Follicle quantification refers to the computation of the number and size of follicles in 3D ultrasound volumes of the
ovary. This is one of the key factors in determining hormonal dosage during female infertility treatments. In this paper,
we propose an automated algorithm to detect and segment follicles in 3D ultrasound volumes of the ovary for
quantification. In a first of its kind attempt, we employ noise-robust phase symmetry feature maps as likelihood function
to perform mean-shift based follicle center detection. Max-flow algorithm is used for segmentation and gray weighted
distance transform is employed for post-processing the results. We have obtained state-of-the-art results with a true
positive detection rate of >90% on 26 3D volumes with 323 follicles.
Nikhil S. Narayan, Srinivasan Sivanandan, Srinivas Kudavelly, Kedar A. Patwardhan, and G. A. Ramaraju, "Automated detection and segmentation of follicles in 3D ultrasound for assisted reproduction," Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751W (Presented at SPIE Medical Imaging: February 15, 2018; Published: 27 February 2018); https://doi.org/10.1117/12.2293121.
Conference Presentations are recordings of oral presentations given at SPIE conferences and published as part of the conference proceedings. They include the speaker's narration along with a video recording of the presentation slides and animations. Many conference presentations also include full-text papers. Search and browse our growing collection of more than 14,000 conference presentations, including many plenary and keynote presentations.