Lung nodules identification in CT scans using multiple instance learning.

Computer Aided Diagnosis (CAD) systems for lung nodules diagnosis aim to classify nodules into benign or malignant based on images obtained from diverse imaging modalities such as Computer Tomography (CT). Automated CAD systems are important in medical domain applications as they assist radiologists in the time-consuming and labor-intensive diagnosis process. However, most available methods require a large collection of nodules that are segmented and annotated by radiologists. This process is labor-intensive and hard to scale to very large datasets. More recently, some CAD systems that are based on deep learning have emerged. These algorithms do not require the nodules to be segmented, and radiologists need to only provide the center of mass of each nodule. The training image patches are then extracted from volumes of fixed-sized centered at the provided nodule\u27s center. However, since the size of nodules can vary significantly, one fixed size volume may not represent all nodules effectively. This thesis proposes a Multiple Instance Learning (MIL) approach to address the above limitations. In MIL, each nodule is represented by a nested sequence of volumes centered at the identified center of the nodule. We extract one feature vector from each volume. The set of features for each nodule are combined and represented by a bag. Next, we investigate and adapt some existing algorithms and develop new ones for this application. We start by applying benchmark MIL algorithms to traditional Gray Level Co-occurrence Matrix (GLCM) engineered features. Then, we design and train simple Convolutional Neural Networks (CNNs) to learn and extract features that characterize lung nodules. These extracted features are then fed to a benchmark MIL algorithm to learn a classification model. Finally, we develop new algorithms (MIL-CNN) that combine feature learning and multiple instance classification in a single network. These algorithms generalize the CNN architecture to multiple instance data. We design and report the results of three experiments applied on both generative (GLCM) and learned (CNN) features using two datasets (The Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) \cite{armato2011lung} and the National Lung Screening Trial (NLST) \cite{national2011reduced}). Two of these experiments perform five-fold cross-validations on the same dataset (NLST or LIDC). The third experiment trains the algorithms on one collection (NLST dataset) and tests it on the other (LIDC dataset). We designed our experiments to compare the different features, compare MIL versus Single Instance Learning (SIL) where a single feature vector represents a nodule, and compare our proposed end-to-end MIL approaches to existing benchmark MIL methods. We demonstrate that our proposed MIL-CNN frameworks are more accurate for the lung nodules diagnosis task. We also show that MIL representation achieves better results than SIL applied on the ground truth region of each nodule

Pulmonary Nodule Classification in Lung Cancer from 3D Thoracic CT Scans Using fastai and MONAI

We construct a convolutional neural network to classify pulmonary nodules as malignant or benign in the context of lung cancer. To construct and train our model, we use our novel extension of the fastai deep learning framework to 3D medical imaging tasks, combined with the MONAI deep learning library. We train and evaluate the model using a large, openly available data set of annotated thoracic CT scans. Our model achieves a nodule classification accuracy of 92.4% and a ROC AUC of 97% when compared to a “ground truth” based on multiple human raters subjective assessment of malignancy. We further evaluate our approach by predicting patient-level diagnoses of cancer, achieving a test set accuracy of 75%. This is higher than the 70% obtained by aggregating the human raters assessments. Class activation maps are applied to investigate the features used by our classifier, enabling a rudimentary level of explainability for what is otherwise close to “black box” predictions. As the classification of structures in chest CT scans is useful across a variety of diagnostic and prognostic tasks in radiology, our approach has broad applicability. As we aimed to construct a fully reproducible system that can be compared to new proposed methods and easily be adapted and extended, the full source code of our work is available at https://github.com/MMIV-ML/Lung-CT-fastai-2020

Towards generalizable machine learning models for computer-aided diagnosis in medicine

