Segmentation and classification of lung nodules from Thoracic CT scans : methods based on dictionary learning and deep convolutional neural networks.

Lung cancer is a leading cause of cancer death in the world. Key to survival of patients is early diagnosis. Studies have demonstrated that screening high risk patients with Low-dose Computed Tomography (CT) is invaluable for reducing morbidity and mortality. Computer Aided Diagnosis (CADx) systems can assist radiologists and care providers in reading and analyzing lung CT images to segment, classify, and keep track of nodules for signs of cancer. In this thesis, we propose a CADx system for this purpose. To predict lung nodule malignancy, we propose a new deep learning framework that combines Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to learn best in-plane and inter-slice visual features for diagnostic nodule classification. Since a nodule\u27s volumetric growth and shape variation over a period of time may reveal information regarding the malignancy of nodule, separately, a dictionary learning based approach is proposed to segment the nodule\u27s shape at two time points from two scans, one year apart. The output of a CNN classifier trained to learn visual appearance of malignant nodules is then combined with the derived measures of shape change and volumetric growth in assigning a probability of malignancy to the nodule. Due to the limited number of available CT scans of benign and malignant nodules in the image database from the National Lung Screening Trial (NLST), we chose to initially train a deep neural network on the larger LUNA16 Challenge database which was built for the purpose of eliminating false positives from detected nodules in thoracic CT scans. Discriminative features that were learned in this application were transferred to predict malignancy. The algorithm for segmenting nodule shapes in serial CT scans utilizes a sparse combination of training shapes (SCoTS). This algorithm captures a sparse representation of a shape in input data through a linear span of previously delineated shapes in a training repository. The model updates shape prior over level set iterations and captures variabilities in shapes by a sparse combination of the training data. The level set evolution is therefore driven by a data term as well as a term capturing valid prior shapes. During evolution, the shape prior influence is adjusted based on shape reconstruction, with the assigned weight determined from the degree of sparsity of the representation. The discriminative nature of sparse representation, affords us the opportunity to compare nodules\u27 variations in consecutive time points and to predict malignancy. Experimental validations of the proposed segmentation algorithm have been demonstrated on 542 3-D lung nodule data from the LIDC-IDRI database which includes radiologist delineated nodule boundaries. The effectiveness of the proposed deep learning and dictionary learning architectures for malignancy prediction have been demonstrated on CT data from 370 biopsied subjects collected from the NLST database. Each subject in this database had at least two serial CT scans at two separate time points one year apart. The proposed RNN CAD system achieved an ROC Area Under the Curve (AUC) of 0.87, when validated on CT data from nodules at second sequential time point and 0.83 based on dictionary learning method; however, when nodule shape change and appearance were combined, the classifier performance improved to AUC=0.89

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

Capsule Network-based Radiomics: From Diagnosis to Treatment

Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals have resulted in a surge of significant interest in ``radiomics". Radiomics is an emerging and relatively new research field, which refers to semi-quantitative and/or quantitative features extracted from medical images with the goal of developing predictive and/or prognostic models. Radiomics is expected to become a critical component for integration of image-derived information for personalized treatment in the near future. The conventional radiomics workflow is, typically, based on extracting pre-designed features (also referred to as hand-crafted or engineered features) from a segmented region of interest. Clinical application of hand-crafted radiomics is, however, limited by the fact that features are pre-defined and extracted without taking the desired outcome into account. The aforementioned drawback has motivated trends towards development of deep learning-based radiomics (also referred to as discovery radiomics). Discovery radiomics has the advantage of learning the desired features on its own in an end-to-end fashion. Discovery radiomics has several applications in disease prediction/ diagnosis. Through this Ph.D. thesis, we develop deep learning-based architectures to address the following critical challenges identified within the radiomics domain. First, we cover the tumor type classification problem, which is of high importance for treatment selection. We address this problem, by designing a Capsule network-based architecture that has several advantages over existing solutions such as eliminating the need for access to a huge amount of training data, and its capability to learn input transformations on its own. We apply different modifications to the Capsule network architecture to make it more suitable for radiomics. At one hand, we equip the proposed architecture with access to the tumor boundary box, and on the other hand, a multi-scale Capsule network architecture is designed. Furthermore, capitalizing on the advantages of ensemble learning paradigms, we design a boosting and also a mixture of experts capsule network. A Bayesian capsule network is also developed to capture the uncertainty of the tumor classification. Beside knowing the tumor type (through classification), predicting the patient's response to treatment plays an important role in treatment design. Predicting patient's response, including survival and tumor recurrence, is another goal of this thesis, which we address by designing a deep learning-based model that takes not only the medical images, but also different clinical factors (such as age and gender) as inputs. Finally, COVID-19 diagnosis, another challenging and crucial problem within the radiomics domain, is dealt with using both X-ray and Computed Tomography (CT) images (in particular low-dose ones), where two in-house datasets are collected for the latter and different capsule network-based models are developed for COVID-19 diagnosis

The impact of arterial input function determination variations on prostate dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic modeling: a multicenter data analysis challenge, part II

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study

Patient-Wise Versus Nodule-Wise Classification of Annotated Pulmonary Nodules using Pathologically Confirmed Cases

This paper presents a novel framework for combining well known shape, texture, size and resolution informatics descriptor of solitary pulmonary nodules (SPNs) detected using CT scan. The proposed methodology evaluates the performance of classifier in differentiating benign, malignant as well as metastasis SPNs with 246 chests CT scan of patients. Both patient-wise as well as nodule-wise available diagnostic report of 80 patients was used in differentiating the SPNs and the results were compared. For patient-wise data, generated a model with efficiency of 62.55% with labeled nodules and using semi-supervised approach, labels of rest of the unknown nodules were predicted and finally classification accuracy of 82.32% is achieved with all labeled nodules. For nodule-wise data, ground truth database of labeled nodules is expanded from a very small ground truth using content based image retrieval CBIR) method and achieved a precision of 98%. Proposed methodology not only avoids unnecessary biopsies but also efficiently label unknown nodules using pre-diagnosed cases which can certainly help the physicians in diagnosis