Automatic ovarian follicle quantification from 3D ultrasound data using global/local context with database guided segmentation

In this paper, we present a novel probabilistic framework for automatic follicle quantification in 3D ultrasound data. The proposed framework robustly estimates size and location of each individual ovarian follicle by fusing the information from both global and local context. Follicle candidates at detected locations are then segmented by a novel database guided segmentation method. To efficiently search hypothesis in a high dimensional space for multiple object detection, a clustered marginal space learning approach is introduced. Extensive evaluations conducted on 501 volumes containing 8108 follicles showed that our method is able to detect and segment ovarian follicles with high robustness and accuracy. It is also much faster than the current ultrasound manual workflow. The proposed method is able to streamline the clinical workflow and improve the accuracy of existing follicular measurements.