Hidden stratification represents a phenomenon in which a training dataset contains unlabeled (hidden) subsets of cases that may affect machine learning model performance. Machine learning models that ignore the hidden stratification phenomenon--despite promising overall performance measured as accuracy and sensitivity--often fail at predicting the low prevalence cases, but those cases remain important. In the medical domain, patients with diseases are often less common than healthy patients, and a misdiagnosis of a patient with a disease can have significant clinical impacts. Therefore, to build a robust and trustworthy CAD system and a reliable treatment effect prediction model, we cannot only pursue machine learning models with high overall accuracy, but we also need to discover any hidden stratification in the data and evaluate the proposing machine learning models with respect to both overall performance and the performance on certain subsets (groups) of the data, such as the ‘worst group’. In this study, I investigated three approaches for data stratification: a novel algorithmic deep learning (DL) approach that learns similarities among cases and two schema completion approaches that utilize domain expert knowledge. I further proposed an innovative way to integrate the discovered latent groups into the loss functions of DL models to allow for better model generalizability under the domain shift scenario caused by the data heterogeneity. My results on lung nodule Computed Tomography (CT) images and breast cancer histopathology images demonstrate that learning homogeneous groups within heterogeneous data significantly improves the performance of the computer-aided diagnosis (CAD) system, particularly for low-prevalence or worst-performing cases. This study emphasizes the importance of discovering and learning the latent stratification within the data, as it is a critical step towards building ML models that are generalizable and reliable. Ultimately, this discovery can have a profound impact on clinical decision-making, particularly for low-prevalence cases

Machine learning approaches for lung cancer diagnosis.

The enormity of changes and development in the field of medical imaging technology is hard to fathom, as it does not just represent the technique and process of constructing visual representations of the body from inside for medical analysis and to reveal the internal structure of different organs under the skin, but also it provides a noninvasive way for diagnosis of various disease and suggest an efficient ways to treat them. While data surrounding all of our lives are stored and collected to be ready for analysis by data scientists, medical images are considered a rich source that could provide us with a huge amount of data, that could not be read easily by physicians and radiologists, with valuable information that could be used in smart ways to discover new knowledge from these vast quantities of data. Therefore, the design of computer-aided diagnostic (CAD) system, that can be approved for use in clinical practice that aid radiologists in diagnosis and detecting potential abnormalities, is of a great importance. This dissertation deals with the development of a CAD system for lung cancer diagnosis, which is the second most common cancer in men after prostate cancer and in women after breast cancer. Moreover, lung cancer is considered the leading cause of cancer death among both genders in USA. Recently, the number of lung cancer patients has increased dramatically worldwide and its early detection doubles a patient’s chance of survival. Histological examination through biopsies is considered the gold standard for final diagnosis of pulmonary nodules. Even though resection of pulmonary nodules is the ideal and most reliable way for diagnosis, there is still a lot of different methods often used just to eliminate the risks associated with the surgical procedure. Lung nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. A pulmonary nodule is the first indication to start diagnosing lung cancer. Lung nodules can be benign (normal subjects) or malignant (cancerous subjects). Large (generally defined as greater than 2 cm in diameter) malignant nodules can be easily detected with traditional CT scanning techniques. However, the diagnostic options for small indeterminate nodules are limited due to problems associated with accessing small tumors. Therefore, additional diagnostic and imaging techniques which depends on the nodules’ shape and appearance are needed. The ultimate goal of this dissertation is to develop a fast noninvasive diagnostic system that can enhance the accuracy measures of early lung cancer diagnosis based on the well-known hypotheses that malignant nodules have different shape and appearance than benign nodules, because of the high growth rate of the malignant nodules. The proposed methodologies introduces new shape and appearance features which can distinguish between benign and malignant nodules. To achieve this goal a CAD system is implemented and validated using different datasets. This CAD system uses two different types of features integrated together to be able to give a full description to the pulmonary nodule. These two types are appearance features and shape features. For the appearance features different texture appearance descriptors are developed, namely the 3D histogram of oriented gradient, 3D spherical sector isosurface histogram of oriented gradient, 3D adjusted local binary pattern, 3D resolved ambiguity local binary pattern, multi-view analytical local binary pattern, and Markov Gibbs random field. Each one of these descriptors gives a good description for the nodule texture and the level of its signal homogeneity which is a distinguishable feature between benign and malignant nodules. For the shape features multi-view peripheral sum curvature scale space, spherical harmonics expansions, and different group of fundamental geometric features are utilized to describe the nodule shape complexity. Finally, the fusion of different combinations of these features, which is based on two stages is introduced. The first stage generates a primary estimation for every descriptor. Followed by the second stage that consists of an autoencoder with a single layer augmented with a softmax classifier to provide us with the ultimate classification of the nodule. These different combinations of descriptors are combined into different frameworks that are evaluated using different datasets. The first dataset is the Lung Image Database Consortium which is a benchmark publicly available dataset for lung nodule detection and diagnosis. The second dataset is our local acquired computed tomography imaging data that has been collected from the University of Louisville hospital and the research protocol was approved by the Institutional Review Board at the University of Louisville (IRB number 10.0642). These frameworks accuracy was about 94%, which make the proposed frameworks demonstrate promise to be valuable tool for the detection of lung cancer

