Yanling Chi

Segmentation of Liver Vasculature From Contrast Enhanced CT Images Using Context-Based Voting

A novel vessel context-based voting is proposed for automatic liver vasculature segmentation in CT images. It is able to conduct full vessel segmentation and recognition of multiple vasculatures effectively. The vessel context describes context information of a voxel related to vessel properties, such as intensity, saliency, direction, and connectivity. Voxels are grouped to liver vasculatures hierarchically based on vessel context. They are first grouped locally into vessel branches with the advantage of a vessel junction measurement and then grouped globally into vasculatures, which is implemented using a multiple feature point voting mechanism. The proposed method has been evaluated on ten clinical CT datasets. Segmentation of third-order vessel trees from CT images (0.76 × 0.76 × 2.0 mm) of the portal venous phase takes less than 3 min on a PC with 2.0 GHz dual core processor and the average segmentation accuracy is up to 98%.

Three-Dimensional Spatiotemporal Features for Fast Content-Based Retrieval of Focal Liver Lesions

Content-based image retrieval systems for 3-D medical datasets still largely rely on 2-D image-based features extracted from a few representative slices of the image stack. Most 2-D features that are currently used in the literature not only model a 3-D tumor incompletely but are also highly expensive in terms of computation time, especially for high-resolution datasets. Radiologist-specified semantic labels are sometimes used along with image-based 2-D features to improve the retrieval performance. Since radiological labels show large interuser variability, are often unstructured, and require user interaction, their use as lesion characterizing features is highly subjective, tedious, and slow. In this paper, we propose a3-D image-based spatiotemporal feature extraction framework for fast content-based retrieval of focal liver lesions. All the features are computer generated and are extracted from four-phase abdominal CT images. Retrieval performance and query processing times for the proposed framework is evaluated on a database of 44 hepatic lesions comprising of five pathological types. Bulls eye percentage score above 85% is achieved for three out of the five lesion pathologies and for 98% of query lesions, at least one same type of lesion is ranked among the top two retrieved results. Experiments show that the proposed systems query processing is more than 20 times faster than other already published systems that use 2-D features. With fast computation time and high retrieval accuracy, the proposed system has the potential to be used as an assistant to radiologists for routine hepatic tumor diagnosis.

Modeling n-Furcated Liver vessels From a 3-D Segmented Volume Using Hole-Making and Subdivision Methods

It is difficult to build an accurate and smooth liver vessel model due to the tiny size, noise, and n-furcations of vessels. To overcome these problems, we propose an n-furcation vessel tree modeling method. In this method, given a segmented volume and a point indicating the root of the vessels, centerlines and cross-sectional contours of the vessels are extracted and organized as a tree first. Then, the tree is broken up into separate branches in descending order of length, and polygonal meshes of all the branches are separately constructed from the cross-sectional contours. Finally, all the meshes are combined sequentially using our hole-making approach. Holes are made on a coarse mesh, and a final fine mesh is generated using a subdivision method. The hole-making approach with the subdivision method provides good efficiency in mesh construction as well as great flexibilities in mesh editing. Experiments show that our method can automatically construct smooth mesh models for n-furcated vessels with mean absolute error of 0.92 voxel and mean relative error of 0.17. It is promising to be used in diagnosis, analysis, and surgery simulation of liver diseases, and is able to model tubular structures with tree topology.

A Composite of Features for Learning-Based Coronary Artery Segmentation on Cardiac CT Angiography

Coronary artery segmentation is important in quantitative coronary angiography. In this work, a novel method is proposed for coronary artery segmentation. It integrates coronary artery features of density, local shape and global structure into the learning framework. The density feature is the vessels relative density estimated by means of Gaussian mixture models and is able to suppress individual variances. The local tube shape of the vessel is measured with the advantages of the 3-dimensional multi-scale Hessian filter and is able to enhance the small vessels. The global structure feature is predicted from a support vector regression in terms of vessels spatial position and emphasizes the geometric morphometric attribute of the coronary artery tree running across the surface of the heart. The features are fed into a support vector classifier for vessel segmentation. The proposed methodology was tested on ten 3D cardiac computed tomography angiography datasets. It obtained a sensitivity of 81%, a specificity of 99%, and Dice coefficient of 84%. The performance is good.

