Hanif M. Ladak

Prostate boundary segmentation from 2D ultrasound images

Outlining, or segmenting, the prostate is a very important task in the assignment of appropriate therapy and dose for cancer treatment; however, manual outlining is tedious and time-consuming. In this paper, an algorithm is described for semiautomatic segmentation of the prostate from 2D ultrasound images. The algorithm uses model-based initialization and the efficient discrete dynamic contour. Initialization requires the user to select only four points from which the outline of the prostate is estimated using cubic interpolation functions and shape information. The estimated contour is then deformed automatically to better fit the image. The algorithm can easily segment a wide range of prostate images, and contour editing tools are included to handle more difficult cases. The performance of the algorithm with a single user was compared to manual outlining by a single expert observer. The average distance between semiautomatically and manually outlined boundaries was found to be less than 5 pixels (0.63 mm), and the accuracy and sensitivity to area measurements were both over 90%.

Prostate segmentation from 2D ultrasound images

Outlining, or segmenting, the prostate is a very important task in the assignment of appropriate therapy and dose for cancer treatment; however, manual outlining is a tedious and time-consuming task. In this paper, an algorithm is described for semi-automatic segmentation of the prostate from 2D ultrasound images. The algorithm uses model-based initialization and the efficient discrete dynamic contour. Initialization requires the user to select only four points from which the outline of the prostate is estimated using cubic interpolation functions and shape information. The estimated contour is then deformed automatically to better fit the image. The algorithm can easily segment a wide range of prostate images, and contour editing tools are included to handle more difficult cases. The performance of the algorithm with a single user was compared to manual outlining by a single expert observer. The average distance between semi-automatically and manually outlined boundaries was found to be less than 5 pixels (0.63 mm), and the accuracy and sensitivity were both over 90%.

Prostate surface segmentation from 3D ultrasound images

Segmenting, or outlining, the prostate boundary is a very important task in diagnosing and treating cancer. In this paper, an algorithm is described for semiautomatic segmentation of the prostate from 3D ultrasound images. The algorithm uses model-based initialization and mesh refinement using an efficient deformable model. Initialization requires the user to select only six points from which the outline of the prostate is estimated using shape information. The estimated outline is then automatically deformed to better fit the prostate boundary. The performance of the algorithm with a single user was compared to manual outlining by the same user. The average distance between semiautomatically and manually outlined boundaries was found to be 1.19 mm, and the average difference in volumes was 7.2%.

Segmentation of ulcerated plaque: a semi-automatic method for tracking the progression of carotid atherosclerosis

A semi-automatic method for segmentation of three-dimensional carotid vascular ultrasound (US) images is presented. The method is based on a dynamic balloon model represented by a triangulated mesh. The mesh is manually placed within the interior of the carotid arteries, then is driven outward until it reaches the vessel wall by applying an inflation force to the mesh. Once the mesh is in close proximity to the vessel wall, it is further deformed using an image-based force, in order to better localize the boundary. The authors examine the ability of the segmentation method to segment in vivo 3D US images of the carotid arteries. They furthermore examine its ability to distinguish subtle changes in vessel morphology, with the ultimate goal being to detect the progression or regression of atherosclerotic plaque. Two nearly identical common carotid vessel phantoms were imaged using a three-dimensional US imaging system, automatically registered, then segmented using the semi-automatic algorithm. The fabrication of the two phantoms was identical except for the inclusion of a hemispherical ulceration cut into one of the vessels. The two segmented surfaces were compared by determining the distance between them at each point along one of the two surfaces. Since the 3D US images had been previously registered, the two segmented surfaces are expected to overlap everywhere except near the region of ulceration. This was confirmed to within a 0.3 mm error.

Development and evaluation of a semiautomatic 3D segmentation technique of the carotid arteries from 3D ultrasound images

In this paper, we report on a semi-automatic approach to segmentation of carotid arteries from 3D ultrasound (US) images. Our method uses a deformable model which first is rapidly inflated to approximately find the boundary of the artery, then is further deformed using image-based forces to better localize the boundary. An operator is required to initialize the model by selecting a position in the 3D US image, which is within the carotid vessel. Since the choice of position is user-defined, and therefore arbitrary, there is an inherent variability in the position and shape of the final segmented boundary. We have assessed the performance of our segmentation method by examining the local variability in boundary shape as the initial selected position is varied in a freehand 3D US image of a human carotid bifurcation. Our results indicate that high variability in boundary position occurs in regions where either the segmented boundary is highly curved, or the 3D US image has poorly defined vessel edges.

Semi-automatic technique for segmentation of the prostate from 2D ultrasound images

The segmentation technique is based on a deformable model that changes shape to fit the boundary of an object. Key to the success of the approach is the careful initialization of the contour, which requires the user to select only four points on the prostate boundary. Anatomic data and cubic interpolation are used to obtain an accurate estimate of the boundary. Gradient direction information is used during deformation to keep the contour from being attracted to extraneous boundaries.

Visualization and Segmentation Techniques in 3D Ultrasound Images

Although ultrasonography is an important cost-effective imaging modality, technical improvements are needed before its full potential is realized for accurate and quantitative monitoring of disease progression or regression. 2D viewing of 3D anatomy, using conventional ultrasonography limits our ability to quantify and visualize pathology and is partly responsible for the reported variability in diagnosis and monitoring of disease progression. Efforts of investigators have focused on overcoming these deficiencies by developing 3D ultrasound imaging techniques using existing conventional ultrasound systems, reconstructing the information into 3D images, and then allowing interactive viewing of the 3D images on inexpensive desktop computers. In addition, the availability of 3D ultrasound images has allowed the development of automated and semi-automated segmentation techniques to quantify organ and pathology volume for monitoring of disease. In this chapter, we introduce the basic principles of 3D ultrasound imaging as well as its visualization techniques. Then, we describe the use of 3D ultrasound in interventional procedures and discuss applications of 3D segmentation techniques of the prostates, needles, and seeds used in prostate brachytherapy.

Accuracy of a semi-automatic technique for segmentation of the carotid arteries from 3D ultrasound images

A semi-automatic 3D segmentation technique has recently been developed and is being used to extract carotid artery lumen from 3D ultrasound images. The accuracy of the segmentation technique has been assessed by comparing 3D surfaces derived by the semi-automatic technique, to surfaces constructed from manual segmentation. Preliminary results show that for a sample free-hand 3D ultrasound image, typical manual and automatic surfaces are separated from each other with a mean error of 0.4 mm.