The Role of the Superior Order GLCM in the Characterization and Recognition of the Liver Tumors from Ultrasound Images

The hepatocellular carcinoma (HCC) is the most frequent malignant liver tumor. It often has a similar visual aspect with the cirrhotic parenchyma on which it evolves and with the benign liver tumors. The golden standard for HCC diagnosis is the needle biopsy, but this is an invasive, dangerous method. We aim to develop computerized,noninvasive techniques for the automatic diagnosis of HCC, based on information obtained from ultrasound images. The texture is an important property of the internal organs tissue, able to provide subtle information about the pathology. We previously defined the textural model of HCC, consisting in the exhaustive set of the relevant textural features, appropriate for HCC characterization and in the specific values of these features. In this work, we analyze the role that the superior order Grey Level Cooccurrence Matrices (GLCM) and the associated parameters have in the improvement of HCC characterization and automatic diagnosis. We also determine the best spatial relations between the pixels that lead to the highest performances, for the third, fifth and seventh order GLCM. The following classes will be considered: HCC, cirrhotic liver parenchyma on which it evolves and benign liver tumors

Abdominal Tumor Characterization and Recognition Using Superior-Order Cooccurrence Matrices, Based on Ultrasound Images

The noninvasive diagnosis of the malignant tumors is an important issue in research nowadays. Our purpose is to elaborate computerized, texture-based methods for performing computer-aided characterization and automatic diagnosis of these tumors, using only the information from ultrasound images. In this paper, we considered some of the most frequent abdominal malignant tumors: the hepatocellular carcinoma and the colonic tumors. We compared these structures with the benign tumors and with other visually similar diseases. Besides the textural features that proved in our previous research to be useful in the characterization and recognition of the malignant tumors, we improved our method by using the grey level cooccurrence matrix and the edge orientation cooccurrence matrix of superior order. As resulted from our experiments, the new textural features increased the malignant tumor classification performance, also revealing visual and physical properties of these structures that emphasized the complex, chaotic structure of the corresponding tissue

Deep Learning Techniques for Liver Tumor Recognition in Ultrasound Images

Cancer is one of the most severe diseases nowadays. Thus, tumor detection in a non-invasive and accurate manner is a challenging subject. Among these tumors, liver cancer is one of the most dangerous, being very common. Hepatocellular Carcinoma (HCC) is the most frequent malignant liver tumor. The golden standard for diagnosing HCC is mainly the biopsy, however invasive and risky, leading to infections, respectively to the spreading of the tumor through the body. We conceive computerized techniques for abdominal tumor recognition within medical images. Formerly, traditional, texture-based methods were involved for this purpose. Both classical texture analysis methods, as well as advanced, original texture analysis techniques, based on superior order statistics, were involved. The superior order Gray Level Cooccurrence Matrix (GLCM), as well as the Textural Microstructure Cooccurrence Matrices (TMCM) were employed and assessed. Recently, deep learning techniques based on Convolutional Neural Networks (CNN), their fusions with the conventional techniques, as well as their combinations among themselves, were assessed in the approached field. We present the most relevant aspects of this study in the current paper

