Characterizing bladder cancer cells by comparing general machine learning methods to convolutional neural network

Recently, deep learning techniques from the computer science field have dramatically improved the ability of computers to recognize objects in images. This raised the possibility of fully automated computer-aided diagnosis in the medical field. Among all the machine learning models, convolutional neural network (CNN) is one of the most studied and validated artificial neural networks in image recognition. Not only that it has great performance, but the design of most modern CNN hidden layers also allows the model to extract meaningful features without the needs of prior knowledge. Thus, the pathology community is showing increasing interests in comparing CNN to human judgments. As demonstrated in a number of studies reporting various image analysis models that can accurately localize and characterize cells into different cell types and predict patient outcome, the pathological field is incorporating artificial intelligence technologies into their diagnosis. Although using the deep neural network on recognizing pathological slides is not a new idea and is showing promising results, its requirement of a large quantity of data for training can be a big obstacle for many unpopular histopathological cases. In the bladder cancer field, the Tumor-Node-Metastasis (TNM) system defines T1 bladder cancer as the invasion of tumor cells into the lamina propria (LP). However, pathologists often struggle to confirm LP and/or muscularis mucosae invasion using hematoxylin & eosin (H&E) stains from bladder biopsies. Accurately reporting the presence of tumor invasion, which is associated with worse clinical outcomes, is critical for adequate patient management. In this thesis, we have developed various traditional machine learning models and compared their performances to 2 convolutional neural networks (CNN), VGG16 and VGG19, on histology image classification in distinguishing non-invasive versus invasive bladder tumors. By using approximately 1,200 H&E images from non-invasive and invasive bladder cancer tissues, our results showed the traditional machine learning methods with the human-directed features outperformed the fully automated CNN model as much as 12%. For 2-class classification task to distinguish non-invasive and invasive bladder cancer tissues, we achieved around 91~96% accuracy by using classic machine learning classifiers such as random forest, logistic regression, and probabilistic neural network. Whereas, CNN with VGG16 as hidden layers only achieved around 84%. In addition to performance, because of the transparency of features extraction in the pipeline, we were able to evaluate and rank the patterns in the bladder histological images. As based on their relative importance in prediction, classic machine learning methods provided a well-rounded approach under limited data size

Experimental Investigation for Detecting Mitotic Cells in Medical Image using an Automated Algorithm

Cancer of the breast is a malignant tumour that originates in the cells of the breast tissue. It is by far the most common kind of cancer found in females around the world, with a projected 2.3 million new cases will be discovered in the year 2020 alone. It is projected that one in eight women will be diagnosed with breast cancer at some point in their life, despite the fact that breast cancer can also occur in men. Breast cancer is a complex condition that can arise from a diverse set of factors, express itself in a variety of ways, and can be treated in a variety of ways. Ductal carcinoma in situ, invasive ductal carcinoma, and invasive lobular carcinoma are all different subtypes. Both the available treatment options and the expected outcome of breast cancer are very variable depending on the particular subtype of the illness. Breast cancer risk factors include drinking alcohol and not getting enough exercise, as well as getting older, having a family history of the disease, having genetic mutations, being exposed to estrogens, and having a family history of the disease. There is not always a connection between having risk factors and developing breast cancer, despite the fact that there can be a link between the two. The prognosis and treatment options for breast cancer are highly dependent on the stage of the disease at the time of diagnosis. During staging, the extent to which the cancer has spread throughout the body and how far it has progressed are both measured. The TNM system, the IAFCM system, the ACM system, and the MPIG system are just few of the staging systems that are used to classify breast cancer. These staging systems consider not only the size of the tumor but also whether or not lymph nodes are involved and whether or not distant metastases are present. The severity of breast cancer symptoms can vary widely, depending not only on the subtype of the disease but also on how far along it has progressed. Alterations in the size or shape of the breast, discharge from the nipple, and alterations in the skin of the breast (such as redness or dimpling) are all common indications. On the other hand, not all cases of breast cancer present themselves in a visible manner, and mammography and other forms of routine screening may be able to detect some of these cases. Options for treating breast cancer vary depending on the patient's condition and the stage of the disease, as well as the patient's overall health and their preferences towards therapy. Common examples of medical interventions include surgery, radiotherapy, chemotherapy, hormone therapy, and targeted therapy. Other examples include. In certain cases, it may be appropriate to participate in more than one form of treatment

Improving cancer subtype diagnosis and grading using clinical decision support system based on computer-aided tissue image analysis

