Ovarian volume assessment is the measurement of the size of ovaries during an Ultrasound (US) in order to estimate the ovarian reserve. Since the ovarian reserve is used in calculating a woman’s reproductive age and is also a diagnostic criterion for polycystic ovary syndrome (PCOS), it is imperative that it is measured accurately. Furthermore, ovarian rendering has clinical significance in terms of assessing ovarian anomalies (ovarian surface epithelial cells). Thus if the spacing in the US volume is high along one direction, reducing the spacing would greatly help in both the accurate measurement of the ovarian volume as well as surface assessment. In this paper, we aim to address this problem by developing a deep learning method for super-resolving 3D US data along the axial direction. On the collected dataset, our method has achieved high PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) values, and has also resulted in a 54% improvement in ovarian volume computation accuracy. Furthermore, our solution has improved the quality of the 3D rendering of the ovary, and has also reduced the problem of fused follicles in segmentation. This proves the viability of our approach for clinical diagnostic assessment.
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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