Classification and Retrieval of Digital Pathology Scans: A New Dataset

In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000$\times$1000 (0.5mm$\times$0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset parameters are adopted. We propose a compound patch-and-scan accuracy measurement that makes achieving high accuracies quite challenging. In addition, we set the benchmarking line by applying LBP, dictionary approach and convolutional neural nets (CNNs) and report their results. The highest accuracy was 41.80\% for CNN.Comment: Accepted for presentation at Workshop for Computer Vision for Microscopy Image Analysis (CVMI 2017) @ CVPR 2017, Honolulu, Hawai

High-resolution fluorescence endomicroscopy for rapid evaluation of breast cancer margins

Breast cancer is a major public health problem world-wide and the second leading cause of cancer-related female deaths. Breast conserving surgery (BCS), in the form of wide local excision (WLE), allows complete tumour resection while maintaining acceptable cosmesis. It is the recommended treatment for a large number of patients with early stage disease or, in more advanced cases, following neoadjuvant chemotherapy. About 30% of patients undergoing BCS require one or more re-operative interventions, mainly due to the presence of positive margins. The standard of care for surgical margin assessment is post-operative examination of histopathological tissue sections. However, this process is invasive, introduces sampling errors and does not provide real-time assessment of the tumour status of radial margins. The objective of this thesis is to improve intra-operative assessment of margin status by performing optical biopsy in breast tissue. This thesis presents several technical and clinical developments related to confocal fluorescence endomicroscopy systems for real-time characterisation of different breast morphologies. The imaging systems discussed employ flexible fibre-bundle based imaging probes coupled to high-speed line-scan confocal microscope set-up. A preliminary study on 43 unfixed breast specimens describes the development and testing of line-scan confocal laser endomicroscope (LS-CLE) to image and classify different breast pathologies. LS-CLE is also demonstrated to assess the intra-operative tumour status of whole WLE specimens and surgical excisions with high diagnostic accuracy. A third study demonstrates the development and testing of a bespoke LS-CLE system with methylene blue (MB), an US Food and Drug Administration (FDA) approved fluorescent agent, and integration with robotic scanner to enable large-area in vivo imaging of breast cancer. The work also addresses three technical issues which limit existing fibre-bundle based fluorescence endomicroscopy systems: i) Restriction to use single fluorescence agent due to low-speed, single excitation and single fluorescence spectral band imaging systems; ii) Limited Field of view (FOV) of fibre-bundle endomicroscopes due to small size of the fibre tip and iii) Limited spatial resolution of fibre-bundle endomicroscopes due to the spacing between the individual fibres leading to fibre-pixelation effects. Details of design and development of a high-speed dual-wavelength LS-CLE system suitable for high-resolution multiplexed imaging are presented. Dual-wavelength imaging is achieved by sequentially switching between 488 nm and 660 nm laser sources for alternate frames, avoiding spectral bleed-through, and providing an effective frame rate of 60 Hz. A combination of hand-held or robotic scanning with real-time video mosaicking, is demonstrated to enable large-area imaging while still maintaining microscopic resolution. Finally, a miniaturised piezoelectric transducer-based fibre-shifting endomicroscope is developed to enhance the resolution over conventional fibre-bundle based imaging systems. The fibre-shifting endomicroscope provides a two-fold improvement in resolution and coupled to a high-speed LS-CLE scanning system, provides real-time imaging of biological samples at 30 fps. These investigations furthered the utility and applications of the fibre-bundle based fluorescence systems for rapid imaging and diagnosis of cancer margins.Open Acces

Novel Deep Learning Models for Medical Imaging Analysis

abstract: Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Towards Secure and Intelligent Diagnosis: Deep Learning and Blockchain Technology for Computer-Aided Diagnosis Systems