This research focuses towards the development of a clinical decision support system (CDSS) based on cellular and tissue image analysis and classification system that improves consistency and facilitates the clinical decision making process. In a typical cancer examination, pathologists make diagnosis by manually reading morphological features in patient biopsy images, in which cancer biomarkers are highlighted by using different staining techniques. This process is subjected to pathologist's training and experience, especially when the same cancer has several subtypes (i.e. benign tumor subtype vs. malignant subtype) and the same cancer tissue biopsy contains heterogeneous morphologies in different locations. The variability in pathologist's manual reading may result in varying cancer diagnosis and treatment. This Ph.D. research aims to reduce the subjectivity and variation existing in traditional histo-pathological reading of patient tissue biopsy slides through Computer-Aided Diagnosis (CAD). Using the CAD, quantitative molecular profiling of cancer biomarkers of stained biopsy images are obtained by extracting and analyzing texture and cellular structure features. In addition, cancer sub-type classification and a semi-automatic grade scoring (i.e. clinical decision making) for improved consistency over a large number of cancer subtype images can be performed. The CAD tools do have their own limitations and in certain cases the clinicians, however, prefer systems which are flexible and take into account their individuality when necessary by providing some control rather than fully automated system. Therefore, to be able to introduce CDSS in health care, we need to understand users' perspectives and preferences on the new information technology. This forms as the basis for this research where we target to present the quantitative information acquired through the image analysis, annotate the images and provide suitable visualization which can facilitate the process of decision making in a clinical setting.PhDCommittee Chair: Dr. May D. Wang; Committee Member: Dr. Andrew N. Young; Committee Member: Dr. Anthony J. Yezzi; Committee Member: Dr. Edward J. Coyle; Committee Member: Dr. Paul Benkese

A Segmentation Method for fluorescence images without a machine learning approach

Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps, and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing Indirect ImmunoFluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is far from the conventional neural network approach, but it is equivalent to their quantitative and qualitative performance, and it is also solid to adversative noise. The method is robust, based on formally correct functions, and does not suffer from tuning on specific data sets. Results: This work demonstrates the robustness of the method against the variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on two datasets (Neuroblastoma and NucleusSegData) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional to a structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) to segment cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches.Comment: 25 page

Probing structural and dynamic properties of trafficking subcellular nanostructures by spatiotemporal fluctuation spectroscopy

Imaging-derived mean square displacement (iMSD) is used to address the structural and dynamic properties of subcellular nanostructures, such as vesicles involved in the endo/exocytotic trafficking of solutes and biomolecules. iMSD relies on standard time-lapse imaging, is compatible with any optical setup, and does not need to dwell on single objects to extract trajectories. From each iMSD trace, a unique triplet of average structural and dynamic parameters (i.e., size, local diffusivity, anomalous coefficient) is calculated and combined to build the "iMSD signature" of the nanostructure under study. The potency of this approach is proved here with the exemplary case of macropinosomes. These vesicles evolve in time, changing their average size, number, and dynamic properties passing from early to late stages of intracellular trafficking. As a control, insulin secretory granules (ISGs) are used as a reference for subcellular structures that live in a stationary state in which the average structural and dynamic properties of the whole population of objects are invariant in time. The iMSD analysis highlights these peculiar features quantitatively and paves the way to similar applications at the sub-cellular level, both in the physiological and pathological states

Insulin secretory granules labelled with phogrin-fluorescent proteins show alterations in size, mobility and responsiveness to glucose stimulation in living β-cells

The intracellular life of insulin secretory granules (ISGs) from biogenesis to secretion depends on their structural (e.g. size) and dynamic (e.g. diffusivity, mode of motion) properties. Thus, it would be useful to have rapid and robust measurements of such parameters in living β-cells. To provide such measurements, we have developed a fast spatiotemporal fluctuation spectroscopy. We calculate an imaging-derived Mean Squared Displacement (iMSD), which simultaneously provides the size, average diffusivity, and anomalous coefficient of ISGs, without the need to extract individual trajectories. Clustering of structural and dynamic quantities in a multidimensional parametric space defines the ISGs’ properties for different conditions. First, we create a reference using INS-1E cells expressing proinsulin fused to a fluorescent protein (FP) under basal culture conditions and validate our analysis by testing well-established stimuli, such as glucose intake, cytoskeleton disruption, or cholesterol overload. After, we investigate the effect of FP-tagged ISG protein markers on the structural and dynamic properties of the granule. While iMSD analysis produces similar results for most of the lumenal markers, the transmembrane marker phogrin-FP shows a clearly altered result. Phogrin overexpression induces a substantial granule enlargement and higher mobility, together with a partial de-polymerization of the actin cytoskeleton, and reduced cell responsiveness to glucose stimulation. Our data suggest a more careful interpretation of many previous ISG-based reports in living β-cells. The presented data pave the way to high-throughput cell-based screening of ISG structure and dynamics under various physiological and pathological conditions