A novel dilated contextual attention module for breast cancer mitosis cell detection

Background and object: Mitotic count (MC) is a critical histological parameter for accurately assessing the degree of invasiveness in breast cancer, holding significant clinical value for cancer treatment and prognosis. However, accurately identifying mitotic cells poses a challenge due to their morphological and size diversity.Objective: We propose a novel end-to-end deep-learning method for identifying mitotic cells in breast cancer pathological images, with the aim of enhancing the performance of recognizing mitotic cells.Methods: We introduced the Dilated Cascading Network (DilCasNet) composed of detection and classification stages. To enhance the model’s ability to capture distant feature dependencies in mitotic cells, we devised a novel Dilated Contextual Attention Module (DiCoA) that utilizes sparse global attention during the detection. For reclassifying mitotic cell areas localized in the detection stage, we integrate the EfficientNet-B7 and VGG16 pre-trained models (InPreMo) in the classification step.Results: Based on the canine mammary carcinoma (CMC) mitosis dataset, DilCasNet demonstrates superior overall performance compared to the benchmark model. The specific metrics of the model’s performance are as follows: F1 score of 82.9%, Precision of 82.6%, and Recall of 83.2%. With the incorporation of the DiCoA attention module, the model exhibited an improvement of over 3.5% in the F1 during the detection stage.Conclusion: The DilCasNet achieved a favorable detection performance of mitotic cells in breast cancer and provides a solution for detecting mitotic cells in pathological images of other cancers

ERNet : Enhanced ResNet for classification of breast histopathological images

Inspite of expeditious approaches in field of breast cancer, histopathological analysis is considered as gold standard in diagnosis of cancer. Researchers are working tremendously to automate the detection and analysis of breast histology images, which confess in improving the accuracy and also induce the mimisation of processing time. Deep learning models are providing greater contribution in solving several image classification tasks. In this paper we propose a model to classify breast histological images, which is redesigned from existing ResNet architecture that minimises model parameters and increase computational efficiency. This approach uses enhanced ResNet connection instead of identity shortcut connection used in ResNet architecture. We apply our proposed method on BreakHis dataset and achieve an accuracy around 95.92 %.  The numerical results show that our proposed approach outperforms the previous methods with respect to sensitivity and accuracy

Deep Learning for Detection and Segmentation in High-Content Microscopy Images

High-content microscopy led to many advances in biology and medicine. This fast emerging technology is transforming cell biology into a big data driven science. Computer vision methods are used to automate the analysis of microscopy image data. In recent years, deep learning became popular and had major success in computer vision. Most of the available methods are developed to process natural images. Compared to natural images, microscopy images pose domain specific challenges such as small training datasets, clustered objects, and class imbalance. In this thesis, new deep learning methods for object detection and cell segmentation in microscopy images are introduced. For particle detection in fluorescence microscopy images, a deep learning method based on a domain-adapted Deconvolution Network is presented. In addition, a method for mitotic cell detection in heterogeneous histopathology images is proposed, which combines a deep residual network with Hough voting. The method is used for grading of whole-slide histology images of breast carcinoma. Moreover, a method for both particle detection and cell detection based on object centroids is introduced, which is trainable end-to-end. It comprises a novel Centroid Proposal Network, a layer for ensembling detection hypotheses over image scales and anchors, an anchor regularization scheme which favours prior anchors over regressed locations, and an improved algorithm for Non-Maximum Suppression. Furthermore, a novel loss function based on Normalized Mutual Information is proposed which can cope with strong class imbalance and is derived within a Bayesian framework. For cell segmentation, a deep neural network with increased receptive field to capture rich semantic information is introduced. Moreover, a deep neural network which combines both paradigms of multi-scale feature aggregation of Convolutional Neural Networks and iterative refinement of Recurrent Neural Networks is proposed. To increase the robustness of the training and improve segmentation, a novel focal loss function is presented. In addition, a framework for black-box hyperparameter optimization for biomedical image analysis pipelines is proposed. The framework has a modular architecture that separates hyperparameter sampling and hyperparameter optimization. A visualization of the loss function based on infimum projections is suggested to obtain further insights into the optimization problem. Also, a transfer learning approach is presented, which uses only one color channel for pre-training and performs fine-tuning on more color channels. Furthermore, an approach for unsupervised domain adaptation for histopathological slides is presented. Finally, Galaxy Image Analysis is presented, a platform for web-based microscopy image analysis. Galaxy Image Analysis workflows for cell segmentation in cell cultures, particle detection in mice brain tissue, and MALDI/H&E image registration have been developed. The proposed methods were applied to challenging synthetic as well as real microscopy image data from various microscopy modalities. It turned out that the proposed methods yield state-of-the-art or improved results. The methods were benchmarked in international image analysis challenges and used in various cooperation projects with biomedical researchers