Liver tumor detection and classification using content-based image retrieval

Computer aided liver tumor detection and diagnosis can assist radiologists to interpret abnormal features in liver CT scans. In this paper, a general frame work is proposed to automatically detect liver focal mass lesions, conduct differential diagnosis of liver focal mass lesions based on multiphase CT scans, and provide visually similar case samples for comparisons. The proposed method first detects liver abnormalities by eliminating the normal tissue/organ from the liver region, and in the second step it ranks these abnormalities with respect to spherical symmetry, compactness and size using a tumoroid measure to facilitate fast location of liver focal mass lesions. To differentiate liver focal mass lesions, content-based image retrieval technique is used to query a CT model database with known diagnosis. Multiple-phase encoded texture features are proposed to represent the focal mass lesions. A hypercube indexing structure based method is adopted as the retrieval strategy and the similarity score is calculated to rank the retrieval results. Good performances are obtained from eight clinical CT scans. With the proposed method, the clinician is expected to improve the accuracy of differential diagnosis.

Surgical Simulation Robot with Haptics and Friction Compensation

Haptic feedback brings a surgical simulator closer to real surgery. However, friction in surgical simulators hardware affects its performance significantly. We introduce a surgical simulation robot with roller mechanism for laparoscopic surgical simulation. Roller mechanism is implemented in a constrained space to reduce the friction. Motion based friction cancellation method is also applied to further mitigate the friction effects. Comparing with the same surgical simulation robot without roller mechanism, the one with roller mechanism reduces friction by 32.86i?ź% and 38.87i?ź% on two motion directions, and the motion based friction cancellation method can mitigate the friction effect by 49.46i?ź% and 62.08i?ź% on the two motion directions.

A quantitative evaluation function for 3D tree-like structure segmentations in liver images

The analysis of vascular structure from volumetric datasets plays a crucial role in many medical applications. Many segmentation algorithms have been designed to extract the vessel features. However, to date, these algorithms have not been efficiently evaluated and a large-scale manual analysis is always impossible. In this paper, we propose a quantitative evaluation function based on connectivity, overlap volume ratio, skeleton coincidence and branches structure error to deal with this evaluation task. The function is applicable to 3D tree-like structure segmentations and does not depend on the segmentation algorithms used. The performances of this evaluation function are tested on real liver datasets. The results show that the values of evaluation function are very close to the human scoring value (standard value), and the average value of relative errors is only 7.3% over all the eight datasets, while other evaluation measurements are 20% or more. So, it provides the greatest correlation with human quality perception when compared with other evaluation measurements. Thus, it is the most suitable measure for the evaluation of 3D tree-like structure segmentations in liver images.

Planning of Middle Hepatic Vein-Guided Hemihepatectomy: Resection Pathway Construction and Optimization

Hemihepatectomy is a regular way to resect the liver graft for living donor liver transplantation. Middle hepatic vein (MHV)-guided precise hemihepatectomy demands high-quality pre-surgery planning. This paper presents a pre-operative planning system to assist surgeons in risk assessment and planning the resection pathway with a desired safety margin to MHV. Our algorithm is able to automatically construct a smooth resection pathway according to a few user-input control points and the desired safety margin. Moreover, the resection pathway is optimized by minimizing the resection area for less liver impair. Experiment of planning MHV-guided hemihepatectomy on six healthy livers was conducted and the blood-free liver parenchyma volumes of the graft and the remnant were computed for risk assessment. The comparison between the planning results using the proposed system and the results from the conventional 2D slice-based planning suggests that the proposed planning system is more convenient and provides a better planning result.