Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology

Multiple instance (MI) learning with a convolutional neural network enables end-to-end training in the presence of weak image-level labels. We propose a new method for aggregating predictions from smaller regions of the image into an image-level classification by using the quantile function. The quantile function provides a more complete description of the heterogeneity within each image, improving image-level classification. We also adapt image augmentation to the MI framework by randomly selecting cropped regions on which to apply MI aggregation during each epoch of training. This provides a mechanism to study the importance of MI learning. We validate our method on five different classification tasks for breast tumor histology and provide a visualization method for interpreting local image classifications that could lead to future insights into tumor heterogeneity

Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics

TriResNet: A Deep Triple-stream Residual Network for Histopathology Grading

While microscopic analysis of histopathological slides is generally considered as the gold standard method for performing cancer diagnosis and grading, the current method for analysis is extremely time consuming and labour intensive as it requires pathologists to visually inspect tissue samples in a detailed fashion for the presence of cancer. As such, there has been significant recent interest in computer aided diagnosis systems for analysing histopathological slides for cancer grading to aid pathologists to perform cancer diagnosis and grading in a more efficient, accurate, and consistent manner. In this work, we investigate and explore a deep triple-stream residual network (TriResNet) architecture for the purpose of tile-level histopathology grading, which is the critical first step to computer-aided whole-slide histopathology grading. In particular, the design mentality behind the proposed TriResNet network architecture is to facilitate for the learning of a more diverse set of quantitative features to better characterize the complex tissue characteristics found in histopathology samples. Experimental results on two widely-used computer-aided histopathology benchmark datasets (CAMELYON16 dataset and Invasive Ductal Carcinoma (IDC) dataset) demonstrated that the proposed TriResNet network architecture was able to achieve noticeably improved accuracies when compared with two other state-of-the-art deep convolutional neural network architectures. Based on these promising results, the hope is that the proposed TriResNet network architecture could become a useful tool to aiding pathologists increase the consistency, speed, and accuracy of the histopathology grading process.Comment: 9 page

Improving biomarker assessment in breast pathology

The accuracy of prognostic and therapy-predictive biomarker assessment in breast tumours is crucial for management and therapy decision in patients with breast cancer. In this thesis, biomarkers used in clinical practice with emphasise on Ki67 and HER2 were studied using several methods including immunocytochemistry, in situ hybridisation, gene expression assays and digital image analysis, with the overall aim to improve routine biomarker evaluation and clarify the prognostic potential in early breast cancer. In paper I, we reported discordances in biomarker status from aspiration cytology and paired surgical specimens from breast tumours. The limited prognostic potential of immunocytochemistry-based Ki67 scoring demonstrated that immunohistochemistry on resected specimens is the superior method for Ki67 evaluation. In addition, neither of the methods were sufficient to predict molecular subtype. Following this in paper II, biomarker agreement between core needle biopsies and subsequent specimens was investigated, both in the adjuvant and neoadjuvant setting. Discordances in Ki67 and HER2 status between core biopsies and paired specimens suggested that these biomarkers should be re-tested on all surgical breast cancer specimens. In paper III, digital image analysis using a virtual double staining software was used to compare methods for assessment of proliferative activity, including mitotic counts, Ki67 and the alternative marker PHH3, in different tumour regions (hot spot, invasive edge and whole section). Digital image analysis using virtual double staining of hot spot Ki67 outperformed the alternative markers of proliferation, especially in discriminating luminal B from luminal A tumours. Replacing mitosis in histological grade with hot spot-scored Ki67 added significant prognostic information. Following these findings, the optimal definition of a hot spot for Ki67 scoring using virtual double staining in relation to molecular subtype and outcome was investigated in paper IV. With the growing evidence of global scoring as a superior method to improve reproducibility of Ki67 scoring, a different digital image analysis software (QuPath) was also used for comparison. Altogether, we found that automated global scoring of Ki67 using QuPath had independent prognostic potential compared to even the best virtual double staining hot spot algorithm, and is also a practical method for routine Ki67 scoring in breast pathology. In paper V, the clinical value of HER2 status was investigated in a unique trastuzumab-treated HER2-positive cohort, on the protein, mRNA and DNA levels. The results demonstrated that low levels of ERBB2 mRNA but neither HER2 copy numbers, HER2 ratio nor ER status, was associated with risk of recurrence among anti-HER2 treated breast cancer patients. In conclusion, we have identified important clinical aspects of Ki67 and HER2 evaluation and provided methods to improve the prognostic potential of Ki67 using digital image analysis. In addition to protein expression of routine biomarkers, mRNA levels by targeted gene expression assays may add further prognostic value in early breast cance

Discriminative Representations for Heterogeneous Images and Multimodal Data

Histology images of tumor tissue are an important diagnostic and prognostic tool for pathologists. Recently developed molecular methods group tumors into subtypes to further guide treatment decisions, but they are not routinely performed on all patients. A lower cost and repeatable method to predict tumor subtypes from histology could bring benefits to more cancer patients. Further, combining imaging and genomic data types provides a more complete view of the tumor and may improve prognostication and treatment decisions. While molecular and genomic methods capture the state of a small sample of tumor, histological image analysis provides a spatial view and can identify multiple subtypes in a single tumor. This intra-tumor heterogeneity has yet to be fully understood and its quantification may lead to future insights into tumor progression. In this work, I develop methods to learn appropriate features directly from images using dictionary learning or deep learning. I use multiple instance learning to account for intra-tumor variations in subtype during training, improving subtype predictions and providing insights into tumor heterogeneity. I also integrate image and genomic features to learn a projection to a shared space that is also discriminative. This method can be used for cross-modal classification or to improve predictions from images by also learning from genomic data during training, even if only image data is available at test time.Doctor of Philosoph

Mitotic cell detection in H&E stained meningioma histopathology slides

Indiana University-Purdue University Indianapolis (IUPUI)Meningioma represent more than one-third of all primary central nervous system (CNS) tumors, and it can be classified into three grades according to WHO (World Health Organization) in terms of clinical aggressiveness and risk of recurrence. A key component of meningioma grades is the mitotic count, which is defined as quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at 10 consecutive high-power fields (HPF) on a glass slide under a microscope, which is an extremely laborious and time-consuming process. The goal of this thesis is to investigate the use of computerized methods to automate the detection of mitotic nuclei with limited labeled data. We built computational methods to detect and quantify the histological features of mitotic cells on a whole slides image which mimic the exact process of pathologist workflow. Since we do not have enough training data from meningioma slide, we learned the mitotic cell features through public available breast cancer datasets, and predicted on meingioma slide for accuracy. We use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Hand crafted features are inspired by the domain knowledge, while the data-driven VGG16 models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. Our work on detection of mitotic cells shows 100% recall , 9% precision and 0.17 F1 score. The detection using VGG16 performs with 71% recall, 73% precision, and 0.77 F1 score. Finally, this research of automated image analysis could drastically increase diagnostic efficiency and reduce inter-observer variability and errors in pathology diagnosis, which would allow fewer pathologists to serve more patients while maintaining diagnostic accuracy and precision. And all these methodologies will increasingly transform practice of pathology, allowing it to mature toward a quantitative science