Computer-aided classification of liver lesions from CT images based on multiple ROI

This manuscript introduces an automated Computer-Aided Classification (CAD) system to classify liver lesion into Benign or Malignant. The system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features from Multiple ROI, which is the novelty. Finally, classifying liver lesions into benign and malignant. The proposed system divides a segmented lesion into three areas, i.e. inside, outside and border areas. This is because the inside lesion, boundary, and surrounding lesion area contribute different information about the lesion. The features are extracted from the three areas and used to build a new feature vector to feed a classifier. The novelty lies in using the features from the multiple ROIs, and particularly surrounding area (outside), because the Malignant lesion affects the surrounding area differently compared to, the Benign lesion. Utilising the features from inside, border, and outside lesion area supports in better differentiation between benign and malignant lesion. The experimental results showed an enhancement in the classification accuracy (using multiple ROI technique) compared to the accuracy using a single ROI

Entwicklung und Anwendung der in vivo abdominellen Magnetresonanzelastographie

Magnetic Resonance Elastography (MRE) is a well-established non-invasive imaging technique used to quantify the mechanical properties of tissues in vivo for the diagnosis of liver fibrosis. However, MRE is limited by its spatial resolution, sensitivity to motion artifacts, and insensitivity to metabolic function. Therefore, three studies of abdominal MRE were conducted to improve the quality of mechanical maps for characterizing liver tumors, to correct for motion artifacts induced by breathing, and to implement MRE on a PET/MRI scanner to correlate mechanical liver properties with metabolic functions in small animals through technical improvements in image acquisition and post-processing. High-resolution stiffness (shear wave speed in m/s), wave penetration (penetration rate in m/s), and fluidity (phase of the complex shear modulus in rad) maps were generated using multifrequency MRE, novel actuators, and tomoelastography post-processing. The first study characterized the stiffness and fluidity of a total of 141 liver tumors in 70 patients. The second study analyzed the motion of abdominal organs and its effect on their stiffness using different acquisition paradigms and image registration in 12 subjects. The third study examined the relationship of liver stiffness and wave penetration to central metabolic liver functions in 19 rabbits. Malignant liver tumors were distinguished from the surrounding liver (stiffness area under the curve [AUC]: 0.88 and fluidity AUC: 0.95) and benign tumors (stiffness AUC: 0.85 and fluidity AUC: 0.86) due to their increased stiffness and fluidity. In the second study, no significant differences in stiffness were observed despite significant differences in examination time, organ motion, and image quality with different image acquisition paradigms. Motion correction by image registration increased image sharpness, so that no significant difference was measurable between MRE in free breathing and breath-hold. Healthy rabbit livers showed heterogeneous liver stiffness, such that division into low and high stiffness (>1.6 m/s) groups resulted in significant differences in central metabolic functions. Stiffness and fluidity measured by multifrequency MRE hold promise as quantitative biomarkers for the diagnosis of malignant liver tumors. Abdominal