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Proceedings Paper

Automated detection and segmentation of follicles in 3D ultrasound for assisted reproduction
Author(s): Nikhil S. Narayan; Srinivasan Sivanandan; Srinivas Kudavelly; Kedar A. Patwardhan; G. A. Ramaraju
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Paper 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.

Paper Details

Date Published: 27 February 2018
PDF: 6 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751W (27 February 2018); doi: 10.1117/12.2293121
Show Author Affiliations
Nikhil S. Narayan, Samsung R&D Institute India - Bangalore (India)
Srinivasan Sivanandan, Samsung R&D Institute India - Bangalore (India)
Srinivas Kudavelly, Samsung R&D Institute India - Bangalore (India)
Kedar A. Patwardhan, Samsung R&D Institute India - Bangalore (India)
G. A. Ramaraju, Krishna IVF Clinic (India)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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