Transfer learning for multicenter classification of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a lung disease which can be quantified using chest computed tomography (CT) scans. Recent studies have shown that COPD can be automatically diagnosed using weakly supervised learning of intensity and texture distributions. However, up till now such classifiers have only been evaluated on scans from a single domain, and it is unclear whether they would generalize across domains, such as different scanners or scanning protocols. To address this problem, we investigate classification of COPD in a multi-center dataset with a total of 803 scans from three different centers, four different scanners, with heterogenous subject distributions. Our method is based on Gaussian texture features, and a weighted logistic classifier, which increases the weights of samples similar to the test data. We show that Gaussian texture features outperform intensity features previously used in multi-center classification tasks. We also show that a weighting strategy based on a classifier that is trained to discriminate between scans from different domains, can further improve the results. To encourage further research into transfer learning methods for classification of COPD, upon acceptance of the paper we will release two feature datasets used in this study on http://bigr.nl/research/projects/copdComment: Accepted at Journal of Biomedical and Health Informatic

Integrative transcriptomics in smoking related lung diseases

Chronic lung diseases including Chronic Obstructive Pulmonary Disease (COPD), Idiopathic Pulmonary Fibrosis (IPF) and lung cancer are major causes of morbidity and mortality in the United States due to high incidence and limited therapeutic options. In order to address this critical issue, I have leveraged RNA sequencing and integrative genomics to define disease-associated transcriptomic changes which could be potentially targeted to lead to new therapeutics. We sequenced the lung transcriptome of subjects with IPF (n=19), emphysema (n=19, a subtype of COPD), or neither (n=20). The expression levels of 1770 genes differed between IPF and control lung, and 220 genes differed between emphysema and control lung (p<0.001). Upregulated genes in both emphysema and IPF were enriched for the p53/hypoxia pathway. These results were validated by immunohistochemistry of select p53/hypoxia proteins and by GSEA analysis of independent expression microarray experiments. To identify regulatory events, I constructed an integrative miRNA target prediction and anticorrelation miRNA-mRNA network, which highlighted several miRNA whose expression levels were the opposite of genes differentially expressed in both IPF and emphysema. MiR-96 was a highly connected hub in this network and was subsequently overexpressed in cell lines to validate several potential regulatory connections. Building upon these successful experiments, I next sought to define gene expression changes and the miRNA-mRNA regulatory network in never smoker lung cancer. Large and small RNA was sequenced from matched lung adenocarcinoma tumor and adjacent normal lung tissue obtained from 22 subjects (8 never, 14 current and former smokers). I identified 120 genes whose expression was modified uniquely in never smoker lung tumors. Using a repository of gene-expression profiles associated with small bioactive molecules, several compounds which counter the never smoker tumor signature were identified in silico. Leveraging differential expression information, I again constructed an mRNA-miRNA regulatory network, and subsequently identified a potential never smoker oncomir has-mir-424 and its transcription factor target FOXP2. In this thesis, I have identified genes, pathways and the miRNA-mRNA regulatory network that is altered in COPD, IPF, and lung adenocarcinoma among never smokers. My findings may ultimately lead to improved treatment options by identifying targetable pathways, regulators, and therapeutic drug candidates.2017-02-01T00:00:00

Artificial Intelligence and Chest Computational Tomography to predict prognosis in Pulmonary Hypertension and lung disease.

Pulmonary hypertension (PH) is an incurable severe condition with poor survival and multiple clinically distinct sub-groups and phenotypes. Accurate diagnosis and identification of the underlying phenotype is an integral step in patient management as it informs treatment choice. Outcomes vary significantly between phenotypes. Patients presenting with signs of both PH and lung disease pose a clinical dilemma between two phenotypes - idiopathic pulmonary arterial hypertension (IPAH) and pulmonary hypertension secondary to lung disease (PH-CLD) as they can present with overlapping features. The impact of lung disease on outcomes is not well understood and this is a challenging area in the literature with limited progress. All patients suspected with PH undergo routine chest Computed Tomography Pulmonary Angiography (CTPA) imaging. Despite this, the prognostic significance of commonly visualised lung parenchymal patterns is currently unknown. Current radiological assessment is also limited by its visual and subjective nature. Recent breakthroughs in deep-learning Artificial Intelligence (AI) approaches have enabled automated quantitative analysis of medical imaging features. This thesis demonstrates the prognostic impact of common lung parenchymal patterns on CT in IPAH and PH-CLD. It describes how this data could aid in phenotyping, and in identification of new sub-groups of patients with distinct clinical characteristics, imaging features and prognostic profiles. It further develops and clinically evaluates an automated CT AI model which quantifies the percentage of lung involvement of prognostic lung parenchymal patterns. Combining this AI model with radiological assessment improves the prognostic predictive strength of lung disease severity in these patients

