Fully automatic prostate segmentation from transrectal ultrasound images based on radial bas-relief initialization and slice-based propagation. (1st July 2016)

• Record Type: Journal Article
• Authors: Yu, Yanyan; Chen, Yimin; Chiu, Bernard
• Is Part Of: Computers in biology and medicine. Volume 74(2016)
• Page Start: 74
• Page End: 90
• Publication Date: 2016-07-01
• Subjects: Prostate transrectal ultrasound image -- Fully automatic segmentation -- Radial bas-relief (RBR) initialization -- 2D-slice-based segmentation propagation -- Dyadic wavelet transform; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2016.05.002
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Prostate segmentation from transrectal ultrasound (TRUS) images plays an important role in the diagnosis and treatment planning of prostate cancer. In this paper, a fully automatic slice-based segmentation method was developed to segment TRUS prostate images. The initial prostate contour was determined using a novel method based on the radial bas-relief (RBR) method, and a false edge removal algorithm proposed here in. 2D slice-based propagation was used in which the contour on each image slice was deformed using a level-set evolution model, which was driven by edge-based and region-based energy fields generated by dyadic wavelet transform. The optimized contour on an image slice propagated to the adjacent slice, and subsequently deformed using the level-set model. The propagation continued until all image slices were segmented. To determine the initial slice where the propagation began, the initial prostate contour was deformed individually on each transverse image. A method was developed to self-assess the accuracy of the deformed contour based on the average image intensity inside and outside of the contour. The transverse image on which highest accuracy was attained was chosen to be the initial slice for the propagation process. Evaluation was performed for 336 transverse images from 15 prostates that include images acquired at mid-gland, base and apex regions of the prostates. The average mean absolute difference (MAD) between algorithm and manual segmentations was 0.79±0.26 mm, which is comparable to results produced by previously published semi-automatic segmentation methods. Statistical evaluation shows that accurate segmentation was not only obtained at the mid-gland, but also at the base and apex regions. Abstract : Highlights: An automated and efficient prostate boundary initialization technique is proposed. The 2D slice-based technique segmented prostate accurately from base to apex. The initial slice where 2D slice-based propagation began was objectively determined. Validation on 336 images shows accuracy comparable to semi-automatic methods.