Incremental shape statistics learning for prostate tracking in TRUS

Authors:
Pingkun Yan;Jochen Kruecker
Affiliations:
Philips Research North America, Briarcliff Manor, NY;Philips Research North America, Briarcliff Manor, NY
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Year:
2010

Citing 5
Cited 0

Active shape models—their training and application

Computer Vision and Image Understanding
CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
Prostate boundary segmentation from ultrasound images using 2D active shape models: Optimisation and extension to 3D

Computer Methods and Programs in Biomedicine
Incremental Learning for Robust Visual Tracking

International Journal of Computer Vision
Sequential Karhunen-Loeve basis extraction and its application to images

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Automatic delineation of the prostate boundary in transrectal ultrasound (TRUS) can play a key role in image-guided prostate intervention. However, it is a very challenging task for several reasons, especially due to the large variation of the prostate shape from the base to the apex. To deal with the problem, a new method for incrementally learning the patient-specific local shape statistics is proposed in this paper to help achieve robust and accurate boundary delineation over the entire prostate gland. The proposed method is fast and memory efficient in that new shapes can be merged into the shape statistics without recomputing using all the training shapes, which makes it suitable for use in real-time interventional applications. In our work, the learned shape statistics is incorporated into a modified sequential inference model for tracking the prostate boundary. Experimental results show that the proposed method is more robust and accurate than the active shape model using global population-based shape statistics in delineating the prostate boundary in TRUS.