27 February 2018 Automated detection and segmentation of follicles in 3D ultrasound for assisted reproduction
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Abstract
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.
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Nikhil S. Narayan, Srinivasan Sivanandan, Srinivas Kudavelly, Kedar A. Patwardhan, 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 (27 February 2018); doi: 10.1117/12.2293121; https://doi.org/10.1117/12.2293121
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