, we propose two methods to filter extracted image features that are sensitive to image scaling and features that are uncorrelated with scaling factor. Feature filtering improves the classification performance of cases that were initially negatively affected by scale normalization.