Cancer is the second leading cause of death across the world after cardiovascular disease. The survival rate of patients with cancerous tissue can significantly decrease due to late-stage diagnosis. Nowadays, advancements of whole slide imaging scanners have resulted in a dramatic increase of patient data in the domain of digital pathology. Large-scale histopathology images need to be analyzed promptly for early cancer detection which is critical for improving patient's survival rate and treatment planning. Advances of medical image processing and deep learning methods have facilitated the extraction and analysis of high-level features from histopathological data that could assist in life-critical diagnosis and reduce the considerable healthcare cost associated with cancer. In clinical trials, due to the complexity and large variance of collected image data, developing computer-aided diagnosis systems to support quantitative medical image analysis is an area of active research. The first goal of this research is to automate the classification and segmentation process of cancerous regions in histopathology images of different cancer tissues by developing models using deep learning-based architectures. In this research, a framework with different modules is proposed, including (1) data pre-processing, (2) data augmentation, (3) feature extraction, and (4) deep learning architectures. Four validation studies were designed to conduct this research. (1) differentiating benign and malignant lesions in breast cancer (2) differentiating between immature leukemic blasts and normal cells in leukemia cancer (3) differentiating benign and malignant regions in lung cancer, and (4) differentiating benign and malignant regions in colorectal cancer. Training machine learning models, disease diagnosis, and treatment often requires collecting patients' medical data. Privacy and trusted authenticity concerns make data owners reluctant to share their personal and medical data. Motivated by the advantages of Blockchain technology in healthcare data sharing frameworks, the focus of the second part of this research is to integrate Blockchain technology in computer-aided diagnosis systems to address the problems of managing access control, authentication, provenance, and confidentiality of sensitive medical data. To do so, a hierarchical identity and attribute-based access control mechanism using smart contract and Ethereum Blockchain is proposed to securely process healthcare data without revealing sensitive information to an unauthorized party leveraging the trustworthiness of transactions in a collaborative healthcare environment. The proposed access control mechanism provides a solution to the challenges associated with centralized access control systems and ensures data transparency and traceability for secure data sharing, and data ownership

Objective localisation of oral mucosal lesions using optical coherence tomography.

PhDIdentification of the most representative location for biopsy is critical in establishing the definitive diagnosis of oral mucosal lesions. Currently, this process involves visual evaluation of the colour characteristics of tissue aided by topical application of contrast enhancing agents. Although, this approach is widely practiced, it remains limited by its lack of objectivity in identifying and delineating suspicious areas for biopsy. To overcome this drawback there is a need to introduce a technique that would provide macroscopic guidance based on microscopic imaging and analysis. Optical Coherence Tomography is an emerging high resolution biomedical imaging modality that can potentially be used as an in vivo tool for selection of the most appropriate site for biopsy. This thesis investigates the use of OCT for qualitative and quantitative mapping of oral mucosal lesions. Feasibility studies were performed on patient biopsy samples prior to histopathological processing using a commercial OCT microscope. Qualitative imaging results examining a variety of normal, benign, inflammatory and premalignant lesions of the oral mucosa will be presented. Furthermore, the identification and utilisation of a common quantifiable parameter in OCT and histology of images of normal and dysplastic oral epithelium will be explored thus ensuring objective and reproducible mapping of the progression of oral carcinogenesis. Finally, the selection of the most representative biopsy site of oral epithelial dysplasia would be investigated using a novel approach, scattering attenuation microscopy. It is hoped this approach may help convey more clinical meaning than the conventional visualisation of OCT images

Elemental and phase composition of breast calcifications

Despite the importance of calcifications in early detection of breast cancer, and their proposed association with tumour growth, remarkably little detail is known about their chemical composition, or how this relates to pathology. One reason for this gap is the difficulty of systematically and precisely locating calcifications for analysis, particularly in sections taken from diagnostic archives. Two simple methods were developed which can achieve this in sections cut from wax embedded breast tissue. These are based on micro-CT and x-ray fluoroscopy mapping, and were used to locate calcifications for further study. The elemental composition of calcifications in histological sections was measured using energy-dispersive x-ray spectroscopy in an environmental scanning electron microscope. Variations in Ca:P ratio could in principle be detected non-invasively by dual energy absorptiometry, as demonstrated in a proof of principle experiment. However, the Ca:P ratio was found to lie in a narrow range similar to bone, with no significant difference between benign and malignant. In contrast, a substantial and significant difference in Na:Ca ratio was found between benign and malignant specimens. This has potential for revealing malignant changes in the vicinity of a core needle biopsy. The phase composition and crystallographic parameters within calcifications was measured using synchrotron x-ray diffraction. This is the first time crystallite size and lattice parameters have been measured in breast calcifications, and it was found that these both parallel closely the changes in these parameters with age observed in foetal bone. It was also discovered that these calcifications contain a small proportion of magnesium whitlockite, and that this proportion increases from benign, to carcinoma in-situ, to invasive cancer. When combined with other recent evidence on the effect of magnesium on hydroxyapatite precipitation, this suggests a mechanism explaining observations that carbonate levels within breast calcifications are lower in malignant specimens

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology