How ultrasound technologies have expanded and revolutionized research in reproduction in large animals

Gray-scale ultrasonic imaging (UI) was introduced in 1980 and initially was used to examine clinically the reproductive tract of mares. By 1983 in mares and 1984 in heifers/cows, UI had become a tool for basic research. In each species, transrectal gray-scale UI has been used extensively to characterize follicle dynamics and investigate the gonadotropic control and hormonal role of the follicles. However, the use of transrectal UI has also disclosed and characterized many other aspects of reproduction in each species, including (1) endometrial echotexture as a biological indicator of circulating estradiol concentrations, (2) relative location of the genital tubercle for fetal gender diagnosis by Days 50 to 60, and (3) timing of follicle evacuation during ovulation. Discoveries in mares include (1) embryo mobility wherein the spherical conceptus (6-16 mm) travels to all parts of the uterus on Days 11 to 15, (2) how one embryo of a twin set eliminates the other without self-inflicted damage, and (3) serration of the granulosum of the preovulatory follicle opposite to the future rupture site as an indicator of imminent ovulation. Studies with color-Doppler UI have shown that vascular perfusion of the endometrium follows the equine embryo back and forth between uterine horns and follows the expansion of the bovine allantochorion throughout each horn. In heifers, blood flow in the CL increases during the ascending portion of an individual pulse of PGF2α metabolite and then decreases. These examples highlight the power of UI in reproduction research. Without UI, it is likely that these and many other findings would still be unknown.

Level set segmentation of bovine corpora lutea in ex situ ovarian ultrasound images

BACKGROUND

The objective of this study was to investigate the viability of level set image segmentation methods for the detection of corpora lutea (corpus luteum, CL) boundaries in ultrasonographic ovarian images. It was hypothesized that bovine CL boundaries could be located within 1-2 mm by a level set image segmentation methodology.

METHODS

Level set methods embed a 2D contour in a 3D surface and evolve that surface over time according to an image-dependent speed function. A speed function suitable for segmentation of CL's in ovarian ultrasound images was developed. An initial contour was manually placed and contour evolution was allowed to proceed until the rate of change of the area was sufficiently small. The method was tested on ovarian ultrasonographic images (n = 8) obtained ex situ. A expert in ovarian ultrasound interpretation delineated CL boundaries manually to serve as a "ground truth". Accuracy of the level set segmentation algorithm was determined by comparing semi-automatically determined contours with ground truth contours using the mean absolute difference (MAD), root mean squared difference (RMSD), Hausdorff distance (HD), sensitivity, and specificity metrics.

RESULTS AND DISCUSSION

The mean MAD was 0.87 mm (sigma = 0.36 mm), RMSD was 1.1 mm (sigma = 0.47 mm), and HD was 3.4 mm (sigma = 2.0 mm) indicating that, on average, boundaries were accurate within 1-2 mm, however, deviations in excess of 3 mm from the ground truth were observed indicating under- or over-expansion of the contour. Mean sensitivity and specificity were 0.814 (sigma = 0.171) and 0.990 (sigma = 0.00786), respectively, indicating that CLs were consistently undersegmented but rarely did the contour interior include pixels that were judged by the human expert not to be part of the CL. It was observed that in localities where gradient magnitudes within the CL were strong due to high contrast speckle, contour expansion stopped too early.

CONCLUSION

The hypothesis that level set segmentation can be accurate to within 1-2 mm on average was supported, although there can be some greater deviation. The method was robust to boundary leakage as evidenced by the high specificity. It was concluded that the technique is promising and that a suitable data set of human ovarian images should be obtained to conduct further studies.