Ovarian assessment using deep learning-based 3D ultrasound super resolution

Saumya Gupta; Venkata Suryanarayana K.; Srinivas Rao Kudavelly; G. A. Ramaraju

doi:10.1117/12.2581286

15 February 2021 Ovarian assessment using deep learning-based 3D ultrasound super resolution

Saumya Gupta, Venkata Suryanarayana K., Srinivas Rao Kudavelly, G. A. Ramaraju

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115970K (2021) https://doi.org/10.1117/12.2581286
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Saumya Gupta, Venkata Suryanarayana K., Srinivas Rao Kudavelly, and G. A. Ramaraju "Ovarian assessment using deep learning-based 3D ultrasound super resolution", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115970K (15 February 2021); https://doi.org/10.1117/12.2581286

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