Modeling small objects under uncertainties : novel algorithms and applications.

Active Shape Models (ASM), Active Appearance Models (AAM) and Active Tensor Models (ATM) are common approaches to model elastic (deformable) objects. These models require an ensemble of shapes and textures, annotated by human experts, in order identify the model order and parameters. A candidate object may be represented by a weighted sum of basis generated by an optimization process. These methods have been very effective for modeling deformable objects in biomedical imaging, biometrics, computer vision and graphics. They have been tried mainly on objects with known features that are amenable to manual (expert) annotation. They have not been examined on objects with severe ambiguities to be uniquely characterized by experts. This dissertation presents a unified approach for modeling, detecting, segmenting and categorizing small objects under uncertainty, with focus on lung nodules that may appear in low dose CT (LDCT) scans of the human chest. The AAM, ASM and the ATM approaches are used for the first time on this application. A new formulation to object detection by template matching, as an energy optimization, is introduced. Nine similarity measures of matching have been quantitatively evaluated for detecting nodules less than 1 em in diameter. Statistical methods that combine intensity, shape and spatial interaction are examined for segmentation of small size objects. Extensions of the intensity model using the linear combination of Gaussians (LCG) approach are introduced, in order to estimate the number of modes in the LCG equation. The classical maximum a posteriori (MAP) segmentation approach has been adapted to handle segmentation of small size lung nodules that are randomly located in the lung tissue. A novel empirical approach has been devised to simultaneously detect and segment the lung nodules in LDCT scans. The level sets methods approach was also applied for lung nodule segmentation. A new formulation for the energy function controlling the level set propagation has been introduced taking into account the specific properties of the nodules. Finally, a novel approach for classification of the segmented nodules into categories has been introduced. Geometric object descriptors such as the SIFT, AS 1FT, SURF and LBP have been used for feature extraction and matching of small size lung nodules; the LBP has been found to be the most robust. Categorization implies classification of detected and segmented objects into classes or types. The object descriptors have been deployed in the detection step for false positive reduction, and in the categorization stage to assign a class and type for the nodules. The AAMI ASMI A TM models have been used for the categorization stage. The front-end processes of lung nodule modeling, detection, segmentation and classification/categorization are model-based and data-driven. This dissertation is the first attempt in the literature at creating an entirely model-based approach for lung nodule analysis

An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification

While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features, and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches

CAD system for lung nodule analysis.

Lung cancer is the deadliest type of known cancer in the United States, claiming hundreds of thousands of lives each year. However, despite the high mortality rate, the 5-year survival rate after resection of Stage 1A non–small cell lung cancer is currently in the range of 62%– 82% and in recent studies even 90%. Patient survival is highly correlated with early detection. Computed Tomography (CT) technology services the early detection of lung cancer tremendously by offering a minimally invasive medical diagnostic tool. Some early types of lung cancer begin with a small mass of tissue within the lung, less than 3 cm in diameter, called a nodule. Most nodules found in a lung are benign, but a small population of them becomes malignant over time. Expert analysis of CT scans is the first step in determining whether a nodule presents a possibility for malignancy but, due to such low spatial support, many potentially harmful nodules go undetected until other symptoms motivate a more thorough search. Computer Vision and Pattern Recognition techniques can play a significant role in aiding the process of detecting and diagnosing lung nodules. This thesis outlines the development of a CAD system which, given an input CT scan, provides a functional and fast, second-opinion diagnosis to physicians. The entire process of lung nodule screening has been cast as a system, which can be enhanced by modern computing technology, with the hopes of providing a feasible diagnostic tool for clinical use. It should be noted that the proposed CAD system is presented as a tool for experts—not a replacement for them. The primary motivation of this thesis is the design of a system that could act as a catalyst for reducing the mortality rate associated with lung cancer

Lung nodule modeling and detection for computerized image analysis of low dose CT imaging of the chest.

