Segmenting CT prostate images using population and patient-specific statistics for radiotherapy

Authors:
Qianjin Feng;Mark Foskey;Songyuan Tang;Wufan Chen;Dinggang Shen
Affiliations:
Biomedical Engineering College, South Medical University, Guangzhou, China and Radiology, University of North Carolina, Chapel Hill;Radiation Oncology Departments, University of North Carolina, Chapel Hill;Radiology Departments, University of North Carolina, Chapel Hill;Biomedical Engineering College, South Medical University, Guangzhou, China;Radiology Departments, University of North Carolina, Chapel Hill
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

This paper presents a new deformable model using both population and patient-specific statistics to segment the prostate from CT images. There are two novelties in the proposed method. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than general intensity and gradient features, is used to characterize the image features. Second, an online training approach is used to build the shape statistics for accurately capturing intra-patient variation, which is more important than inter-patient variation for prostate segmentation in clinical radiotherapy. Experimental results show that the proposed method is robust and accurate, suitable for clinical application.