Review on the methods of automatic liver segmentation from abdominal images

Automatic liver segmentation from abdominal images is challenging on the aspects of segmentation accuracy, automation and robustness. There exist many methods of liver segmentation and ways of categorisingthem. In this paper, we present a new way of summarizing the latest achievements in automatic liver segmentation.We categorise a segmentation method according to the image feature it works on, therefore better summarising the performance of each category and leading to finding an optimal solution for a particular segmentation task. All the methods of liver segmentation are categorized into three main classes including gray level based method, structure based method and texture based method. In each class, the latest advance is reviewed with summary comments on the advantages and drawbacks of each discussed approach. Performance comparisons among the classes are given along with the remarks on the problems existed and possible solutions. In conclusion, we point out that liver segmentation is still an open issue and the tendency is that multiple methods will be employed to-gether to achieve better segmentation performance

Segmentation and Deformable Modelling Techniques for a Virtual Reality Surgical Simulator in Hepatic Oncology

Liver surgical resection is one of the most frequently used curative therapies. However, resectability is problematic. There is a need for a computer-assisted surgical planning and simulation system which can accurately and efficiently simulate the liver, vessels and tumours in actual patients. The present project describes the development of these core segmentation and deformable modelling techniques. For precise detection of irregularly shaped areas with indistinct boundaries, the segmentation incorporated active contours - gradient vector flow (GVF) snakes and level sets. To improve efficiency, a chessboard distance transform was used to replace part of the GVF effort. To automatically initialize the liver volume detection process, a rotating template was introduced to locate the starting slice. For shape maintenance during the segmentation process, a simplified object shape learning step was introduced to avoid occasional significant errors. Skeletonization with fuzzy connectedness was used for vessel segmentation. To achieve real-time interactivity, the deformation regime of this system was based on a single-organ mass-spring system (MSS), which introduced an on-the-fly local mesh refinement to raise the deformation accuracy and the mesh control quality. This method was now extended to a multiple soft-tissue constraint system, by supplementing it with an adaptive constraint mesh generation. A mesh quality measure was tailored based on a wide comparison of classic measures. Adjustable feature and parameter settings were thus provided, to make tissues of interest distinct from adjacent structures, keeping the mesh suitable for on-line topological transformation and deformation. More than 20 actual patient CT and 2 magnetic resonance imaging (MRI) liver datasets were tested to evaluate the performance of the segmentation method. Instrument manipulations of probing, grasping, and simple cutting were successfully simulated on deformable constraint liver tissue models. This project was implemented in conjunction with the Division of Surgery, Hammersmith Hospital, London; the preliminary reality effect was judged satisfactory by the consultant hepatic surgeon

