CR-Unet: A Composite Network for Ovary and Follicle Segmentation in Ultrasound Images

Abstract

Transvaginal ultrasound (TVUS) is widely used in infertility treatment. The size and shape of the ovary and follicles must be measured manually for assessing their physiological status by sonographers. However, this process is extremely time-consuming and operator-dependent. In this study, we propose a novel composite network, namely CR-Unet, to simultaneously segment the ovary and follicles in TVUS. The CR-Unet incorporates the spatial recurrent neural network (RNN) into a plain U-Net. It can effectively learn multi-scale and long-range spatial contexts to combat the challenges of this task, such as the poor image quality, low contrast, boundary ambiguity, and complex anatomy shapes. We further adopt deep supervision strategy to make model training more effective and efficient. In addition, self-supervision is employed to iteratively refine the segmentation results. Experiments on 3204 TVUS images from 219 patients demonstrate the proposed method achieved the best segmentation performance compared to other state-of-the-art methods for both the ovary and follicles, with a Dice Similarity Coefficient (DSC) of 0.912 and 0.858, respectively.