Dynamic And Quantitative Radiomics Analysis In Interventional Radiology

Interventional Radiology (IR) is a subspecialty of radiology that performs invasive procedures driven by diagnostic imaging for predictive and therapeutic purpose. The development of artificial intelligence (AI) has revolutionized the industry of IR. Researchers have created sophisticated models backed by machine learning algorithms and optimization methodologies for image registration, cellular structure detection and computer-aided disease diagnosis and prognosis predictions. However, due to the incapacity of the human eye to detect tiny structural characteristics and inter-radiologist heterogeneity, conventional experience-based IR visual evaluations may have drawbacks. Radiomics, a technique that utilizes machine learning, offers a practical and quantifiable solution to this issue. This technology has been used to evaluate the heterogeneity of malignancies that are difficult to detect by the human eye by creating an automated pipeline for the extraction and analysis of high throughput computational imaging characteristics from radiological medical pictures. However, it is a demanding task to directly put radiomics into applications in IR because of the heterogeneity and complexity of medical imaging data. Furthermore, recent radiomics studies are based on static images, while many clinical applications (such as detecting the occurrence and development of tumors and assessing patient response to chemotherapy and immunotherapy) is a dynamic process. Merely incorporating static features cannot comprehensively reflect the metabolic characteristics and dynamic processes of tumors or soft tissues. To address these issues, we proposed a robust feature selection framework to manage the high-dimensional small-size data. Apart from that, we explore and propose a descriptor in the view of computer vision and physiology by integrating static radiomics features with time-varying information in tumor dynamics. The major contributions to this study include: Firstly, we construct a result-driven feature selection framework, which could efficiently reduce the dimension of the original feature set. The framework integrates different feature selection techniques to ensure the distinctiveness, uniqueness, and generalization ability of the output feature set. In the task of classification hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) in primary liver cancer, only three radiomics features (chosen from more than 1, 800 features of the proposed framework) can obtain an AUC of 0.83 in the independent dataset. Besides, we also analyze features’ pattern and contributions to the results, enhancing clinical interpretability of radiomics biomarkers. Secondly, we explore and build a pulmonary perfusion descriptor based on 18F-FDG whole-body dynamic PET images. Our major novelties include: 1) propose a physiology-and-computer-vision-interpretable descriptor construction framework by the decomposition of spatiotemporal information into three dimensions: shades of grey levels, textures, and dynamics. 2) The spatio-temporal comparison of pulmonary descriptor intra and inter patients is feasible, making it possible to be an auxiliary diagnostic tool in pulmonary function assessment. 3) Compared with traditional PET metabolic biomarker analysis, the proposed descriptor incorporates image’s temporal information, which enables a better understanding of the time-various mechanisms and detection of visual perfusion abnormalities among different patients. 4) The proposed descriptor eliminates the impact of vascular branching structure and gravity effect by utilizing time warping algorithms. Our experimental results showed that our proposed framework and descriptor are promising tools to medical imaging analysis

Development of an image guidance system for laparoscopic liver surgery and evaluation of optical and computer vision techniques for the assessment of liver tissue

Introduction: Liver resection is increasingly being carried out via the laparoscopic approach (keyhole surgery) because there is mounting evidence that it benefits patients by reducing pain and length of hospitalisation. There are however ongoing concerns about oncological radicality (i.e. ability to completely remove cancer) and an inability to control massive haemorrhage. These issues can partially be attributed to a loss of sensation such as depth perception, tactile feedback and a reduced field of view. Utilisation of optical imaging and computer vision may be able to compensate for some of the lost sensory input because these modalities can facilitate visualisation of liver tissue and structural anatomy. Their use in laparoscopy is attractive because it is easy to adapt or integrate with existing technology. The aim of this thesis is to explore to what extent this technology can aid in the detection of normal and abnormal liver tissue and structures. / Methods: The current state of the art for optical imaging and computer vision in laparoscopic liver surgery is assessed in a systematic review. Evaluation of confocal laser endomicroscopy is carried out on a murine and porcine model of liver disease. Multispectral near infrared imaging is evaluated on ex-vivo liver specimen. Video magnification is assessed on a mechanical flow phantom and a porcine model of liver disease. The latter model was also employed to develop a computer vision based image guidance system for laparoscopic liver surgery. This image guidance system is further evaluated in a clinical feasibility study. Where appropriate, experimental findings are substantiated with statistical analysis. / Results: Use of confocal laser endomicroscopy enabled discrimination between cancer and normal liver tissue with a sub-millimetre precision. This technology also made it possible to verify the adequacy of thermal liver ablation. Multispectral imaging, at specific wavelengths was shown to have the potential to highlight the presence of colorectal and hepatocellular cancer. An image reprocessing algorithm is proposed to simplify visual interpretation of the resulting images. It is shown that video magnification can determine the presence of pulsatile motion but that it cannot reliably determine the extent of motion. Development and performance metrics of an image guidance system for laparoscopic liver surgery are outlined. The system was found to improve intraoperative orientation more development work is however required to enable reliable prediction of oncological margins. / Discussion: The results in this thesis indicate that confocal laser endomicroscopy and image guidance systems have reached a development stage where their intraoperative use may benefit surgeons by visualising features of liver anatomy and tissue characteristics. Video magnification and multispectral imaging require more development and suggestions are made to direct this work. It is also highlighted that it is crucial to standardise assessment methods for these technologies which will allow a more direct comparison between the outcomes of different groups. Limited imaging depth is a major restriction of these technologies but this may be overcome by combining them with preoperatively obtained imaging data. Just like laparoscopy, optical imaging and computer vision use functions of light, a shared characteristic that makes their combined use complementary