On Medical Image Segmentation and on Modeling Long Term Dependencies

La délimitation (segmentation) des tumeurs malignes à partir d’images médicales est importante pour le diagnostic du cancer, la planification des traitements ciblés, ainsi que les suivis de la progression du cancer et de la réponse aux traitements. Cependant, bien que la segmentation manuelle des images médicales soit précise, elle prend du temps, nécessite des opérateurs experts et est souvent peu pratique lorsque de grands ensembles de données sont utilisés. Ceci démontre la nécessité d’une segmentation automatique. Cependant, la segmentation automatisée des tumeurs est particulièrement difficile en raison de la variabilité de l’apparence des tumeurs, de l’équipement d’acquisition d’image et des paramètres d’acquisition, et de la variabilité entre les patients. Les tumeurs varient en type, taille, emplacement et quantité; le reste de l’image varie en raison des différences anatomiques entre les patients, d’une chirurgie antérieure ou d’une thérapie ablative, de différences dans l’amélioration du contraste des tissus et des artefacts d’image. De plus, les protocoles d’acquisition du scanner varient considérablement entre les cliniques et les caractéristiques de l’image varient selon le modèle du scanner. En raison de toutes ces variabilités, un modèle de segmentation doit être suffisamment flexible pour apprendre les caractéristiques générales des données. L’avènement des réseaux profonds de neurones à convolution (convolutional neural networks, CNN) a permis une classification exacte et précise des images hautement variables et, par extension, une segmentation de haute qualité des images. Cependant, ces modèles doivent être formés sur d’énormes quantités de données étiquetées. Cette contrainte est particulièrement difficile dans le contexte de la segmentation des images médicales, car le nombre de segmentations pouvant être produites est limité dans la pratique par la nécessité d’employer des opérateurs experts pour réaliser un tel étiquetage. De plus, les variabilités d’intérêt dans les images médicales semblent suivre une distribution à longue traîne, ce qui signifie qu’un nombre particulièrement important de données utilisées pour l’entraînement peut être nécessaire pour fournir un échantillon suffisant de chaque type de variabilité à un CNN. Cela démontre la nécessité de développer des stratégies pour la formation de ces modèles avec des segmentations de vérité-terrain disponibles limitées.----------ABSTRACT: The delineation (segmentation) of malignant tumours in medical images is important for cancer diagnosis, the planning of targeted treatments, and the tracking of cancer progression and treatment response. However, although manual segmentation of medical images is accurate, it is time consuming, requires expert operators, and is often impractical with large datasets. This motivates the need for training automated segmentation. However, automated segmentation of tumours is particularly challenging due to variability in tumour appearance, image acquisition equipment and acquisition parameters, and variability across patients. Tumours vary in type, size, location, and quantity; the rest of the image varies due to anatomical differences between patients, prior surgery or ablative therapy, differences in contrast enhancement of tissues, and image artefacts. Furthermore, scanner acquisition protocols vary considerably between clinical sites and image characteristics vary according to the scanner model. Due to all of these variabilities, a segmentation model must be flexible enough to learn general features from the data. The advent of deep convolutional neural networks (CNN) allowed for accurate and precise classification of highly variable images and, by extension, of high quality segmentation images. However, these models must be trained on enormous quantities of labeled data. This constraint is particularly challenging in the context of medical image segmentation because the number of segmentations that can be produced is limited in practice by the need to employ expert operators to do such labeling. Furthermore, the variabilities of interest in medical images appear to follow a long tail distribution, meaning a particularly large amount of training data may be required to provide a sufficient sample of each type of variability to a CNN. This motivates the need to develop strategies for training these models with limited ground truth segmentations available

A Multiatlas Segmentation Using Graph Cuts with Applications to Liver Segmentation in CT Scans

An atlas-based segmentation approach is presented that combines low-level operations, an affine probabilistic atlas, and a multiatlas-based segmentation. The proposed combination provides highly accurate segmentation due to registrations and atlas selections based on the regions of interest (ROIs) and coarse segmentations. Our approach shares the following common elements between the probabilistic atlas and multiatlas segmentation: (a) the spatial normalisation and (b) the segmentation method, which is based on minimising a discrete energy function using graph cuts. The method is evaluated for the segmentation of the liver in computed tomography (CT) images. Low-level operations define a ROI around the liver from an abdominal CT. We generate a probabilistic atlas using an affine registration based on geometry moments from manually labelled data. Next, a coarse segmentation of the liver is obtained from the probabilistic atlas with low computational effort. Then, a multiatlas segmentation approach improves the accuracy of the segmentation. Both the atlas selections and the nonrigid registrations of the multiatlas approach use a binary mask defined by coarse segmentation. We experimentally demonstrate that this approach performs better than atlas selections and nonrigid registrations in the entire ROI. The segmentation results are comparable to those obtained by human experts and to other recently published results

Characterising pattern asymmetry in pigmented skin lesions

Abstract. In clinical diagnosis of pigmented skin lesions asymmetric pigmentation is often indicative of melanoma. This paper describes a method and measures for characterizing lesion symmetry. The estimate of mirror symmetry is computed first for a number of axes at different degrees of rotation with respect to the lesion centre. The statistics of these estimates are the used to assess the overall symmetry. The method is applied to three different lesion representations showing the overall pigmentation, the pigmentation pattern, and the pattern of dermal melanin. The best measure is a 100% sensitive and 96% specific indicator of melanoma on a test set of 33 lesions, with a separate training set consisting of 66 lesions

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas