Correlation filters for detection of cellular nuclei in histopathology images

Nuclei detection in histology images is an essential part of computer aided diagnosis of cancers and tumors. It is a challenging task due to diverse and complicated structures of cells. In this work, we present an automated technique for detection of cellular nuclei in hematoxylin and eosin stained histopathology images. Our proposed approach is based on kernelized correlation filters. Correlation filters have been widely used in object detection and tracking applications but their strength has not been explored in the medical imaging domain up till now. Our experimental results show that the proposed scheme gives state of the art accuracy and can learn complex nuclear morphologies. Like deep learning approaches, the proposed filters do not require engineering of image features as they can operate directly on histopathology images without significant preprocessing. However, unlike deep learning methods, the large-margin correlation filters developed in this work are interpretable, computationally efficient and do not require specialized or expensive computing hardware. Availability: A cloud based webserver of the proposed method and its python implementation can be accessed at the following URL: http://faculty.pieas.edu.pk/fayyaz/software.html#corehist

Mitosis Detection from Breast Cancer Histology Slide Images using Particle Swarm Optimization and Support Vector Machine

This paper introduces a new strategy for the purpose of automatic mitosis detection from breast cancer histopathology slide images. In this method, a new approach for reducing the number of false positive using Particle Swarm Optimization (PSO) is proposed. The proposed system is implemented on the histopathology slide images acquired by Aperio XT scanner (scanner A). In PSO algorithm the number of false positive objects or non-mitosis are defined as a cast function and by the minimization it the most of the non-mitosis candidates will be removed. Then some color, texture features such as co-occurrence and ru

MITOTIC HEp-2 CELL RECOGNITION USING LOCAL BINARY PATTERN (LBP) AND k-NEAREST NEIGHBOUR (k-NN) CLASSIFIER

Immune system produces autoantibody either randomly or as a result of an unknown material in the body itself. A failure of the body’s own defences against diseases has result to the auto immune diseases. Auto immune diseases will start attacking its own cell as they unable to differentiate between the foreign material and its own cell. An antinuclear antibody (ANA) is required as an indicator of the autoimmune process. IIF is the recommended technique for ANA detection in patient serum. IIF slides are observed by specialists reporting for the fluorescence intensity, staining pattern and looking for the mitotic cells. Indeed, the presence of the mitotic cells significantly important due to several key factors, first to guarantee the correctness of the slide preparation process and reported the staining pattern. Therefore the ability to detect mitotic cells is acquired to develop Computer-Aided Diagnosis (CAD) system in IIF to support the specialists form image acquisition up to image classification. This work aims to highlight the features of mitotic cells and to develop recognition algorithm for mitotic cell by incorporated Local Binary Pattern (LBP) and k-Nearest Neighbour to classified unlabelled image. A completed modelling of the LBP operator is proposed which is represented by its centre pixel and a local sign-magnitude transform (LDSMT). k-NN classifies unlabelled images based on the utmost majority samples in the feature space. This work involves five stages; image acquisition, pre-processing, feature extraction, distance measurement and classification

Mitosis Detection from Pathology Images

In the case of breast cancer, according to the Nottingham Grading System, counting mitotic cells is an important indicator of tumour diagnosis and grading. Pathologists usually manually count mitosis from histopathology images to determine the cancer grade. This is a challenging and time-consuming procedure. In most recent works, different deep neural networks have been designed to detect the suspicious cells initially and count the number of them afterwards. However, these detection approaches have certain limitations including complicated structures, the detection performance is still not satisfactory, and the need of a large number of labeled images to train a satisfied model. In this paper, we modify and improve a popular one-stage object-detection deep network to facilitate the mitotic cells detection task. Our novel improvements include using different loss functions for cells of different sizes, designing new data augmentation methods, generating prior anchor boxes with approximate sizes by using an improved clustering algorithm, and so on. We validate our deep learning model on two public benchmark datasets named Mitosis Detection in Breast Cancer Histological Images (MITOSIS). The experimental results indicate that our method achieves the competitive results on MITOSIS-2012 dataset and on the MITOSIS-2014 dataset with faster inference speed. More importantly, we design an interactive system with a correction and relearning pipeline so that our system can relearn from a small number of slides from a new lab and achieve satisfactory results. We design a web portal (http://ai4path.ca/#/) where this online pipeline can be easily utilized by pathologists in Western Hospital Pathology Group(WHPG) and hopefully in the future, by all pathologists in the world

A survey on artificial intelligence in histopathology image analysis

The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning-based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field