Radon Projections as Image Descriptors for Content-Based Retrieval of Medical Images

Clinical analysis and medical diagnosis of diverse diseases adopt medical imaging techniques to empower specialists to perform their tasks by visualizing internal body organs and tissues for classifying and treating diseases at an early stage. Content-Based Image Retrieval (CBIR) systems are a set of computer vision techniques to retrieve similar images from a large database based on proper image representations. Particularly in radiology and histopathology, CBIR is a promising approach to effectively screen, understand, and retrieve images with similar level of semantic descriptions from a database of previously diagnosed cases to provide physicians with reliable assistance for diagnosis, treatment planning and research. Over the past decade, the development of CBIR systems in medical imaging has expedited due to the increase in digitized modalities, an increase in computational efficiency (e.g., availability of GPUs), and progress in algorithm development in computer vision and artificial intelligence. Hence, medical specialists may use CBIR prototypes to query similar cases from a large image database based solely on the image content (and no text). Understanding the semantics of an image requires an expressive descriptor that has the ability to capture and to represent unique and invariant features of an image. Radon transform, one of the oldest techniques widely used in medical imaging, can capture the shape of organs in form of a one-dimensional histogram by projecting parallel rays through a two-dimensional object of concern at a specific angle. In this work, the Radon transform is re-designed to (i) extract features and (ii) generate a descriptor for content-based retrieval of medical images. Radon transform is applied to feed a deep neural network instead of raw images in order to improve the generalization of the network. Specifically, the framework is composed of providing Radon projections of an image to a deep autoencoder, from which the deepest layer is isolated and fed into a multi-layer perceptron for classification. This approach enables the network to (a) train much faster as the Radon projections are computationally inexpensive compared to raw input images, and (b) perform more accurately as Radon projections can make more pronounced and salient features to the network compared to raw images. This framework is validated on a publicly available radiography data set called "Image Retrieval in Medical Applications" (IRMA), consisting of 12,677 train and 1,733 test images, for which an classification accuracy of approximately 82% is achieved, outperforming all autoencoder strategies reported on the Image Retrieval in Medical Applications (IRMA) dataset. The classification accuracy is calculated by dividing the total IRMA error, a calculation outlined by the authors of the data set, with the total number of test images. Finally, a compact handcrafted image descriptor based on Radon transform was designed in this work that is called "Forming Local Intersections of Projections" (FLIP). The FLIP descriptor has been designed, through numerous experiments, for representing histopathology images. The FLIP descriptor is based on Radon transform wherein parallel projections are applied in a local 3x3 neighborhoods with 2 pixel overlap of gray-level images (staining of histopathology images is ignored). Using four equidistant projection directions in each window, the characteristics of the neighborhood is quantified by taking an element-wise minimum between each adjacent projection in each window. Thereafter, the FLIP histogram (descriptor) for each image is constructed. A multi-resolution FLIP (mFLIP) scheme is also proposed which is observed to outperform many state-of-the-art methods, among others deep features, when applied on the histopathology data set KIMIA Path24. Experiments show a total classification accuracy of approximately 72% using SVM classification, which surpasses the current benchmark of approximately 66% on the KIMIA Path24 data set