CELLULAR AND MOLECULAR MECHANISMS OF EMPHYSEMA PATHOGENESIS

Emphysema is a progressive disease characterized by alveolar degradation, irreversible airway enlargement, and impaired gas exchange over time. We and others have theorized that innate immune cells, such as macrophages, become dysregulated in the emphysematous lung and ultimately cause long-term damage to tissue and airways. However, this claim is supported largely by circumstantial evidence, despite years of studying COPD pathogenesis in humans or mice. Herein, we applied several next- generation techniques for studying lung cell phenotypes and molecular mechanisms of disease pathogenesis in mice with elastase-induced emphysema. By the end of this Thesis, we suggest that severe damage post-elastase is likely orchestrated by a network of dysregulated immune cells; but CD63+ alveolar macrophages, specifically, were identified as a candidate cell type with the functional capacity to damage lung tissue over time. Future consideration and characterization of CD63+ alveolar macrophages may shed light on how dysregulated immune cells drive emphysema pathogenesis or the progression of related, degenerative diseases

Image processing in medicine advances for phenotype characterization, computer-assisted diagnosis and surgical planning

En esta Tesis presentamos nuestras contribuciones al estado del arte en procesamiento digital de imágenes médicas, articulando nuestra exposición en torno a los tres principales objetivos de la adquisición de imágenes en medicina: la prevención, el diagnóstico y el tratamiento de las enfermedades. La prevención de la enfermedad se puede conseguir a veces mediante una caracterización cuidadosa de los fenotipos propios de la misma. Tal caracterización a menudo se alcanza a partir de imágenes. Presentamos nuestro trabajo en caracterización del enfisema pulmonar a partir de imágenes TAC (Tomografía Axial Computerizada) de tórax en alta resolución, a través del análisis de las texturas locales de la imagen. Nos proponemos llenar el vacío existente entre la práctica clínica actual, y las sofisticadas pero costosas técnicas de caracterización de regiones texturadas, disponibles en la literatura. Lo hacemos utilizando la distribución local de intensidades como un descriptor adecuado para determinar el grado de destrucción de tejido en pulmones enfisematosos. Se presentan interesantes resultados derivados del análisis de varios cientos de imágenes para niveles variables de severidad de la enfermedad, sugiriendo tanto la validez de nuestras hipótesis, como la pertinencia de este tipo de análisis para la comprensión de la enfermedad pulmonar obstructiva crónica. El procesado de imágenes médicas también puede asistir en el diagnóstico y detección de enfermedades. Presentamos nuestras contribuciones a este campo, que consisten en técnicas de segmentación y cuantificación de imágenes dermatoscópicas de lesiones de la piel. La segmentación se obtiene mediante un novedoso algoritmo basado en contornos activos que explota al máximo el contenido cromático de las imágenes, gracias a la maximización de la discrepancia mediante comparaciones cross-bin. La cuantificación de texturas en lesiones melanocíticas se lleva a cabo utilizando un modelado de los patrones de pigmentación basado en campos aleatorios de Markov, en un esfuerzo por adoptar la tendencia emergente en dermatología: la detección de la malignidad mediante el análisis de la irregularidad de la textura. Los resultados para ambas técnicas son validados con un conjunto significativo de imágenes dermatológicas, sugiriendo líneas interesantes para la detección automática del melanoma maligno. Cuando la enfermedad ya está presente, el tratamiento digital de imágenes puede asistir en la planificación quirúrgica y la intervención guiada por imagen. La planificación terapeútica, ejemplicada por la planificación de cirugía plástica usando realidad virtual, se aborda en nuestro trabajo en segmentación de hueso/grasa/músculo en imágenes TAC. Usando un abordaje interactivo e incremental, nuestro sistema permite obtener segmentaciones precisas a partir de unos cuantos clics de ratón para una gran variedad de condiciones de adquisición y frente a anatomícas anormales. Presentamos nuestra metodología, y nuestra validación experimental profusa basada tanto en segmentaciones manuales como en valoraciones subjetivas de los usuarios, e indicamos referencias al lector que detallan los beneficios obtenidos con el uso de la plataforma de planifificación que utiliza nuestro algoritmo. Como conclusión presentamos una disertación final sobre la importancia de nuestros resultados y las líneas probables de trabajo futuro hacía el objetivo último de mejorar el cuidado de la salud mediante técnicas de tratamiento digital de imágenes médicas.In this Thesis we present our contributions to the state-of-the-art in medical image processing, articulating our exposition around the three main roles of medical imaging: disease prevention, diagnosis and treatment. Disease prevention can sometimes be achieved by proper characterization of disease phenotypes. Such characterization is often attained from the standpoint of imaging. We present our work in characterization of emphysema from highresolution computed-tomography images via quanti_cation of local texture. We propose to _ll the gap between current clinical practice and sophisticated texture approaches by the use of local intensity distributions as an adequate descriptor for the degree of tissue destruction in the emphysematous lung. Interesting results are presented from the analysis of several hundred datasets of lung CT for varying disease severity, suggesting both the correctness of our hypotheses and the pertinence of _ne emphysema quanti_cation for understanding of chronic obstructive pulmonary disease. Medical image processing can also assist in the diagnosis and detection of disease. We introduce our contributions to this_eld, consisting of segmentation and quanti_cation techniques in application to dermatoscopy images of skin lesions. Segmentation is achieved via a novel active contour algorithm that fully exploits the color content of the images, via cross-bin histogram dissimilarity maximization. Texture quanti_cation in the context of melanocytic lesions is performed using modelization of the pigmentation patterns via Markov random elds, in an e_ort to embrace the emerging trend in dermatology: malignancy assessment based on texture irregularity analysis. Experimental results for both, the segmentation and quanti_cation proposed techniques, will be validated on a signi_cant set of dermatoscopy images, suggesting interesting pathways towards automatic detection and diagnosis of malignant melanoma. Once disease has occurred, image processing can assist in therapeutical planning and image-guided intervention. Therapeutical planning, exempli_ed by virtual reality surgical planning, is tackled by our work in segmentation of bone/fat/muscle in CT images for plastic surgery planning. Using an interactive, incremental approach, our system is able to provide accurate segmentations based on a couple of mouse-clicks for a wide variety of imaging conditions and abnormal anatomies. We present our methodology, and provide profuse experimental validation based on manual segmentations and subjective assessment, and refer the reader to related work reporting on the clinical bene_ts obtained using the virtual reality platform hosting our algorithm. As a conclusion we present a _nal dissertation on the signi_cance of our results and the probable lines of future work towards fully bene_tting healthcare using medical image processing

