Conventional ultrasonographic examination of the bovine ovary is based on a sequence of two-dimensional (2D) cross-section images. Day-to-day estimation of the number, size, shape and position of the ovarian follicles is one of the most important aspects of ovarian research. Computer-assisted follicle segmentation of ovarian volume can relieve physicians from the tedious manual detection of follicles, provide objective assessment of spatial relationships between the ovarian structures and therefore has the potential to improve accuracy. Modern segmentation procedures are performed on 2D images and the three-dimensional (3D) visualization of follicles is obtained from the reconstruction of a sequence of 2D segmented follicles. <p>The objective of this study was to develop a semi-automatic 3D follicle segmentation method based on seeded region growing. The 3D datasets were acquired from a sequence of 2D ultrasound images and the ovarian structures were segmented from the reconstructed ovarian volume in a single step. A seed is placed manually in each follicle and the growth of the seed is controlled by the algorithm using a combination of average grey-level, standard deviation of the intensity, newly-developed volumetric comparison test and a termination criterion. One important contribution of this algorithm is that it overcomes the boundary leakage problem of follicles of conventional 2D segmentation procedures. The results were validated against the aspiration volume of follicles, the manually detected follicles by an expert and an existing algorithm.<p>We anticipate that this algorithm will enhance follicular assessment based on current ultrasound techniques in cases when large numbers of follicles (e.g. ovarian superstimulation) obviate accurate counting and size measurement.

Conventional ultrasonographic examination of the bovine ovary is based on a sequence of two-dimensional (2D) cross-section images. Day-to-day estimation of the number, size, shape and position of the ovarian follicles is one of the most important aspects of ovarian research. Computer-assisted follicle segmentation of ovarian volume can relieve physicians from the tedious manual detection of follicles, provide objective assessment of spatial relationships between the ovarian structures and therefore has the potential to improve accuracy. Modern segmentation procedures are performed on 2D images and the three-dimensional (3D) visualization of follicles is obtained from the reconstruction of a sequence of 2D segmented follicles. <p>The objective of this study was to develop a semi-automatic 3D follicle segmentation method based on seeded region growing. The 3D datasets were acquired from a sequence of 2D ultrasound images and the ovarian structures were segmented from the reconstructed ovarian volume in a single step. A seed is placed manually in each follicle and the growth of the seed is controlled by the algorithm using a combination of average grey-level, standard deviation of the intensity, newly-developed volumetric comparison test and a termination criterion. One important contribution of this algorithm is that it overcomes the boundary leakage problem of follicles of conventional 2D segmentation procedures. The results were validated against the aspiration volume of follicles, the manually detected follicles by an expert and an existing algorithm.<p>We anticipate that this algorithm will enhance follicular assessment based on current ultrasound techniques in cases when large numbers of follicles (e.g. ovarian superstimulation) obviate accurate counting and size measurement.