Medical Image Analytics (Radiomics) with Machine/Deeping Learning for Outcome Modeling in Radiation Oncology

Image-based quantitative analysis (radiomics) has gained great attention recently. Radiomics possesses promising potentials to be applied in the clinical practice of radiotherapy and to provide personalized healthcare for cancer patients. However, there are several challenges along the way that this thesis will attempt to address. Specifically, this thesis focuses on the investigation of repeatability and reproducibility of radiomics features, the development of new machine/deep learning models, and combining these for robust outcomes modeling and their applications in radiotherapy. Radiomics features suffer from robustness issues when applied to outcome modeling problems, especially in head and neck computed tomography (CT) images. These images tend to contain streak artifacts due to patients’ dental implants. To investigate the influence of artifacts for radiomics modeling performance, we firstly developed an automatic artifact detection algorithm using gradient-based hand-crafted features. Then, comparing the radiomics models trained on ‘clean’ and ‘contaminated’ datasets. The second project focused on using hand-crafted radiomics features and conventional machine learning methods for the prediction of overall response and progression-free survival for Y90 treated liver cancer patients. By identifying robust features and embedding prior knowledge in the engineered radiomics features and using bootstrapped LASSO to select robust features, we trained imaging and dose based models for the desired clinical endpoints, highlighting the complementary nature of this information in Y90 outcomes prediction. Combining hand-crafted and machine learnt features can take advantage of both expert domain knowledge and advanced data-driven approaches (e.g., deep learning). Thus, we proposed a new variational autoencoder network framework that modeled radiomics features, clinical factors, and raw CT images for the prediction of intrahepatic recurrence-free and overall survival for hepatocellular carcinoma (HCC) patients in this third project. The proposed approach was compared with widely used Cox proportional hazard model for survival analysis. Our proposed methods achieved significant improvement in terms of the prediction using the c-index metric highlighting the value of advanced modeling techniques in learning from limited and heterogeneous information in actuarial prediction of outcomes. Advances in stereotactic radiation therapy (SBRT) has led to excellent local tumor control with limited toxicities for HCC patients, but intrahepatic recurrence still remains prevalent. As an extension of the third project, we not only hope to predict the time to intrahepatic recurrence, but also the location where the tumor might recur. This will be clinically beneficial for better intervention and optimizing decision making during the process of radiotherapy treatment planning. To address this challenging task, firstly, we proposed an unsupervised registration neural network to register atlas CT to patient simulation CT and obtain the liver’s Couinaud segments for the entire patient cohort. Secondly, a new attention convolutional neural network has been applied to utilize multimodality images (CT, MR and 3D dose distribution) for the prediction of high-risk segments. The results showed much improved efficiency for obtaining segments compared with conventional registration methods and the prediction performance showed promising accuracy for anticipating the recurrence location as well. Overall, this thesis contributed new methods and techniques to improve the utilization of radiomics for personalized radiotherapy. These contributions included new algorithm for detecting artifacts, a joint model of dose with image heterogeneity, combining hand-crafted features with machine learnt features for actuarial radiomics modeling, and a novel approach for predicting location of treatment failure.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163092/1/liswei_1.pd

