A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term

Automatical segmentation : Application to 3D angiograms of the liver

As part of a hepatic surgery simulator, we have developed a new method for the extraction of the portal vein's vascular tree i n 3D liver angioscanners . In practice, this tree is used to localize the different anatomical segments that correspond to the unit o f surgical ablation of the liver . Our method thus facilitates the surgeon's task by automatically giving the 3D model of the portal vei n in a three-step segmentation . The first step reduces the image to the ROI defined by the liver contours and increases its qualit y by an anisotropic filtering . The second step performs the segmentation of vascular networks by a global thresholding followed b y a local analysis . The third step translates a priori knowledge in topological and geometrical constraints . This last step allows to remove mistakes due to the anisotropy of the images by disconnecting the different vascular trees in order to extract the porta l vein . Results on 12 patients, validated by a radiologist, showed that the algorithm automatically extracts the principal branche s of the portal vein, allowing to delimit the anatomical segments defined in the conventional liver anatomy .Dans le cadre de la réalisation d'un simulateur de chirurgie laparoscopique' du foie, nous avons développé une nouvelle méthode permettant d'extraire dans les angioscanners 3D du foie, le réseau vasculaire de la veine porte. Ce réseau est utilisé en pratique pour repérer les différents segments anatomiques, qui représentent l'unité d'intervention dans les exérèses2 du foie. Notre méthode facilite ainsi la tâche des chirurgiens, en leur fournissant automatiquement le modèle 3D de la veine porte par une segmentation décomposée en trois étapes. La première étape réduit l'image à la région d'intérêt correspondant au contour du foie et améliore sa qualité en réalisant un filtrage anisotrope. La seconde segmente les réseaux vasculaires et appliquant un seuillage global, suivi d'une analyse locale. La troisième étape traduit les connaissances a priori que nous avons des réseaux vasculaires, en contraintes topologiques et géométriques. Cette dernière étape permet de corriger les problèmes résultant de l'anisotropie des images, en déconnectant les différentes arborescences du foie pour en extraire la veine porte. Les résultats obtenus sur douze patients, et vérifiés par un radiologue, montrent que l'algorithme extrait automatiquement les principales branches de la veine porte, permettant de délimiter les segments anatomiques définis dans l'anatomie conventionnelle du foie

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

Fast Elastic Registration of Soft Tissues under Large Deformations

International audienceA fast and accurate fusion of intra-operative images with a pre-operative data is a key component of computer-aided interventions which aim at improving the outcomes of the intervention while reducing the patient's discomfort. In this paper, we focus on the problematic of the intra-operative navigation during abdominal surgery, which requires an accurate registration of tissues undergoing large deformations. Such a scenario occurs in the case of partial hepatectomy: to facilitate the access to the pathology, e.g. a tumor located in the posterior part of the right lobe, the surgery is performed on a patient in lateral position. Due to the change in patient's position, the resection plan based on the pre-operative CT scan acquired in the supine position must be updated to account for the deformations. We suppose that an imaging modality, such as the cone-beam CT, provides the information about the intra-operative shape of an organ, however, due to the reduced radiation dose and contrast, the actual locations of the internal structures necessary to update the planning are not available. To this end, we propose a method allowing for fast registration of the pre-operative data represented by a detailed 3D model of the liver and its internal structure and the actual configuration given by the organ surface extracted from the intra-operative image. The algorithm behind the method combines the iterative closest point technique with a biomechanical model based on a co-rotational formulation of linear elasticity which accounts for large deformations of the tissue. The performance, robustness and accuracy of the method is quantitatively assessed on a control semi-synthetic dataset with known ground truth and a real dataset composed of nine pairs of abdominal CT scans acquired in supine and flank positions. It is shown that the proposed surface-matching method is capable of reducing the target registration error evaluated of the internal structures of the organ from more than 40 mm to less then 10 mm. Moreover, the control data is used to demonstrate the compatibility of the method with intra-operative clinical scenario, while the real datasets are utilized to study the impact of parametrization on the accuracy of the method. The method is also compared to a state-of-the art intensity-based registration technique in terms of accuracy and performance

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