Three‐dimensional prostate segmentation using level set with shape constraint based on rotational slices for 3D end‐firing TRUS guided biopsy

PURPOSE: Prostate segmentation is an important step in the planning and treatment of 3D end-firing transrectal ultrasound (TRUS) guided prostate biopsy. In order to improve the accuracy and efficiency of prostate segmentation in 3D TRUS images, an improved level set method is incorporated into a rotational-slice-based 3D prostate segmentation to decrease the accumulated segmentation errors produced by the slice-by-slice segmentation method. METHODS: A 3D image is first resliced into 2D slices in a rotational manner in both the clockwise and counterclockwise directions. All slices intersect approximately along the rotational scanning axis and have an equal angular spacing. Six to eight boundary points are selected to initialize a level set function to extract the prostate contour within the first slice. The segmented contour is then propagated to the adjacent slice and is used as the initial contour for segmentation. This process is repeated until all slices are segmented. A modified distance regularization level set method is used to segment the prostate in all resliced 2D slices. In addition, shape-constraint and local-region-based energies are imposed to discourage the evolved level set function to leak in regions with weak edges or without edges. An anchor point based energy is used to promote the level set function to pass through the initial selected boundary points. The algorithm's performance was evaluated using distance- and volume-based metrics (sensitivity (Se), Dice similarity coefficient (DSC), mean absolute surface distance (MAD), maximum absolute surface distance (MAXD), and volume difference) by comparison with expert delineations. RESULTS: The validation results using thirty 3D patient images showed that the authors' method can obtain a DSC of 93.1% ± 1.6%, a sensitivity of 93.0% ± 2.0%, a MAD of 1.18 ± 0.36 mm, a MAXD of 3.44 ± 0.8 mm, and a volume difference of 2.6 ± 1.9 cm(3) for the entire prostate. A reproducibility experiment demonstrated that the proposed method yielded low intraobserver and interobserver variability in terms of DSC. The mean segmentation time of the authors' method for all patient 3D TRUS images was 55 ± 3.5 s, in addition to 30 ± 5 s for initialization. CONCLUSIONS: To address the challenges involved with slice-based 3D prostate segmentation, a level set based method is proposed in this paper. This method is especially developed for a 3D end-firing TRUS guided prostate biopsy system. The extensive experimental results demonstrate that the proposed method is accurate, robust, and computationally efficient.

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