From a computerized image analysis prospective, early diagnosis of lung cancer involves detection of doubtful nodules and classification into different pathologies. The detection stage involves a detection approach, usually by template matching, and an authentication step to reduce false positives, usually conducted by a classifier of one form or another; statistical, fuzzy logic, support vector machines approaches have been tried. The classification stage matches, according to a particular approach, the characteristics (e.g., shape, texture and spatial distribution) of the detected nodules to common characteristics (again, shape, texture and spatial distribution) of nodules with known pathologies (confirmed by biopsies). This thesis focuses on the first step; i.e., nodule detection. Specifically, the thesis addresses three issues: a) understanding the CT data of typical low dose CT (LDCT) scanning of the chest, and devising an image processing approach to reduce the inherent artifacts in the scans; b) devising an image segmentation approach to isolate the lung tissues from the rest of the chest and thoracic regions in the CT scans; and c) devising a nodule modeling methodology to enhance the detection rate and lend benefits for the ultimate step in computerized image analysis of LDCT of the lungs, namely associating a pathology to the detected nodule. The methodology for reducing the noise artifacts is based on noise analysis and examination of typical LDCT scans that may be gathered on a repetitive fashion; since, a reduction in the resolution is inevitable to avoid excessive radiation. Two optimal filtering methods are tested on samples of the ELCAP screening data; the Weiner and the Anisotropic Diffusion Filters. Preference is given to the Anisotropic Diffusion Filter, which can be implemented on 7x7 blocks/windows of the CT data. The methodology for lung segmentation is based on the inherent characteristics of the LDCT scans, shown as distinct bi-modal gray scale histogram. A linear model is used to describe the histogram (the joint probability density function of the lungs and non-lungs tissues) by a linear combination of weighted kernels. The Gaussian kernels were chosen, and the classic Expectation-Maximization (EM) algorithm was employed to estimate the marginal probability densities of the lungs and non-lungs tissues, and select an optimal segmentation threshold. The segmentation is further enhanced using standard shape analysis based on mathematical morphology, which improves the continuity of the outer and inner borders of the lung tissues. This approach (a preliminary version of it appeared in [14]) is found to be adequate for lung segmentation as compared to more sophisticated approaches developed at the CVIP Lab (e.g., [15][16]) and elsewhere. The methodology developed for nodule modeling is based on understanding the physical characteristics of the nodules in LDCT scans, as identified by human experts. An empirical model is introduced for the probability density of the image intensity (or Hounsfield units) versus the radial distance measured from the centroid – center of mass - of typical nodules. This probability density showed that the nodule spatial support is within a circle/square of size 10 pixels; i.e., limited to 5 mm in length; which is within the range that the radiologist specify to be of concern. This probability density is used to fill in the intensity (or Hounsfield units) of parametric nodule models. For these models (e.g., circles or semi-circles), given a certain radius, we calculate the intensity (or Hounsfield units) using an exponential expression for the radial distance with parameters specified from the histogram of an ensemble of typical nodules. This work is similar in spirit to the earlier work of Farag et al., 2004 and 2005 [18][19], except that the empirical density of the radial distance and the histogram of typical nodules provide a data-driven guide for estimating the intensity (or Hounsfield units) of the nodule models. We examined the sensitivity and specificity of parametric nodules in a template-matching framework for nodule detection. We show that false positives are inevitable problems with typical machine learning methods of automatic lung nodule detection, which invites further efforts and perhaps fresh thinking into automatic nodule detection. A new approach for nodule modeling is introduced in Chapter 5 of this thesis, which brings high promise in both the detection, and the classification of nodules. Using the ELCAP study, we created an ensemble of four types of nodules and generated a nodule model for each type based on optimal data reduction methods. The resulting nodule model, for each type, has lead to drastic improvements in the sensitivity and specificity of nodule detection. This approach may be used as well for classification. In conclusion, the methodologies in this thesis are based on understanding the LDCT scans and what is to be expected in terms of image quality. Noise reduction and image segmentation are standard. The thesis illustrates that proper nodule models are possible and indeed a computerized approach for image analysis to detect and classify lung nodules is feasible. Extensions to the results in this thesis are immediate and the CVIP Lab has devised plans to pursue subsequent steps using clinical data