The purpose of this Ph.D. thesis is to illustrate the research works carried out during the conceptualization, design, implementation, and evaluation of novel Clinical Decision Support Systems (CDSSs) based on Radiomics, Pathomics and Deep Learning (DL) techniques. CDSSs can be effective systems for implementing Precision Medicine into clinical practice since they permit the objective and repeatable evaluation of patients. Precision Medicine can enable the improvement of the healthcare system by employing a personal healthcare process for the health status of an individual patient, which evolves in a unique way. The methodologies concerning CDSSs were developed with different underlying goals: improvement of the clinical results, availability and usability of the method, and feasibility of the integration into the routine clinical practice. The applications considered span from Radiology to Digital Pathology. Tasks under consideration in Medical Imaging applications, from a computer vision perspective, concerned object detection, instance segmentation, semantic segmentation, color normalization, and characterization and classification of regions of interest. Data under consideration were either provided by local hospitals or obtained from public repositories. Validation of the developed systems has been done in accordance with the physicians. Moreover, the explainability of the realized systems has been investigated, by analyzing features' structure or by means of perceptive saliency maps. In the aforementioned scenario, the main purpose of this thesis is to develop new systems based on Deep Learning, Radiomics and Pathomics for the processing and analysis of medical images. Computational Imaging is a promising methodology to incorporate in the framework of Precision Medicine. Indeed, it creates the possibility to characterize the lesions in large datasets of images belonging to Radiology and Digital Pathology domains in an effective way, offering a personalized evaluation of the patient. Merits and shortcomings regarding DL in the field of Medical Imaging have been investigated for applications in Radiology and Digital Pathology. Technical contributions include devising novel algorithms, improving existing workflows, and assembling complex CDSSs by combining in an original and effective way different techniques proposed in the literature. In the Radiology domain, the following tasks have been tackled for what concerns applications related to Image-guided Surgery (IGS): liver segmentation, including also the classification into anatomical segments; vertebrae segmentation and identification; prostate segmentation and registration for image fusion. Radiomics has been exploited for characterizing lung lesions in COVID-19 patients, in order to discover a prognostic signature for those with a higher risk of developing pulmonary thromboembolism. With regard to Digital Pathology, applications included colorectal cancer (CRC) tissue classification; hematoxylin and eosin (H&E) stain color normalization; nuclei segmentation and detection; glomeruli lesions classifications according to Oxford score for IgA nephropathy patients. These automatic pipelines for histological data analysis can enable Pathomics, allowing the objective quantification and evaluation of tissue patterns. The developed solutions in all these scenarios were put in comparison with state-of-the-art approaches proposed in the literature, and were validated with physicians when possible. In many cases, data have also been collected from local institutions. This thesis work is organized into five chapters. Chapter 1 introduces the objective and the technical contribution of the thesis. Chapter 2 describes the state-of-the-art in all the considered clinical scenarios, with a particular focus on Radiology and Digital Pathology, encompassing emerging trends such as Radiomics and Pathomics. Chapter 3 describes the contributions proposed in the Radiology field. In particular, IGS applications concern liver segmentation and classification into segments, vertebrae segmentation and identification, and prostate segmentation and registration. Also, a Radiomics-based analysis of lung lesions of patients diagnosed with COVID-19 is presented. Chapter 4 presents the contributions proposed in the field of Digital Pathology, concerning tissue segmentation, normalization and classification, and detection of objects of interest, such as nuclei of cells. Lastly, final remarks and considerations for future works are drawn in Chapter 5.

Computational imaging for precision medicine: the emergence of radiomics, pathomics and deep learning

Altini, Nicola
2022-01-01

Abstract

The purpose of this Ph.D. thesis is to illustrate the research works carried out during the conceptualization, design, implementation, and evaluation of novel Clinical Decision Support Systems (CDSSs) based on Radiomics, Pathomics and Deep Learning (DL) techniques. CDSSs can be effective systems for implementing Precision Medicine into clinical practice since they permit the objective and repeatable evaluation of patients. Precision Medicine can enable the improvement of the healthcare system by employing a personal healthcare process for the health status of an individual patient, which evolves in a unique way. The methodologies concerning CDSSs were developed with different underlying goals: improvement of the clinical results, availability and usability of the method, and feasibility of the integration into the routine clinical practice. The applications considered span from Radiology to Digital Pathology. Tasks under consideration in Medical Imaging applications, from a computer vision perspective, concerned object detection, instance segmentation, semantic segmentation, color normalization, and characterization and classification of regions of interest. Data under consideration were either provided by local hospitals or obtained from public repositories. Validation of the developed systems has been done in accordance with the physicians. Moreover, the explainability of the realized systems has been investigated, by analyzing features' structure or by means of perceptive saliency maps. In the aforementioned scenario, the main purpose of this thesis is to develop new systems based on Deep Learning, Radiomics and Pathomics for the processing and analysis of medical images. Computational Imaging is a promising methodology to incorporate in the framework of Precision Medicine. Indeed, it creates the possibility to characterize the lesions in large datasets of images belonging to Radiology and Digital Pathology domains in an effective way, offering a personalized evaluation of the patient. Merits and shortcomings regarding DL in the field of Medical Imaging have been investigated for applications in Radiology and Digital Pathology. Technical contributions include devising novel algorithms, improving existing workflows, and assembling complex CDSSs by combining in an original and effective way different techniques proposed in the literature. In the Radiology domain, the following tasks have been tackled for what concerns applications related to Image-guided Surgery (IGS): liver segmentation, including also the classification into anatomical segments; vertebrae segmentation and identification; prostate segmentation and registration for image fusion. Radiomics has been exploited for characterizing lung lesions in COVID-19 patients, in order to discover a prognostic signature for those with a higher risk of developing pulmonary thromboembolism. With regard to Digital Pathology, applications included colorectal cancer (CRC) tissue classification; hematoxylin and eosin (H&E) stain color normalization; nuclei segmentation and detection; glomeruli lesions classifications according to Oxford score for IgA nephropathy patients. These automatic pipelines for histological data analysis can enable Pathomics, allowing the objective quantification and evaluation of tissue patterns. The developed solutions in all these scenarios were put in comparison with state-of-the-art approaches proposed in the literature, and were validated with physicians when possible. In many cases, data have also been collected from local institutions. This thesis work is organized into five chapters. Chapter 1 introduces the objective and the technical contribution of the thesis. Chapter 2 describes the state-of-the-art in all the considered clinical scenarios, with a particular focus on Radiology and Digital Pathology, encompassing emerging trends such as Radiomics and Pathomics. Chapter 3 describes the contributions proposed in the Radiology field. In particular, IGS applications concern liver segmentation and classification into segments, vertebrae segmentation and identification, and prostate segmentation and registration. Also, a Radiomics-based analysis of lung lesions of patients diagnosed with COVID-19 is presented. Chapter 4 presents the contributions proposed in the field of Digital Pathology, concerning tissue segmentation, normalization and classification, and detection of objects of interest, such as nuclei of cells. Lastly, final remarks and considerations for future works are drawn in Chapter 5.
Deep Learning; radiomics; pathomics; clinical decision support, medical image analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/245880
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