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

Real-time computer assisted measurement of oocyte and embryo volume for assessment of transport parameters

Cryopreservation of gametes has revolutionized both animal agriculture and human reproductive medicine. Although many new technologies have tremendously improved the cryopreservation of oocytes and embryos, osmotic stress encountered during the equilibration process can cause their loss of function. Rational cryoprotective agent (CPA) equilibration strategies can be used to minimize this stress but require trained personnel to monitor the process in individual oocytes or embryos or require the use of suboptimal average transport parameter values in mathematically guided protocols. To enable individually optimized equilibration of CPAs in individual cells, here we establish experimental and computational techniques to track the osmotic behavior of individual bovine oocytes and embryos during CPA equilibration in real time. We designed a microfluidic device to provide a controlled flow of CPA and modified standard image analysis techniques to estimate real-time cell volume changes. In particular, we used a level-set method to define a boundary within a contour plot which could automate the image analysis process. A colour based level set algorithm coupled with contour smoothing not only provided the best fit but also reduced the segmentation time to well under a second per image. The accuracy of the automated method was comparable to human segmented images for both oocytes and embryos. This technology should enable both rapid evaluation of key biophysical parameters in oocytes and embryos undergoing CPA equilibration and the development of real-time feedback-control of CPA equilibration, enabling individual oocyte- and embryo-specific optimal protocols.

Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns

In this study, we propose a fully automatic algorithm to detect and segment corpora lutea (CL) using genetic programming and rotationally invariant local binary patterns. Detection and segmentation experiments were conducted and evaluated on 30 images containing a CL and 30 images with no CL. The detection algorithm correctly determined the presence or absence of a CL in 93.33 % of the images. The segmentation algorithm achieved a mean (±standard deviation) sensitivity and specificity of 0.8693 ± 0.1371 and 0.9136 ± 0.0503, respectively, over the 30 CL images. The mean root mean squared distance of the segmented boundary from the true boundary was 1.12 ± 0.463 mm and the mean maximum deviation (Hausdorff distance) was 3.39 ± 2.00 mm. The success of these algorithms demonstrates that similar algorithms designed for the analysis of in vivo human ovaries are likely viable.