Computer-Aided Assessment of Tuberculosis with Radiological Imaging: From rule-based methods to Deep Learning

Mención Internacional en el título de doctorTuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb.) that produces pulmonary damage due to its airborne nature. This fact facilitates the disease fast-spreading, which, according to the World Health Organization (WHO), in 2021 caused 1.2 million deaths and 9.9 million new cases. Traditionally, TB has been considered a binary disease (latent/active) due to the limited specificity of the traditional diagnostic tests. Such a simple model causes difficulties in the longitudinal assessment of pulmonary affectation needed for the development of novel drugs and to control the spread of the disease. Fortunately, X-Ray Computed Tomography (CT) images enable capturing specific manifestations of TB that are undetectable using regular diagnostic tests, which suffer from limited specificity. In conventional workflows, expert radiologists inspect the CT images. However, this procedure is unfeasible to process the thousands of volume images belonging to the different TB animal models and humans required for a suitable (pre-)clinical trial. To achieve suitable results, automatization of different image analysis processes is a must to quantify TB. It is also advisable to measure the uncertainty associated with this process and model causal relationships between the specific mechanisms that characterize each animal model and its level of damage. Thus, in this thesis, we introduce a set of novel methods based on the state of the art Artificial Intelligence (AI) and Computer Vision (CV). Initially, we present an algorithm to assess Pathological Lung Segmentation (PLS) employing an unsupervised rule-based model which was traditionally considered a needed step before biomarker extraction. This procedure allows robust segmentation in a Mtb. infection model (Dice Similarity Coefficient, DSC, 94%±4%, Hausdorff Distance, HD, 8.64mm±7.36mm) of damaged lungs with lesions attached to the parenchyma and affected by respiratory movement artefacts. Next, a Gaussian Mixture Model ruled by an Expectation-Maximization (EM) algorithm is employed to automatically quantify the burden of Mtb.using biomarkers extracted from the segmented CT images. This approach achieves a strong correlation (R2 ≈ 0.8) between our automatic method and manual extraction. Consequently, Chapter 3 introduces a model to automate the identification of TB lesions and the characterization of disease progression. To this aim, the method employs the Statistical Region Merging algorithm to detect lesions subsequently characterized by texture features that feed a Random Forest (RF) estimator. The proposed procedure enables a selection of a simple but powerful model able to classify abnormal tissue. The latest works base their methodology on Deep Learning (DL). Chapter 4 extends the classification of TB lesions. Namely, we introduce a computational model to infer TB manifestations present in each lung lobe of CT scans by employing the associated radiologist reports as ground truth. We do so instead of using the classical manually delimited segmentation masks. The model adjusts the three-dimensional architecture, V-Net, to a multitask classification context in which loss function is weighted by homoscedastic uncertainty. Besides, the method employs Self-Normalizing Neural Networks (SNNs) for regularization. Our results are promising with a Root Mean Square Error of 1.14 in the number of nodules and F1-scores above 0.85 for the most prevalent TB lesions (i.e., conglomerations, cavitations, consolidations, trees in bud) when considering the whole lung. In Chapter 5, we present a DL model capable of extracting disentangled information from images of different animal models, as well as information of the mechanisms that generate the CT volumes. The method provides the segmentation mask of axial slices from three animal models of different species employing a single trained architecture. It also infers the level of TB damage and generates counterfactual images. So, with this methodology, we offer an alternative to promote generalization and explainable AI models. To sum up, the thesis presents a collection of valuable tools to automate the quantification of pathological lungs and moreover extend the methodology to provide more explainable results which are vital for drug development purposes. Chapter 6 elaborates on these conclusions.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidenta: María Jesús Ledesma Carbayo.- Secretario: David Expósito Singh.- Vocal: Clarisa Sánchez Gutiérre