Prediction of Chemotherapy Response of Liver Metastases from Baseline CT-Images Using Deep Neural Networks

Dans les pays développés, le cancer colorectal est reconnu comme étant la deuxième cause la plus importante de mortalité liée au cancer. La chimiothérapie est considérée comme un traitement standard pour les métastases colorectales du foie (MCF). Parmi les patients qui développent des MCF, l’évaluation de la réponse du patient au traitement de chimiothérapie est souvent requise pour déterminer le besoin d’une chimiothérapie de seconde ligne, ainsi qu’une éligibilité à la chirurgie. Toutefois, tandis que les régimes basés sur un régime dénommé FOLFOX sont typiquement utilisés pour le traitement de la MCF, l’identification de la sensibilité du patient reste difficile. Les systèmes de diagnostic assistés par ordinateur peuvent fournir de l’information supplémentaire sur la classification des métastases du foie identifiées au niveau des images de diagnostic. Du aux quelques difficultés que rencontrent les radiologues pour distinguer, à l’oeil nu, les lésions traitées des lésions non-traitées, nous proposons dans cette étude un système automatisé basé sur les réseaux profonds convolutifs (RPC). Dans un premier lieu, ces réseaux profonds différencient les lésions traitées des lésions non-traitées, pour ensuite identifier les nouvelles lésions apparaissant sur les tomodensitométries. Ensuite, un réseau de neurones dense émet une prédiction, à partir des lésions non traitées visibles sur les tomodensitométries de prétraitement pour les patients à MCF sous chimiothérapie, sur leur réponse au régime spécifique de chimiothérapie. Dans ce contexte, la référence pour l’évaluation de la réponse au traitement pour le régime approprié de chimiothérapie était le degré de régression de la tumeur en histopathologie. La méthode adoptée dans cette étude nous a aidé à adresser les trois grands objectifs de ce projet de recherche. La première étape était de développer un système automatique de classification des tumeurs traitées et non-traitées à partir des tomodensitométries de patients. La deuxième étape a été de concevoir une nouvelle approche pour prédire la réponse au traitement FOLFOX qui utilise le médicament Bevacizumab en tant que traitement de première ligne. La troisième étape était de prédire le pourcentage de changement volumétrique de la tumeur suivant deux moments temporels consécutifs. Les algorithmes d’intelligence artificielle (IA), et les approches d’apprentissage profond en particulier, ont montré des progrès prometteurs en vision par ordinateur ainsi qu’en traitement d’images. Il existe de nombreuses applications en analyse d’images médicales qui utilisent des réseaux profonds convolutifs pour propulser ces progrès en avant le plus rapidement possible. En pratique, les radiologues et lesmédecins évaluent les images médicales visuellement pour le diagnostic, la détection, la récurrence, le suivi et la caractérisation des maladies. Les méthodes d’apprentissage profond sont souvent supérieures pour la reconnaissance automatique de structures complexes à partir d’images, et quantifier l’évaluation de propriétés et caractéristiques radiographiques.----------ABSTRACT: In developed countries, colorectal cancer is known as the second cause of cancer-related mortality. Chemotherapy is considered a standard treatment for colorectal liver metastases (CLM). Among patients who develop CLM, the assessment of patient response to chemotherapy is often required to determine the need for second-line chemotherapy, and eligibility for surgery. However, a drug regimen known as FOLFOX are typically used for CLM treatment, the identification of responsive patients remains elusive. Computer-aided diagnosis systems may provide insight into the classification of liver metastases identified on diagnostic images. Due to some difficulties for radiologists to distinguish between treated and untreated lesions from the naked eyes, in this study, we propose a fully automated framework based on deep convolutional neural networks (DCNN) which first differentiates treated and untreated lesions to identify new lesions appearing on CT scans, followed by a fully connected neural network to predict from untreated lesions in pre-treatment computed tomography (CT) for patients with CLMundergoing chemotherapy, their response to the specific chemotherapy regimen. In this respect, the ground truth for assessment of treatment response for proper chemotherapy regimen was histopathology to determine the tumor regression grade (TRG). The adopted method in this study helped us to address the three main research objectives. The first step is to develop an automated framework for the classification of treated and untreated tumors based on the patient’s CT images. The second step is to design a new approach for the prediction of response to FOLFOX regimens with Bevacizumab agents as the first-line of treatment. The third step is to predict the percentage of the tumor volume change following two consecutive exams. Artificial intelligence (AI) algorithms, particularly deep learning approach, have shown very astonishing progress in computer vision and image processing tasks. There are several applications in the medical image analysis area which use DCNN to propel these methods forward as quickly as possible. In practice, radiologists and physicians attempt to assess visually medical images for diagnosis, detection, recurrence, monitoring, and characterization of diseases. Deep learning methods surpass at the recognition of complicated patterns from imaging data automatically and quantify the assessment of radiographic features and characteristics