MRE with free breathing, followed by image registration, is recommended as the best balance between fast examination time and good image quality. Additionally, the applicability of abdominal MRE in small animals in a clinical MRI was demonstrated, and correlations between mechanical liver properties and metabolic functions were found. This study demonstrates improvements in the quality of maps of biophysical parameters for both clinical and preclinical studies, making an important contribution to the clinical translation of multifrequency MRE as a non-invasive imaging modality for abdominal organs and pathologies.Die Magnetresonanzelastographie (MRE) ist eine nichtinvasive Bildgebungsmethode zur Quantifizierung mechanischer Gewebeeigenschaften in vivo bei der Diagnose von Leberfibrose. Limitationen bestehen aufgrund örtlicher Bildauflösung, Bewegungsempfindlichkeit und Insensitivität zu metabolischen Funktionen. Aufgrund technischer Verbesserung in der Bildaufnahme und der Bildauswertung wurde daher anhand von drei Studien zur abdominellen MRE die Bildqualität mechanischer Karten zur Charakterisierung von Lebertumoren verbessert, atmungsinduzierte Organbewegungen korrigiert und die MRE an klinischen PET/MRT implementiert, um an Kleintieren die mechanischen Lebereigenschaften mit metabolischen Funktionen zu korrelieren. Mittels multifrequenter MRE, neuartiger Aktoren und tomoelastographischer Auswertung wurden hochaufgelöste Karten der Steifigkeit (Scherwellengeschwindigkeit in m/s), Wellenpenetration (Wellenpenetrationsrate in m/s) und Fluidität (Phase des komplexen Schermoduls in rad) generiert. Die erste Studie charakterisierte die Steifigkeit und Fluidität von insgesamt 141 Lebertumoren an 70 Patienten. Eine zweite Studie analysierte die Bewegung und den Einfluss auf die Steifigkeit abdomineller Organe mittels unterschiedlicher Aufnahmeparadigmen und Bildregistrierung in 12 Probanden. In einer dritten Studie wurde der Zusammenhang von Lebersteifigkeit und Wellenpenetration zu zentralen metabolischen Leberfunktionen an 19 Kaninchen untersucht. Maligne Lebertumoren können durch erhöhte Steifigkeit und Fluidität (Steifigkeit AUC: 0.88 und Fluidität AUC: 0.95) gut von gutartigen Tumoren (Steifigkeit AUC: 0.85 und Fluidität AUC: 0.86) unterschieden werden. In der zweiten Studie wurden trotz verschiedener Aufnahmeparadigmen und Unterschiede in Untersuchungsdauer, Organbewegung und Bildqualität keine signifikanten Unterschiede in der Organsteifigkeit festgestellt. Die Bildregistrierung verbesserte die Bildschärfe, sodass kein signifikanter Unterschied zwischen freier Atmung und Atempause messbar war. Kaninchenlebern zeigten heterogene Steifigkeiten, sodass eine Zweiteilung in niedrige und hohe Steifigkeit (>1.6 m/s) signifikante Unterschiede in zentralen metabolischen Funktionen zeigte. Steifigkeit und Fluidität, die mittels der Mehrfrequenz-MRE gemessen werden, stellen vielversprechende quantitative Biomarker für die Diagnose maligner Lebertumoren dar. Abdominelle MRE in freier Atmung mit Bildregistrierung ist der beste Kompromiss aus schneller Untersuchungsdauer und guter Bildqualität. Die Anwendbarkeit an Kleintieren in einem klinischen MRT wurde gezeigt, inklusive Korrelationen zwischen mechanischen Lebereigenschaften und metabolischen Funktionen. Diese Arbeit konnte somit die Bildqualität mechanischer Karten sowohl für klinische als auch präklinische Untersuchungen verbessern und damit einen wichtigen Beitrag zur Translation der Multifrequenz-MRE als klinisch angewandte nichtinvasive Bildgebungsmethode abdomineller Organe und Pathologien leisten

