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.

中文翻译：

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CR-Unet：超声图像中卵巢和卵泡分割的复合网络。

经阴道超声（TVUS）被广泛用于不孕症治疗。超声医师必须手动测量卵巢和卵泡的大小和形状以评估其生理状态。但是，此过程非常耗时且取决于操作员。在这项研究中，我们提出了一种新颖的复合网络，即CR-Unet，以同时分割TVUS的卵巢和卵泡。CR-Unet将空间递归神经网络（RNN）合并到普通的U-Net中。它可以有效地学习多尺度和远距离的空间环境，以应对此任务的挑战，例如图像质量差，对比度低，边界模糊和复杂的解剖形状。我们进一步采用深度监督策略，使模型训练更加有效。此外，使用自我监督来迭代地细化分割结果。在219位患者的3204张TVUS图像上进行的实验表明，与其他最新技术相比，该方法对卵巢和卵泡的分割效果最佳，骰子相似系数（DSC）分别为0.912和0.858。