Feature Selection for Computer-Aided Polyp Detection using MRMR

In building robust classifiers for computer-aided detection (CAD) of lesions, selection of relevant features is of fundamental importance. Typically one is interested in determining which, of a large number of potentially redundant or noisy features, are most discriminative for classification. Searching all possible subsets of features is impractical computationally. This paper proposes a feature selection scheme combining AdaBoost with the Minimum Redundancy Maximum Relevance (MRMR) to focus on the most discriminative features. A fitness function is designed to determine the optimal number of features in a forward wrapper search. Bagging is applied to reduce the variance of the classifier and make a reliable selection. Experiments demonstrate that by selecting just 11 percent of the total features, the classifier can achieve better prediction on independent test data compared to the 70 percent of the total features selected by AdaBoost

A Robust and Fast System for CTC Computer-Aided Detection of Colorectal Lesions

We present a complete, end-to-end computer-aided detection (CAD) system for identifying lesions in the colon, imaged with computed tomography (CT). This system includes facilities for colon segmentation, candidate generation, feature analysis, and classification. The algorithms have been designed to offer robust performance to variation in image data and patient preparation. By utilizing efficient 2D and 3D processing, software optimizations, multi-threading, feature selection, and an optimized cascade classifier, the CAD system quickly determines a set of detection marks. The colon CAD system has been validated on the largest set of data to date, and demonstrates excellent performance, in terms of its high sensitivity, low false positive rate, and computational efficiency

Framework for the detection and classification of colorectal polyps

In this thesis we propose a framework for the detection and classification of colorectal polyps to assist endoscopists in bowel cancer screening. Such a system will help reduce not only the miss rate of possibly malignant polyps during screening but also reduce the number of unnecessary polypectomies where the histopathologic analysis could be spared. Our polyp detection scheme is based on a cascade filter to pre-process the incoming video frames, select a group of candidate polyp regions and then proceed to algorithmically isolate the most probable polyps based on their geometry. We also tested this system on a number of endoscopic and capsule endoscopy videos collected with the help of our clinical collaborators. Furthermore, we developed and tested a classification system for distinguishing cancerous colorectal polyps from non-cancerous ones. By analyzing the surface vasculature of high magnification polyp images from two endoscopic platforms we extracted a number of features based primarily on the vessel contrast, orientation and colour. The feature space was then filtered as to leave only the most relevant subset and this was subsequently used to train our classifier. In addition, we examined the scenario of splitting up the polyp surface into patches and including only the most feature rich areas into our classifier instead of the surface as a whole. The stability of our feature space relative to patch size was also examined to ensure reliable and robust classification. In addition, we devised a scale selection strategy to minimize the effect of inconsistencies in magnification and geometric polyp size between samples. Lastly, several techniques were also employed to ensure that our results will generalise well in real world practise. We believe this to be a solid step in forming a toolbox designed to aid endoscopists not only in the detection but also in the optical biopsy of colorectal polyps during in vivo colonoscopy.Open Acces

A Deep Learning Approach for Multi-Omics Data Integration to Diagnose Early-Onset Colorectal Cancer

Colorectal cancer is one of the most common cancers and is a leading cause of death worldwide. It starts in the colon or the rectum, and they are often grouped together because they have many features in common. It has been noticed that colorectal cancer attacks young-onset patients who are less than 50 years of age in increasing rates lately. Rapid developments in omics technologies have led them to be highly regarded in the field of biomedical research for the early detection of cancer. Omics data revealed how different molecules and clinical features work together in the disease progression. However, Omics data sources are variants in nature and require careful preprocessing to be integrated. A convolutional neural network is a class of deep neural networks, commonly applied to analyze visual imagery. In this thesis, we propose a model that converts one-dimensional vectors of omics into RGB images to be integrated into the hidden layers of the convolutional neural network. The prediction model will allow all different omics to contribute to the decision making based on extracting the hidden interactions among these omics. These subsets of interacted omics can serve as potential biomarkers for young-onset colorectal cancer

Quantitative chemical imaging: A top-down systems pathology approach to predict colon cancer patient survival

