Statistical shape models for 3D medical image segmentation: a review.

Current medical imaging technologies allow visualization of tissue anatomy in the human body at resolutions ranging from 100 micrometers to 1 millimeter. These technologies are generally not sensitive enough to detect early-stage tissue abnormalities associated with diseases such as cancer and atherosclerosis, which require micrometer-scale resolution. Here, optical coherence tomography was adapted to allow high-speed visualization of tissue in a living animal with a catheter-endoscope 1 millimeter in diameter. This method, referred to as "optical biopsy," was used to obtain cross-sectional images of the rabbit gastrointestinal and respiratory tracts at 10-micrometer resolution.

In Vivo Endoscopic Optical Biopsy with Optical Coherence Tomography

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets

Computerized Image Registration approaches can offer automatic and accurate image alignments without extensive user involvement and provide tools for visualizing combined images. The aim of this survey is to present a review of publications related to Medical Image Registration. This paper paints a comprehensive picture of image registration methods and their applications. This paper is an introduction for those new to the ﬁeld, an overview for those working in the ﬁeld and a reference for those searching for literature on a speciﬁc application. Methods are classiﬁed according to the diﬀerent aspects of Medical Image Registration.

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Survey of Medical Image Registration

The software system Amira has been designed to close the gap among the ease of use, power, interactivity of monolithic special-purpose software, and the flexibility and extensibility of data-flow-oriented visualization environments. One major focus of the software is the visualization and analysis of volumetric data, which is common in medicine, biology, and microscopy. With Amira, volumes can be displayed and segmented, 3D polygonal models can be reconstructed, and these models can be further processed and converted into tetrahedral volume grids. Due to its flexible design, Amira can also perform many other tasks, including finite-element postprocessing, flow visualization, and visualization of molecules. This chapter discusses the fundamental concepts, techniques, and features of Amira.

Amira: A highly interactive system for visual data analysis

We present a fully automatic 3D segmentation method for the liver from contrast-enhanced CT data. It is based on a combination of a constrained free-form and statistical deformable model. The adap- tation of the model to the image data is performed according to a simple model of the typical intensity distribution around the liver boundary and neighboring anatomical structures, considering the potential presence of tumors in the liver. All parameters of the deformation as well as the initial positioning of the model in the data are estimated automatically.

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Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model

An important issue in computer-assisted surgery of the liver is a fast and reliable transfer of preoperative resection plans to the intraoperative situation. One problem is to match the planning data, derived from preoperative CT or MR images, with 3D ultrasound images of the liver, acquired during surgery. As the liver deforms significantly in the intraoperative situation non-rigid registration is necessary. This is a particularly challenging task because pre- and intraoperative image data stem from different modalities and ultrasound images are generally very noisy. One way to overcome these problems is to incorporate prior knowledge into the registration process. We propose a method of combining anatomical landmark information with a fast non-parametric intensity registration approach. Mathematically, this leads to a constrained optimization problem. As distance measure we use the normalized gradient field which allows for multimodal image registration. A qualitative and quantitative validation on clinical liver data sets of three different patients has been performed. We used the distance of dense corresponding points on vessel center lines for quantitative validation. The combined landmark and intensity approach improves the mean and percentage of point distances above 3 mm compared to rigid and thin-plate spline registration based only on landmarks. The proposed algorithm offers the possibility to incorporate additional a priori knowledge—in terms of few landmarks—provided by a human expert into a non-rigid registration process.

3D ultrasound-CT registration of the liver using combined landmark-intensity information

This paper presents an automatic approach for segmentation of the liver from computer tomography (CT) images based on a 3D statistical shape model. Segmentation of the liver is an important prerequisite in liver surgery planning. One of the major challenges in building a 3D shape model from a training set of segmented instances of an object is the determination of the correspondence between different surfaces. We propose to use a geometric approach that is based on minimizing the distortion of the correspondence mapping between two different surfaces. For the adaption of the shape model to the image data a profile model based on the grey value appearance of the liver and its surrounding tissues in contrast enhanced CT data was developed. The robustness of this method results from a previous nonlinear diffusion filtering of the image data. Special focus is turned to the quantitative evaluation of the segmentation process. Several different error measures are discussed and implemented in a study involving more than 30 livers.

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Segmentation of the Liver using a 3D Statistical Shape Model

Organ deformation between preoperative image data and the patient in the OR is the main obstacle for using surgical navigation systems in liver surgery. Our approach is to provide accurate navigation via intraoperative 3D ultrasound. These ultrasound data are augmented with preoperative anatomical models and planning data as an important additional orientation aid for the surgeon. We present an overview of the whole ultrasound navigation system as well as an approach for fast intraoperative non-rigid registration of the preoperative models to the ultrasound volume. The registration method is based on the vessel center lines and consists of a combination of the Iterative Closest Point algorithm and multilevel B-Splines. Quantitative results for three different patients are presented.

/pdf/augmenting-intraoperative-3d-ultrasound-with-preoperative-362hu70my8.pdf

Augmenting Intraoperative 3D Ultrasound with Preoperative Models for Navigation in Liver Surgery

Liver resection has become increasingly safe as a result of considerable progress in equipment, technology, perioperative management, and surgical technique.1 Intraoperative identification of the vascular tree, ie, hepatic and portal veins and the localization of the tumor remains essential for segment oriented surgical resection.

Preoperative 3-dimensional (3D) imaging techniques using computed tomography (CT) or magnetic resonance imaging (MRI) data offer perfect visualization of the anatomy.2 However, 3D simulation models are mainly used for preoperative planning. Anatomic information cannot be transferred to the intraoperative situation directly, due to organ shift and deformation of the liver during mobilization for resection.3

The most frequently used intraoperative imaging technique is ultrasound. It is generally available, and its diagnostic quality is continuously improving. The main objectives of intraoperative ultrasound during liver surgery are to determine tumor resectability, to localize nonpalpable tumors, and to guide surgical procedures.4 Intraoperative imaging is particularly useful for resection of malignant liver lesions,5 but at this time it is mainly used to detect additional metastases.

Two-dimensional ultrasound is mostly displayed on a monoscopic video monitor. Advanced 3D and four-dimensional visualization for guidance of interventional procedures has been only realized in tumor ablation and liver biopsy procedures.6,7 The spatial relationship of the needle and the target lesion was conceived more intuitively with 3D and four-dimensional ultrasonography and helped in adjusting the needle to an optimal position.

We have integrated 3D ultrasound and optoelectronic tracking into a strategy of navigated liver resection. Optoelectronic tracking was used for this study. Accuracy and precision of the system were first validated in a tumor model in an experimental setup. Then a clinical study was performed to evaluate feasibility, basic requirements of 3D ultrasound visualization, performance, and resection margins in relation to the surgical plan.

Thomas Lange

Papers

Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model

3D ultrasound-CT registration of the liver using combined landmark-intensity information

Segmentation of the Liver using a 3D Statistical Shape Model

Augmenting Intraoperative 3D Ultrasound with Preoperative Models for Navigation in Liver Surgery

Feasibility of navigated resection of liver tumors using multiplanar visualization of intraoperative 3-dimensional ultrasound data.