Predicting Panel Ratings for Semantic Characteristics of Lung Nodules

In reading CT scans with potentially malignant lung nodules, radiologists make use of high level information (semantic characteristics) in their analysis. CAD systems can assist radiologists by offering a “second opinion” - predicting these semantic characteristics for lung nodules. In our previous work, we developed such a CAD system, training and testing it on the publicly available Lung Image Database Consortium (LIDC) dataset, which includes semantic annotations by up to four human radiologists for every nodule. However, due to the lack of ground truth and the uncertainty in the dataset, each nodule was viewed as four distinct instances when training the classifier. In this work, we propose a way of predicting the distribution of opinions of the four radiologists using a multiple-label classification algorithm based on belief decision trees. We evaluate our results using a distance-threshold curve and, measuring the area under this curve, obtain 69% accuracy on the testing subset. We conclude that multiple-label classification algorithms are an appropriate method of representing the diagnoses of multiple radiologists on lung CT scans when a single ground truth is not available

Learning Algorithms for Fat Quantification and Tumor Characterization

Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice

Advanced machine learning methods for oncological image analysis

Cancer is a major public health problem, accounting for an estimated 10 million deaths worldwide in 2020 alone. Rapid advances in the field of image acquisition and hardware development over the past three decades have resulted in the development of modern medical imaging modalities that can capture high-resolution anatomical, physiological, functional, and metabolic quantitative information from cancerous organs. Therefore, the applications of medical imaging have become increasingly crucial in the clinical routines of oncology, providing screening, diagnosis, treatment monitoring, and non/minimally- invasive evaluation of disease prognosis. The essential need for medical images, however, has resulted in the acquisition of a tremendous number of imaging scans. Considering the growing role of medical imaging data on one side and the challenges of manually examining such an abundance of data on the other side, the development of computerized tools to automatically or semi-automatically examine the image data has attracted considerable interest. Hence, a variety of machine learning tools have been developed for oncological image analysis, aiming to assist clinicians with repetitive tasks in their workflow. This thesis aims to contribute to the field of oncological image analysis by proposing new ways of quantifying tumor characteristics from medical image data. Specifically, this thesis consists of six studies, the first two of which focus on introducing novel methods for tumor segmentation. The last four studies aim to develop quantitative imaging biomarkers for cancer diagnosis and prognosis. The main objective of Study I is to develop a deep learning pipeline capable of capturing the appearance of lung pathologies, including lung tumors, and integrating this pipeline into the segmentation networks to leverage the segmentation accuracy. The proposed pipeline was tested on several comprehensive datasets, and the numerical quantifications show the superiority of the proposed prior-aware DL framework compared to the state of the art. Study II aims to address a crucial challenge faced by supervised segmentation models: dependency on the large-scale labeled dataset. In this study, an unsupervised segmentation approach is proposed based on the concept of image inpainting to segment lung and head- neck tumors in images from single and multiple modalities. The proposed autoinpainting pipeline shows great potential in synthesizing high-quality tumor-free images and outperforms a family of well-established unsupervised models in terms of segmentation accuracy. Studies III and IV aim to automatically discriminate the benign from the malignant pulmonary nodules by analyzing the low-dose computed tomography (LDCT) scans. In Study III, a dual-pathway deep classification framework is proposed to simultaneously take into account the local intra-nodule heterogeneities and the global contextual information. Study IV seeks to compare the discriminative power of a series of carefully selected conventional radiomics methods, end-to-end Deep Learning (DL) models, and deep features-based radiomics analysis on the same dataset. The numerical analyses show the potential of fusing the learned deep features into radiomic features for boosting the classification power. Study V focuses on the early assessment of lung tumor response to the applied treatments by proposing a novel feature set that can be interpreted physiologically. This feature set was employed to quantify the changes in the tumor characteristics from longitudinal PET-CT scans in order to predict the overall survival status of the patients two years after the last session of treatments. The discriminative power of the introduced imaging biomarkers was compared against the conventional radiomics, and the quantitative evaluations verified the superiority of the proposed feature set. Whereas Study V focuses on a binary survival prediction task, Study VI addresses the prediction of survival rate in patients diagnosed with lung and head-neck cancer by investigating the potential of spherical convolutional neural networks and comparing their performance against other types of features, including radiomics. While comparable results were achieved in intra- dataset analyses, the proposed spherical-based features show more predictive power in inter-dataset analyses. In summary, the six studies incorporate different imaging modalities and a wide range of image processing and machine-learning techniques in the methods developed for the quantitative assessment of tumor characteristics and contribute to the essential procedures of cancer diagnosis and prognosis

