Liver Segmentation and its Application to Hepatic Interventions

The thesis addresses the development of an intuitive and accurate liver segmentation approach, its integration into software prototypes for the planning of liver interventions, and research on liver regeneration. The developed liver segmentation approach is based on a combination of the live wire paradigm and shape-based interpolation. Extended with two correction modes and integrated into a user-friendly workflow, the method has been applied to more than 5000 data sets. The combination of the liver segmentation with image analysis of hepatic vessels and tumors allows for the computation of anatomical and functional remnant liver volumes. In several projects with clinical partners world-wide, the benefit of the computer-assisted planning was shown. New insights about the postoperative liver function and regeneration could be gained, and most recent investigations into the analysis of MRI data provide the option to further improve hepatic intervention planning

Computer-Aided Diagnosis and Quantification of Cirrhotic Livers Based on Morphological Analysis and Machine Learning

It is widely known that morphological changes of the liver and the spleen occur during the clinical course of chronic liver diseases. In this paper, we proposed a morphological analysis method based on statistical shape models (SSMs) of the liver and spleen for computer-aided diagnosis and quantification of the chronic liver. We constructed not only the liver SSM but also the spleen SSM and a joint SSM of the liver and the spleen for a morphologic analysis of the cirrhotic liver in CT images. The effective modes are selected based on both its accumulation contribution rate and its correlation with doctor’s opinions (stage labels). We then learn a mapping function between the selected mode and the stage of chronic liver. The mapping function was used for diagnosis and staging of chronic liver diseases

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

Optimization of tumor ablation by monitoring tissue temperature via CT

Niet-invasieve temperatuurmeting met behulp van beeldvormende technieken zoals magnetische resonantie beeldvorming (MRI), echografie (US) en computer tomografie (CT) maakt het mogelijk om de thermische ablatie van tumoren te optimaliseren door middel van het real-time bewaken van de temperatuurverdeling in de tumor en de aangrenzende weefsels. Hoewel er studies zijn gedaan naar de warmtegevoeligheid van de CT in verschillende weefsels, is verder onderzoek met CT tot nu toe niet gedaan als gevolg van technische beperkingen. Met de komst van de nieuwe generatie CT scanners zijn daarom nu de mogelijkheden van de huidige CT systemen onderzocht op warmtegevoeligheid en de mogelijkheden om CT thermometrie te implementeren. In dit onderzoek is de warmtegevoeligheid van CT beoordeeld tijdens ablatie en laserablatie terwijl tegelijkertijd de temperatuur werd gemeten met thermische probes. Dichtbij de warmtebron werd een hypodense gebied waargenomen veroorzaakt door een verandering in de CT-waarden ten gevolge van temperatuurstijgingen. In alle experimenten was er omgekeerde lineaire afhankelijkheid te zien van de CT-waarden van de temperatuur. De warmtegevoeligheid varieerde van -0.35HU/oC tot -0.65HU/oC in ex-vivo modellen en van -0.03HU/oC tot -0.43HU/oC in in-vivo modellen. Bovendien kon met behulp van de gemeten warmtegevoeligheid het geableerde gebied omgezet worden in een warmtebeeld met temperatuurverdeling. Uit deze studie kan worden geconcludeerd dat niet-invasieve CT thermometrie een bruikbare techniek is en dat aan alle criteria voor niet-invasieve temperatuurmeting werd voldaan, behalve de temperatuur resolutie (Frich et al. 2006). Verdere in-vivo studies moeten non-invasieve CT thermometrie verder verbeteren zodat deze techniek mogelijk kan worden toegepast in de klinische praktijk voor de behandeling van tumoren met een real-time bewaking van de temperatuur

State-of-the-art MR imaging in the work-up of primary hepatocellular tumors

Magnetic resonance (MR) imaging is an imaging modality that has evolved rapidly in the past two decades. The development of advanced hardware and new sophisticated pulse sequences have allowed faster imaging, with increased temporal and spatial resolution. This has resulted in the development and implementation of new acquisition techniques that facilitate improved visualisation of neoplastic processes. In addition, faster sequences enable multiphasic dynamic imaging after intravenous administration of contrast material, which results in better tumor characterisation and improved diagnostic confidence by the reading radiologist. The radiol

Development Of Semi-Automatic Liver Segmentation Method For Three-Dimensional Computed Tomography Dataset

Segmentation of liver from 3D computed tomography (CT) dataset is very important in hepatic disease diagnosis and treatment planning. Manual segmentation gives accurate result but the process is tedious and time-consuming due to a large number of slices produced by the CT scanner. Low contrast of liver boundary with neighbouring organs, high shape variability of liver and presence of various liver pathologies will affect the accuracy of automatic liver segmentation and thus make automatic liver segmentation a challenging task. Therefore, a semi-automated liver segmentation program is developed in this project in order to obtain high accuracy in liver segmentation and reduce the time required for manual liver segmentation. The proposed algorithm can be divided into three stages. The first stage is parameter setup and pre-processing. User interaction is required to setup the segmentation parameters. For pre-processing, anisotropic diffusion filtering is applied to reduce noise in the image and smooth the image. In second stage, thresholding is applied to CT images to extract the possible liver regions. Then, morphological closing and opening are used close small holes inside liver region and break the thin connections between liver and neighbouring organs. Hole-filling is employed to fill up the large holes inside liver region. Next, the connected component analysis is performed to extract liver region from the CT slices. The last stage is post-processing. In post-processing, the contour of liver is smooth by binary Gaussian filter. The liver segmentation program with proposed algorithm is evaluated with CT datasets obtained from SLIVER07 to prove its effectiveness in liver segmentation. The results of liver segmentation achieved average VOE of 9.9

Automatic 2D and 3D segmentation of liver from Computerised Tomography.

As part of the diagnosis of liver disease, a Computerised Tomography (CT) scan is taken of the patient, which the clinician then uses for assistance in determining the presence and extent of the disease. This thesis presents the background, methodology, results and future work of a project that employs automated methods to segment liver tissue. The clinical motivation behind this work is the desire to facilitate the diagnosis of liver disease such as cirrhosis or cancer, assist in volume determination for liver transplantation, and possibly assist in measuring the effect of any treatment given to the liver. Previous attempts at automatic segmentation of liver tissue have relied on 2D, low-level segmentation techniques, such as thresholding and mathematical morphology, to obtain the basic liver structure. The derived boundary can then be smoothed or refined using more advanced methods. The 2D results presented in this thesis improve greatly on this previous work by using a topology adaptive active contour model to accurately segment liver tissue from CT images. The use of conventional snakes for liver segmentation is difficult due to the presence of other organs closely surrounding the liver this new technique avoids this problem by adding an inflationary force to the basic snake equation, and initialising the snake inside the liver. The concepts underlying the 2D technique are extended to 3D, and results of full 3D segmentation of the liver are presented. The 3D technique makes use of an inflationary active surface model which is adaptively reparameterised, according to its size and local curvature, in order that it may more accurately segment the organ. Statistical analysis of the accuracy of the segmentation is presented for 18 healthy liver datasets, and results of the segmentation of unhealthy livers are also shown. The novel work developed during the course of this project has possibilities for use in other areas of medical imaging research, for example the segmentation of internal liver structures, and the segmentation and classification of unhealthy tissue. The possibilities of this future work are discussed towards the end of the report