High-resolution imaging for cancer detection with a fiber bundle microendoscope

Dysplasia and cancer of epithelial tissues, including the oral cavity and esophagus, typically have much higher survival rates if diagnosed at an early stage. Unfortunately, the clinical appearance of lesions in these tissues can be highly variable. To achieve a definitive diagnosis of a suspected lesion at these sites, an excisional biopsy must be examined at high-resolution. These procedures can be costly and timeconsuming, and in the case of Barrett's esophagus, surveillance biopsy strategies may not be entirely effective. Optical imaging modalities have the potential to yield qualitative and quantitative high-resolution data at low cost, enabling clinicians to improve early detection rate. This dissertation presents a low-cost high-resolution microendoscopy system based on a fiber optic bundle image guide. In combination with a topical fluorescent dye, the fiber bundle can be placed into contact with the tissue to be observed. A high-resolution image is then projected onto a CCD camera and stored on a PC. A pilot study was performed on both resected esophageal tissue containing intestinal metaplasia (a condition known as Barrett's esophagus, which can transform to esophageal adenocarcinoma) and resected oral tissue following surgical removal of cancer. Qualitative image analysis demonstrated similar features were visible in both microendoscope images and standard histology images, and quantitative image processing and analysis yielded an objective classification algorithm. The classification algorithm was developed to discriminate between neoplastic and non-neoplastic imaging sites. The performance of this algorithm was monitored by comparing the predicted results to the pathology diagnosis at each measurement site. In the oral cancer pilot study, the classifier achieved 85% sensitivity and 78% specificity with 141 independent measurement sites. In the Barrett's metaplasia pilot study, 87% sensitivity and 85% specificity were achieved with 128 independent measurement sites. The work presented in this dissertation outlines the design, testing, and initial validation of the high-resolution microendoscope system. This microendoscope system has demonstrated potential utility over a wide range of modalities, including small animal imaging, molecular-specific imaging, ex vivo and ultimately in vivo imaging

Conduits of Intratumor Heterogeneity: Centrosome Amplification, Centrosome Clustering and Mitotic Frequency

Tumor initiation and progression is dependent on the acquisition and accumulation of multiple driver mutations that acti­vate and fuel oncogenic pathways and deactivate tumor suppressor networks. This complex continuum of non-stochastic genetic changes in accompaniment with error-prone mitoses largely explains why tumors are a mosaic of different cells. Contrary to the long-held notion that tumors are dominated by genetically-identical cells, tumors often contain many different subsets of cells that are remarkably diverse and distinct. The extent of this intratumor heterogeneity has bewildered cancer biologists’ and clinicians alike, as this partly illuminates why most cancer treatments fail. Unsurprisingly, there is no “wonder” drug yet available which can target all the different sub-populations including rare clones, and conquer the war on cancer. Breast tumors harbor ginormous extent of intratumoral heterogeneity, both within primary and metastatic lesions. This revelation essentially calls into question mega clinical endeavors such as the Human Genome Project that have sequenced a single biopsy from a large tumor mass thus precluding realization of the fact that a single tumor mass comprises of cells that present a variety of flavors in genotypic compositions. It is also becoming recognized that intratumor clonal heterogeneity underlies therapeutic resistance. Thus to comprehend the clinical behavior and therapeutic management of tumors, it is imperative to recognize and understand how intratumor heterogeneity arises. To this end, my research proposes to study two main features/cellular traits of tumors that can be quantitatively evaluated as “surrogates” to represent tumor heterogeneity at various stages of the disease: (a) centrosome amplification and clustering, and (b) mitotic frequency. This study aims at interrogating how a collaborative interplay of these “vehicles” support the tumor’s evolutionary agenda, and how we can glean prognostic and predictive information from an accurate determination of these cellular traits