Fast Segmentation of Ovarian Ultrasound Volumes Using Support Vector Machines and Sparse Learning Sets

Ovarian ultrasound imaging has recently drawn attention because of the improved ultrasound-based diagnostic methods and because of its application to in-vitro fertilisation and prediction of women's fertility. Modem ultrasound devices enable frequent examinations and sophisticated built-in image processing options. However, precise detection of different ovarian structures, in particular follicles and their growth still need additional, mainly off-line processing with highly specialised algorithms. Manual annotation of a whole 3D ultrasound volume consisting of 100 and more slices, i.e. 2D ultrasound images, is a tedious task even when using handy, computer-assisted segmentation tools. Our paper reveals how all application of support vector machines (SVM) can ease the follicle detection by speeding up the learning and annotation processes at the same time. An iterative SVM approach is introduced using training oil sparse learning sets only. The recognised follicles are compared to the referential expert readings and to the results obtained after learning oil the entire annotated 3D ovarian volume.