Adnan Mujahid Khan

A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution

Histopathology diagnosis is based on visual examination of the morphology of histological sections under a microscope. With the increasing popularity of digital slide scanners, decision support systems based on the analysis of digital pathology images are in high demand. However, computerized decision support systems are fraught with problems that stem from color variations in tissue appearance due to variation in tissue preparation, variation in stain reactivity from different manufacturers/batches, user or protocol variation, and the use of scanners from different manufacturers. In this paper, we present a novel approach to stain normalization in histopathology images. The method is based on nonlinear mapping of a source image to a target image using a representation derived from color deconvolution. Color deconvolution is a method to obtain stain concentration values when the stain matrix, describing how the color is affected by the stain concentration, is given. Rather than relying on standard stain matrices, which may be inappropriate for a given image, we propose the use of a color-based classifier that incorporates a novel stain color descriptor to calculate image-specific stain matrix. In order to demonstrate the efficacy of the proposed stain matrix estimation and stain normalization methods, they are applied to the problem of tumor segmentation in breast histopathology images. The experimental results suggest that the paradigm of color normalization, as a preprocessing step, can significantly help histological image analysis algorithms to demonstrate stable performance which is insensitive to imaging conditions in general and scanner variations in particular.

Assessment of algorithms for mitosis detection in breast cancer histopathology images.

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.

A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images.

In this paper, we propose a statistical approach for mitosis detection in breast cancer histological images. The proposed algorithm models the pixel intensities in mitotic and non-mitotic regions by a Gamma-Gaussian mixture model and employs a context-aware post-processing in order to reduce false positives. Experimental results demonstrate the ability of this simple, yet effective method to detect mitotic cells in standard H&E stained breast cancer histology images.

HyMaP: A hybrid magnitude-phase approach to unsupervised segmentation of tumor areas in breast cancer histology images

Background: Segmentation of areas containing tumor cells in standard H&E histopathology images of breast (and several other tissues) is a key task for computer-assisted assessment and grading of histopathology slides. Good segmentation of tumor regions is also vital for automated scoring of immunohistochemical stained slides to restrict the scoring or analysis to areas containing tumor cells only and avoid potentially misleading results from analysis of stromal regions. Furthermore, detection of mitotic cells is critical for calculating key measures such as mitotic index; a key criteria for grading several types of cancers including breast cancer. We show that tumor segmentation can allow detection and quantification of mitotic cells from the standard H&E slides with a high degree of accuracy without need for special stains, in turn making the whole process more cost-effective. Method: Based on the tissue morphology, breast histology image contents can be divided into four regions: Tumor, Hypocellular Stroma (HypoCS), Hypercellular Stroma (HyperCS), and tissue fat (Background). Background is removed during the preprocessing stage on the basis of color thresholding, while HypoCS and HyperCS regions are segmented by calculating features using magnitude and phase spectra in the frequency domain, respectively, and performing unsupervised segmentation on these features. Results: All images in the database were hand segmented by two expert pathologists. The algorithms considered here are evaluated on three pixel-wise accuracy measures: precision, recall, and F1-Score. The segmentation results obtained by combining HypoCS and HyperCS yield high F1-Score of 0.86 and 0.89 with re-spect to the ground truth. Conclusions: In this paper, we show that segmentation of breast histopathology image into hypocellular stroma and hypercellular stroma can be achieved using magnitude and phase spectra in the frequency domain. The segmentation leads to demarcation of tumor margins leading to improved accuracy of mitotic cell detection.

A novel system for scoring of hormone receptors in breast cancer histopathology slides

Grading of breast cancer is often done by an expert pathologist based on their analysis of micro-level structural features of the cancerous tissue specimen as well as the level of presence of certain protein molecules in the specimen. The process of assessment of the level of presence of estrogen and progesterone receptors molecules is subjective by its very nature and therefore, causes large inter-expert and sometimes even intra-expert variability, potentially adding noise to the process of selecting the treatment regime for the patient. Quantification of immunohistochemical stains is critical for an objective assessment of breast cancer histopathology specimens. We present a fast, compact and inexpensive system for scoring the Estrogen and Progesterone hormone receptors in breast cancer histopathology slides using image analysis algorithm. We describe hardware and software issues in the construction of the system, and present a comparison of scores produced by our system to those produced by many expert pathologists.

A Global Covariance Descriptor for Nuclear Atypia Scoring in Breast Histopathology Images

Nuclear atypia scoring is a diagnostic measure commonly used to assess tumor grade of various cancers, including breast cancer. It provides a quantitative measure of deviation in visual appearance of cell nuclei from those in normal epithelial cells. In this paper, we present a novel image-level descriptor for nuclear atypia scoring in breast cancer histopathology images. The method is based on the region covariance descriptor that has recently become a popular method in various computer vision applications. The descriptor in its original form is not suitable for classification of histopathology images as cancerous histopathology images tend to possess diversely heterogeneous regions in a single field of view. Our proposed image-level descriptor, which we term as the geodesic mean of region covariance descriptors, possesses all the attractive properties of covariance descriptors lending itself to tractable geodesic-distance-based k-nearest neighbor classification using efficient kernels. The experimental results suggest that the proposed image descriptor yields high classification accuracy compared to a variety of widely used image-level descriptors.

A novel paradigm for mining cell phenotypes in multi-tag bioimages using a locality preserving nonlinear embedding

Multi-tag bioimaging systems such as the toponome imaging system (TIS) require sophisticated analytical methods to extract molecular signatures of various types of cells. In this paper, we present a novel paradigm for mining cell phenotypes based on their high-dimensional co-expression profiles contained within the images generated by the robotically controlled TIS microscope installed at Warwick. The proposed paradigm employs a refined cell segmentation algorithm followed by a locality preserving nonlinear embedding algorithm which is shown to produce significantly better cell classification and phenotype distribution results as compared to its linear counterpart.

On generating cell exemplars for detection of mitotic cells in breast cancer histopathology images.

Mitotic activity is one of the main criteria that pathologists use to decide the grade of the cancer. Computerised mitotic cell detection promises to bring efficiency and accuracy into the grading process. However, detection and classification of mitotic cells in breast cancer histopathology images is a challenging task because of the large intra-class variation in the visual appearance of mitotic cells in various stages of cell division life cycle. In this paper, we test the hypothesis that cells in histopathology images can be effectively represented using cell exemplars derived from sub-images of various kinds of cells in an image for the purposes of mitotic cell classification. We compare three methods for generating exemplar cells. The methods have been evaluated in terms of classification performance on the MITOS dataset. The experimental results demonstrate that eigencells combined with support vector machines produce reasonably high detection accuracy among all the methods.

Geodesic Geometric Mean of Regional Covariance Descriptors as an Image-Level Descriptor for Nuclear Atypia Grading in Breast Histology Images

The region covariance descriptors have recently become a popular method for detection and tracking of objects in an image. However, these descriptors are not suitable for classification of images with heterogeneous contents. In this paper, we present an image-level descriptor obtained using an affine-invariant geodesic mean of region covariance descriptors on the Riemannian manifold of symmetric positive definite (SPD) matrices. The resulting image descriptors are also SPD matrices, lending themselves to tractable geodesic distance based k-nearest neighbour classification using efficient kernels. We show that the proposed descriptor yields high classification accuracy on a challenging problem of nuclear pleomorphism scoring in breast cancer histology images.