Colon cancer is the second deadliest cancer, affecting the quality of life in older patients. Prognosis is useful in developing an informed disease management strategy, which can improve mortality as well as patient comfort. Morphometric assessment provides diagnosis, grade, and stage information. However, it is subjective, requires multi-step sample processing, and annotations by pathologists. In addition, morphometric techniques offer minimal molecular information that can be crucial in determining prognosis. The interaction of the tumor with its surrounding stroma, comprised of several biomolecular factors and cells is a critical determinant of the behavior of the disease. To evaluate this interaction objectively, we need biomolecular profiling in spatially specific context. In this work, we achieved this by analyzing tissue microarrays using infrared spectroscopic imaging. We developed supervised classification algorithms that were used to reliably segment colon tissue into histological components, including differentiation of normal and desmoplastic stroma. Thus, infrared spectroscopic imaging enabled us to map the stromal changes around the tumor. This supervised classification achieved >0.90 area under the curve of the receiver operating characteristic curve for pixel level classification. Using these maps, we sought to define evaluation criteria to assess the segmented colon images to determine prognosis. We measured the interaction of tumor with the surrounding stroma containing activated fibroblast in the form of mathematical functions that took into account the structure of tumor and the prevalence of reactive stroma. Using these functions, we found that the interaction effect of large tumor size in the presence of a high density of activated fibroblasts provided patients with worse outcome. The overall 6-year probability of survival in patient groups that were classified as “low-risk” was 0.73 whereas in patients that were “high-risk” was 0.54 at p-value <0.0003. Remarkably, the risk score defined in this work was independent of patient risk assessed by stage and grade of the tumor. Thus, objective evaluation of prognosis, which adds to the current clinical regimen, was achieved by a completely automated analysis of unstained patient tissue to determine the risk of 6-year death. In this work, we demonstrate that quantitative chemical imaging using infrared spectroscopic imaging is an effective method to measure tumor-tumor microenvironment interactions. As a top-down systems pathology approach, our work integrated morphometry based spatial constraints and biochemistry based stromal changes to identify markers that gave us mechanistic insights into the tumor behavior. Our work shows that while the tumor microenvironment changes are prognostic, an interaction model that takes into account both the extent of microenvironment modifications, as well as the tumor morphology, is a better predictor of prognosis. Finally, we also developed automated tumor grade determination using deep learning based infrared image analysis. Thus, the computational models developed in this work provide an objective, processing-free and automated way to predict tumor behavior

Color and morphological features extraction and nuclei classification in tissue samples of colorectal cancer

Cancer is an important public health problem and the third most leading cause of death in North America. Among the highest impact types of cancer are colorectal, breast, lung, and prostate. This thesis addresses the features extraction by using different artificial intelligence algorithms that provide distinct solutions for the purpose of Computer-AidedDiagnosis (CAD). For example, classification algorithms are employed in identifying histological structures, such as lymphocytes, cancer-cells nuclei and glands, from features like existence, extension or shape. The morphological aspect of these structures indicates the degree of severity of the related disease. In this paper, we use a large dataset of 5000 images to classify eight different tissue types in the case of colorectal cancer. We compare results with another dataset. We perform image segmentation and extract statistical information about the area, perimeter, circularity, eccentricity and solidity of the interest points in the image. Finally, we use and compare four popular machine learning techniques, i.e., Naive Bayes, Random Forest, Support Vector Machine and Multilayer Perceptron to classify and to improve the precision of category assignation. The performance of each algorithm was measured using 3 types of metrics: Precision, recall and F1-Score representing a huge contribution to the existing literature complementing it in a quantitative way. The large number of images has helped us to circumvent the overfitting and reproducibility problems. The main contribution is the use of new characteristics different from those already studied, this work researches about the color and morphological characteristics in the images that may be useful for performing tissue classification in colorectal cancer histology

Algorithms for Fluorescence Lifetime Microscopy and Optical Coherence Tomography Data Analysis: Applications for Diagnosis of Atherosclerosis and Oral Cancer

With significant progress made in the design and instrumentation of optical imaging systems, it is now possible to perform high-resolution tissue imaging in near real-time. The prohibitively large amount of data obtained from such high-speed imaging systems precludes the possibility of manual data analysis by an expert. The paucity of algorithms for automated data analysis has been a major roadblock in both evaluating and harnessing the full potential of optical imaging modalities for diagnostic applications. This consideration forms the central theme of the research presented in this dissertation. Specifically, we investigate the potential of automated analysis of data acquired from a multimodal imaging system that combines fluorescence lifetime imaging (FLIM) with optical coherence tomography (OCT), for the diagnosis of atherosclerosis and oral cancer. FLIM is a fluorescence imaging technique that is capable of providing information about auto fluorescent tissue biomolecules. OCT on the other hand, is a structural imaging modality that exploits the intrinsic reflectivity of tissue samples to provide high resolution 3-D tomographic images. Since FLIM and OCT provide complimentary information about tissue biochemistry and structure, respectively, we hypothesize that the combined information from the multimodal system would increase the sensitivity and specificity for the diagnosis of atherosclerosis and oral cancer. The research presented in this dissertation can be divided into two main parts. The first part concerns the development and applications of algorithms for providing quantitative description of FLIM and OCT images. The quantitative FLIM and OCT features obtained in the first part of the research, are subsequently used to perform automated tissue diagnosis based on statistical classification models. The results of the research presented in this dissertation show the feasibility of using automated algorithms for FLIM and OCT data analysis for performing tissue diagnosis