Deep learning for automatic microscopy image analysis

Microscopy imaging techniques allow for the creation of detailed images of cells (or nuclei) and have been widely employed for cell studies in biological research and disease diagnosis in clinic practices.Microscopy image analysis (MIA), with tasks of cell detection, cell classification, and cell counting, etc., can assist with the quantitative analysis of cells and provide useful information for a cellular-level understanding of biological activities and pathology. Manual MIA is tedious, time-consuming, prone to subject errors, and are not feasible for the high-throughput cell analysis process. Thus, automatic MIA methods can facilitate all kinds of biological studies and clinical tasks. Conventional feature engineering-based methods use handcrafted features to address MIA problems, but their performances are generally limited since the handcrafted features can lack feature diversity as well as relevancy to specific tasks. In recent years, deep learning, especially convolutional neuronal networks (CNNs), have shown promising performances on MIA tasks, due to their strong ability to automatically learn task-specific features directly from images in an end-to-end learning manner. However, there still remains a large gap between deep learning algorithms shown to be successful on retrospective datasets and those translated to clinical and biological practice. The major challenges for the application of deep learning into practical MIA problems include: (1) MIA tasks themselves are challenging due to limited image quality, the ambiguous appearance of inter-class nuclei, occluded cells, low cell specificity, and imaging artifacts; (2) training a learning algorithm is very challenging due to the potential gradient vanishing issue and the limited availability of annotated images. In this thesis, we investigate and propose deep learning methods for three challenging MIA tasks: cell counting, multi-class nuclei segmentation, and 3D phase-to-fluorescent image translation. We demonstrate the effectiveness of the proposed methods by intensively evaluating them on practical MIA problems. The proposed methods show superior performances compared to competitive state-of-the-art methods. Experimental results demonstrated that the proposed methods hold great promise to be applied in practical biomedical applications

Differentiating Human Embryonic Stem Cells in Micropatterns to Study Cell Fate Specification and Morphogenetic Events During Gastrulation

During mammalian embryogenesis, the first major lineage segregation occurs when embryonic epiblast, and extraembryonic trophectoderm and hypoblast arise in the blastocyst. In the next fundamental and conserved phase of animal embryogenesis known as gastrulation, extraembryonic cells provide signals to epiblast to instruct embryonic patterning, and epiblast gives rise to germ layers ectoderm, mesoderm, and endoderm, that will establish all embryonic tissues. Proper specification and morphogenesis of germ layers during gastrulation is vital for correct embryonic development. Due to ethical and legal restrictions limiting human embryo studies, human gastrulation is poorly understood. Our knowledge of human gastrulation has largely been derived from studies in model organisms, including mouse and more recently, cynomolgus monkey. However, interspecies differences underscore the need for alternative human gastrulation models. In this regard, human and mouse embryonic stem cells have been shown to recapitulate aspects of in vivo gastrulation including germ layer specification, and internalization and elongation morphogenesis. These in vitro systems represent powerful models of gastrulation due to the ease of genetic manipulations and the ability to finely control experimental factors. Human embryonic stem cells, treated with BMP4 for 44 hours in spatially confined micro-discs of extracellular matrix, have been shown to differentiate into 2D micro-colonies termed gastruloids. These gastruloids display highly reproducible differentiation of germ layers and extraembryonic cell types in a radial arrangement. We used combinatorial single-cell RNA sequencing and immunofluorescence imaging to characterize these BMP4-treated 2D gastruloids, and showed the formation in gastruloids of seven cell types, including epiblast, prospective ectoderm, two populations of mesoderm, and endoderm, as well as previously undescribed cell types in 2D gastruloids, primordial germ cell-like cells, and extraembryonic cells that are transcriptionally similar to trophectoderm and amnion. Comparative transcriptomic analyses with human, mouse, and cynomolgus monkey gastrulae support the notion that 2D gastruloid differentiation recapitulates formation of cell types relevant to and models an early-mid stage of in vivo gastrulation. Time course scRNA-seq and immunofluorescence analyses of 2D gastruloid differentiation after 12, 24, and 44 hours of BMP4 treatment showed that germ layer emergence in gastruloids follows the temporal sequence of in vivo gastrulation, with epiblast and ectoderm precursors forming at 12 hour, mesendoderm precursors arising from epiblast at 24 hour to give rise to nascent mesoderm and endoderm at 44 hour, when primordial germ cell-like cells also form. Comparison with human gastrula also showed similarity in transcriptomes and differentiation trajectories of gastruloid cells to their in vivo counterparts. Dynamic changes in transcripts encoding components of key signaling pathways support a BMP, WNT and Nodal hierarchy underlying germ layer specification conserved across mammals, with FGF and HIPPO signaling being active throughout the time course of 2D micropattern gastruloid differentiation. To probe morphogenetic properties of gastruloid cells, differentiated gastruloids treated with BMP4 for 44 hours were dissociated and re-seeded onto extracellular matrix micro-discs. The reseeded cells were highly motile and tended to form aggregates with the same but segregate from or mix with distinct cell types, supporting that 2D gastruloid system exhibits evolutionarily conserved sorting behaviors. In particular, ectodermal cells segregated from endodermal and extraembryonic cells but mixed with mesodermal cells. These results demonstrate that 2D gastruloid system models specification of germ layers and extraembryonic cell types, temporal order and differentiation trajectories of germ layer emergence, and signaling interactions found in early-mid in vivo gastrulation. Dissociated and reseeded gastruloid cells also exhibit conserved cell sorting behaviors. Lastly, this work provides a resource for mining genes and pathways expressed in a stereotyped 2D gastruloid model, common with other species or unique to human gastrulation