Computational methods for the analysis of functional 4D-CT chest images.

Medical imaging is an important emerging technology that has been intensively used in the last few decades for disease diagnosis and monitoring as well as for the assessment of treatment effectiveness. Medical images provide a very large amount of valuable information that is too huge to be exploited by radiologists and physicians. Therefore, the design of computer-aided diagnostic (CAD) system, which can be used as an assistive tool for the medical community, is of a great importance. This dissertation deals with the development of a complete CAD system for lung cancer patients, which remains the leading cause of cancer-related death in the USA. In 2014, there were approximately 224,210 new cases of lung cancer and 159,260 related deaths. The process begins with the detection of lung cancer which is detected through the diagnosis of lung nodules (a manifestation of lung cancer). These nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. The treatment of these lung cancer nodules is complex, nearly 70% of lung cancer patients require radiation therapy as part of their treatment. Radiation-induced lung injury is a limiting toxicity that may decrease cure rates and increase morbidity and mortality treatment. By finding ways to accurately detect, at early stage, and hence prevent lung injury, it will have significant positive consequences for lung cancer patients. The ultimate goal of this dissertation is to develop a clinically usable CAD system that can improve the sensitivity and specificity of early detection of radiation-induced lung injury based on the hypotheses that radiated lung tissues may get affected and suffer decrease of their functionality as a side effect of radiation therapy treatment. These hypotheses have been validated by demonstrating that automatic segmentation of the lung regions and registration of consecutive respiratory phases to estimate their elasticity, ventilation, and texture features to provide discriminatory descriptors that can be used for early detection of radiation-induced lung injury. The proposed methodologies will lead to novel indexes for distinguishing normal/healthy and injured lung tissues in clinical decision-making. To achieve this goal, a CAD system for accurate detection of radiation-induced lung injury that requires three basic components has been developed. These components are the lung fields segmentation, lung registration, and features extraction and tissue classification. This dissertation starts with an exploration of the available medical imaging modalities to present the importance of medical imaging in today’s clinical applications. Secondly, the methodologies, challenges, and limitations of recent CAD systems for lung cancer detection are covered. This is followed by introducing an accurate segmentation methodology of the lung parenchyma with the focus of pathological lungs to extract the volume of interest (VOI) to be analyzed for potential existence of lung injuries stemmed from the radiation therapy. After the segmentation of the VOI, a lung registration framework is introduced to perform a crucial and important step that ensures the co-alignment of the intra-patient scans. This step eliminates the effects of orientation differences, motion, breathing, heart beats, and differences in scanning parameters to be able to accurately extract the functionality features for the lung fields. The developed registration framework also helps in the evaluation and gated control of the radiotherapy through the motion estimation analysis before and after the therapy dose. Finally, the radiation-induced lung injury is introduced, which combines the previous two medical image processing and analysis steps with the features estimation and classification step. This framework estimates and combines both texture and functional features. The texture features are modeled using the novel 7th-order Markov Gibbs random field (MGRF) model that has the ability to accurately models the texture of healthy and injured lung tissues through simultaneously accounting for both vertical and horizontal relative dependencies between voxel-wise signals. While the functionality features calculations are based on the calculated deformation fields, obtained from the 4D-CT lung registration, that maps lung voxels between successive CT scans in the respiratory cycle. These functionality features describe the ventilation, the air flow rate, of the lung tissues using the Jacobian of the deformation field and the tissues’ elasticity using the strain components calculated from the gradient of the deformation field. Finally, these features are combined in the classification model to detect the injured parts of the lung at an early stage and enables an earlier intervention