Biomedical subjects

Mingyue Ding

Publications and source records attributed to Mingyue Ding.

5 recordsLinked to original sources

PubMed2006

Needle and seed segmentation in intra-operative 3D ultrasound-guided prostate brachytherapy.

In order to guide the needle to the correct location in 3D US-guided brachytherapy, the needle is continuously tracked as it is being inserted. A pre-scan before the needle insertion and a post-scan after the needle insertion are subtracted to obtain a difference image containing the needle. The image is projected along two orthogonal directions approximately perpendicular to the needle, and the 3D needle is reconstructed from the segmented needles in the two projected images. The seeds implanted with the needle are located in the cropped volume along the needle. Thus, the seeds are segmented using a tri-bar model and 3D line segment patterns. Finally, the positions of the seeds are determined using a peak detection technique. Experiments with agar and turkey/chicken phantoms as well as patient data demonstrated that our needle segmentation technique could segment the needle in near real-time with an accuracy of 0.6 mm in position and 1.0 degrees in orientation. The true-positive rate for seed segmentation is 100% for the agar phantom and 93% for the chicken phantom. The average distance to manual seed segmentation was 1.0mm for the agar phantom and 1.7 mm for the chicken phantom.

Algorithms↗

PubMed2005

3D TRUS guided robot assisted prostate brachytherapy.

This paper describes a system for dynamic intraoperative prostate brachytherapy using 3D ultrasound guidance with robot assistance. The system consists of 3D transrectal ultrasound (TRUS) imaging, a robot and software for prostate segmentation, 3D dose planning, oblique needle segmentation and tracking, seed segmentation, and dynamic re-planning and verification. The needle targeting accuracy of the system was 0.79 mm +/- 0.32 mm in a phantom study.

Algorithms↗

PubMed2004

Projection-based needle segmentation in 3D ultrasound images.

Needles are used extensively in interventional procedures such as biopsy and brachytherapy. To deliver radioactive seeds to pre-planned positions or sample lesions from the region that may contain cancer cells, the 3D position of the needle must be determined accurately and quickly. Three-dimensional ultrasound (US) image guidance is an efficient technique used to perform this task. In this paper, we describe the development of a projection-based needle segmentation method comprising three steps. First, the 3D image is projected along an initial direction perpendicular to the approximate needle direction determined from the 3D imaging system. The needle is then segmented in a projected 2D image. Using the projection direction and the detected 2D needle direction, a plane containing the needle--called the needle plane--is determined. Secondly, the 3D image is re-projected in the direction perpendicular to the normal of the needle plane and step 1 is repeated. If the needle direction in the projected 2D image is horizontal, the needle plane is correct; otherwise, steps 1 and 2 are repeated until a correct needle plane is found. Thirdly, the 3D image is projected along the normal direction of the needle plane and the needle endpoints in the projected 2D image are determined. Using the relationship between the 3D projection and the 3D volume coordinate systems, the coordinates of the endpoints of the needle in the 3D US coordinate system are determined. Experiments with agar and turkey phantom 3D US images demonstrated that our method could segment the needle from 3D US images with an average accuracy of 0.7 mm in position and 1.2 degrees in orientation with a speed of 13 fps on a 1.3-GHz PC. In addition, experiments illustrated that our method is robust to variations in the initial estimated needle direction, the size of the cropped volume, and the ray-casting transfer function parameters used in pre-processing.

Algorithms↗

PubMed2003

Automatic needle segmentation in three-dimensional ultrasound images using two orthogonal two-dimensional image projections.

In this paper, we describe an algorithm to segment a needle from a three-dimensional (3D) ultrasound image by using two orthogonal two-dimensional (2D) image projections. Not only is the needle more conspicuous in a projected (volume-rendered) image, but its direction in 3D lies in the plane defined by the projection direction and the needle direction in the projected 2D image. Hence, using two such projections, the 3D vector describing the needle direction lies along the intersection of the two corresponding planes. Thus, the task of 3D needle segmentation is reduced to two 2D needle segmentations. For improved accuracy and robustness, we use orthogonal projection directions (both orthogonal to a given a priori estimate of the needle direction), and use volume cropping and Gaussian transfer functions to remove complex background from the 2D projection images. To evaluate our algorithm, we tested it with 3D ultrasound images of agar and turkey breast phantoms. Using a 500 MHz personal computer equipped with a commercial volume-rendering card, we found that our 3D needle segmentation algorithm performed in near real time (about 10 fps) with a root-mean-square accuracy in needle length and endpoint coordinates of better than 0.8 mm, and about 0.5 mm on average, for needles lengths in the 3D image from 4.0 mm to 36.7 mm.

Algorithms↗

PubMed2003

A real-time biopsy needle segmentation technique using Hough transform.

Real-time needle segmentation and tracking is very important in image-guided surgery, biopsy, and therapy. Due to its robustness to the addition of extraneous noise, the Hough Transform is one of the most powerful line-detection techniques nowadays and has been widely used in different areas. Unfortunately, its high computation needs often prevent it from being applied in real-time applications without the help of specially designed hardware. In order to solve this problem, a variety of fast implementation algorithms have been proposed. However, none of them can be performed in a real time on an affordable computer. In this paper, we describe a fast implementation of the Hough Transform based on coarse-fine search and the determination of the optimal image resolution. Compared to conventional techniques, our approach decreases the time for needle segmentation by an order of magnitude. Experiments with agar phantom and patient breast biopsy ultrasound (US) image sequences showed that our approach can segment the biopsy needle in real time (i.e., less than 33 ms) on an affordable PC computer without the help of specially designed hardware with the angular rms error of about 1 degrees and the position rms error of about 0.5 mm.

Agar↗