Deep Learning based Quantification of Ovary and Follicles using 3D Transvaginal Ultrasound in Assisted Reproduction

Quantification of ovarian and follicular volume and follicle count are performed in clinical practice for diagnosis and management in assisted reproduction. Ovarian volume and Antral Follicle Count (AFC) are typically tracked over the ovulation cycle. Volumetric analysis of ovary and follicle is manual and largely operator dependent. In this manuscript, we have proposed a deep-learning method for automatic simultaneous segmentation of ovary and follicles in 3D Transvaginal Ultrasound (TVUS), namely S-Net. The proposed loss function restricts false detection of follicles outside the ovary. Additionally, we have used multi-layered loss to provide deep supervision for training the network. S-Net is optimized for inference time and memory while utilizing 3D context in the 2D deep-learning network. 66 3D TVUS volumes (13,200 2D image slices) were acquired from 66 subjects in this Institutional Review Board (IRB) approved study. The segmentation framework provides approximately 92% and 87% average DICE overlap with the ground truth annotations for ovary and follicles, respectively. We have obtained state-of-the-art results with a detection rate of 88%, 91% and 98% for follicles of size 2-4mm, 4-12mm and >12mm.