Hierarchical classification of liver tumor from CT images based on difference-of-features (DOF)

This manuscript presents an automated classification approach to classifying lesions into four categories of liver diseases, based on Computer Tomography (CT) images. The four diseases types are Cyst, Hemangioma, Hepatocellular carcinoma (HCC), and Metastasis. The novelty of the proposed approach is attributed to utilising the difference of features (DOF) between the lesion area and the surrounding normal liver tissue. The DOF (texture and intensity) is used as the new feature vector that feeds the classifier. The classification system consists of two phases. The first phase differentiates between Benign and Malignant lesions, using a Support Vector Machine (SVM) classifier. The second phase further classifies the Benign into Hemangioma or Cyst and the Malignant into Metastasis or HCC, using a Naïve Bayes (NB) classifier. The experimental results show promising improvements to classify the liver lesion diseases. Furthermore, the proposed approach can overcome the problems of varying intensity ranges, textures between patients, demographics, and imaging devices and settings

Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions

Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio

Liver Tumors

This book is oriented towards clinicians and scientists in the field of the management of patients with liver tumors. As many unresolved problems regarding primary and metastatic liver cancer still await investigation, I hope this book can serve as a tiny step on a long way that we need to run on the battlefield of liver tumors

Diseases of the Abdomen and Pelvis 2018-2021: Diagnostic Imaging - IDKD Book

Gastrointestinal disease; PET/CT; Radiology; X-ray; IDKD; Davo

Ultrasound Imaging

This book provides an overview of ultrafast ultrasound imaging, 3D high-quality ultrasonic imaging, correction of phase aberrations in medical ultrasound images, etc. Several interesting medical and clinical applications areas are also discussed in the book, like the use of three dimensional ultrasound imaging in evaluation of Asherman's syndrome, the role of 3D ultrasound in assessment of endometrial receptivity and follicular vascularity to predict the quality oocyte, ultrasound imaging in vascular diseases and the fetal palate, clinical application of ultrasound molecular imaging, Doppler abdominal ultrasound in small animals and so on

Automated Characterisation and Classification of Liver Lesions From CT Scans

Cancer is a general term for a wide range of diseases that can affect any part of the body due to the rapid creation of abnormal cells that grow outside their normal boundaries. Liver cancer is one of the common diseases that cause the death of more than 600,000 each year. Early detection is important to diagnose and reduce the incidence of death. Examination of liver lesions is performed with various medical imaging modalities such as Ultrasound (US), Computer tomography (CT), and Magnetic resonance imaging (MRI). The improvements in medical imaging and image processing techniques have significantly enhanced the interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate. Moreover, CAD systems can help physician, as a second opinion, in characterising lesions and making the diagnostic decision. Thus, CAD systems have become an important research area. Particularly, these systems can provide diagnostic assistance to doctors to improve overall diagnostic accuracy. The traditional methods to characterise liver lesions and differentiate normal liver tissues from abnormal ones are largely dependent on the radiologists experience. Thus, CAD systems based on the image processing and artificial intelligence techniques gained a lot of attention, since they could provide constructive diagnosis suggestions to clinicians for decision making. The liver lesions are characterised through two ways: (1) Using a content-based image retrieval (CBIR) approach to assist the radiologist in liver lesions characterisation. (2) Calculating the high-level features that describe/ characterise the liver lesion in a way that is interpreted by humans, particularly Radiologists/Clinicians, based on the hand-crafted/engineered computational features (low-level features) and learning process. However, the research gap is related to the high-level understanding and interpretation of the medical image contents from the low-level pixel analysis, based on mathematical processing and artificial intelligence methods. In our work, the research gap is bridged if a relation of image contents to medical meaning in analogy to radiologist understanding is established. This thesis explores an automated system for the classification and characterisation of liver lesions in CT scans. Firstly, the liver is segmented automatically by using anatomic medical knowledge, histogram-based adaptive threshold and morphological operations. The lesions and vessels are then extracted from the segmented liver by applying AFCM and Gaussian mixture model through a region growing process respectively. Secondly, the proposed framework categorises the high-level features into two groups; the first group is the high-level features that are extracted from the image contents such as (Lesion location, Lesion focality, Calcified, Scar, ...); the second group is the high-level features that are inferred from the low-level features through machine learning process to characterise the lesion such as (Lesion density, Lesion rim, Lesion composition, Lesion shape,...). The novel Multiple ROIs selection approach is proposed, in which regions are derived from generating abnormality level map based on intensity difference and the proximity distance for each voxel with respect to the normal liver tissue. Then, the association between low-level, high-level features and the appropriate ROI are derived by assigning the ability of each ROI to represents a set of lesion characteristics. Finally, a novel feature vector is built, based on high-level features, and fed into SVM for lesion classification. In contrast with most existing research, which uses low-level features only, the use of high-level features and characterisation helps in interpreting and explaining the diagnostic decision. The methods are evaluated on a dataset containing 174 CT scans. The experimental results demonstrated that the efficacy of the proposed framework in the successful characterisation and classification of the liver lesions in CT scans. The achieved average accuracy was 95:56% for liver lesion characterisation. While the lesion’s classification accuracy was 97:1% for the entire dataset. The proposed framework is developed to provide a more robust and efficient lesion characterisation framework through comprehensions of the low-level features to generate semantic features. The use of high-level features (characterisation) helps in better interpretation of CT liver images. In addition, the difference-of-features using multiple ROIs were developed for robust capturing of lesion characteristics in a reliable way. This is in contrast to the current research trend of extracting the features from the lesion only and not paying much attention to the relation between lesion and surrounding area. The design of the liver lesion characterisation framework is based on the prior knowledge of the medical background to get a better and clear understanding of the liver lesion characteristics in medical CT images