Deep learning-based diagnostic system for malignant liver detection

Cancer is the second most common cause of death of human beings, whereas liver cancer is the fifth most common cause of mortality. The prevention of deadly diseases in living beings requires timely, independent, accurate, and robust detection of ailment by a computer-aided diagnostic (CAD) system. Executing such intelligent CAD requires some preliminary steps, including preprocessing, attribute analysis, and identification. In recent studies, conventional techniques have been used to develop computer-aided diagnosis algorithms. However, such traditional methods could immensely affect the structural properties of processed images with inconsistent performance due to variable shape and size of region-of-interest. Moreover, the unavailability of sufficient datasets makes the performance of the proposed methods doubtful for commercial use. To address these limitations, I propose novel methodologies in this dissertation. First, I modified a generative adversarial network to perform deblurring and contrast adjustment on computed tomography (CT) scans. Second, I designed a deep neural network with a novel loss function for fully automatic precise segmentation of liver and lesions from CT scans. Third, I developed a multi-modal deep neural network to integrate pathological data with imaging data to perform computer-aided diagnosis for malignant liver detection. The dissertation starts with background information that discusses the proposed study objectives and the workflow. Afterward, Chapter 2 reviews a general schematic for developing a computer-aided algorithm, including image acquisition techniques, preprocessing steps, feature extraction approaches, and machine learning-based prediction methods. The first study proposed in Chapter 3 discusses blurred images and their possible effects on classification. A novel multi-scale GAN network with residual image learning is proposed to deblur images. The second method in Chapter 4 addresses the issue of low-contrast CT scan images. A multi-level GAN is utilized to enhance images with well-contrast regions. Thus, the enhanced images improve the cancer diagnosis performance. Chapter 5 proposes a deep neural network for the segmentation of liver and lesions from abdominal CT scan images. A modified Unet with a novel loss function can precisely segment minute lesions. Similarly, Chapter 6 introduces a multi-modal approach for liver cancer variants diagnosis. The pathological data are integrated with CT scan images to diagnose liver cancer variants. In summary, this dissertation presents novel algorithms for preprocessing and disease detection. Furthermore, the comparative analysis validates the effectiveness of proposed methods in computer-aided diagnosis

Multimodality and multi-parametric imaging in abdominal oncology:current and future strategies to harnessing the complementary value of PET/CT and MRI

Medical imaging is essential for the diagnosis, treatment and follow-up of patients with cancer. Combinations of different, complementary imaging modalities are increasingly being used: multimodal imaging. This thesis describes recent developments and expected innovations in research (and application of) combined PET/CT and MRI in patients with abdominal cancer. To this end, the effect of integrated assessment of PET/CT and MRI scans was investigated. This resulted in a different result in 1 in 9 patients, as well as a positive effect on the confidence in the results. As a next step, the value of quantitative parameters from PET/CT and MRI was assessed, to predict the treatment outcome of patients with cancer of the rectum, uterine cervix or anus. This value appears to be limited, but the findings from conventional, visual image assessment, does contribute to the prediction