On-line segmentation of the uterus can aid effective image-based guidance for precise delivery of dose to the target tissue (the uterocervix) during cervix cancer radiotherapy. 3D ultrasound (US) can be used to image the uterus, however, finding the position of uterine boundary in US images is a challenging task due to large daily positional and shape changes in the uterus, large variation in bladder filling, and the limitations of 3D US images such as low resolution in the elevational direction and imaging aberrations. Previous studies on uterus segmentation mainly focused on developing semi-automatic algorithms where require manual initialization to be done by an expert clinician. Due to limited studies on the automatic 3D uterus segmentation, the aim of the current study was to overcome the need for manual initialization in the semi-automatic algorithms using the recent deep learning-based algorithms. Therefore, we developed 2D UNet-based networks that are trained based on two scenarios. In the first scenario, we trained 3 different networks on each plane (i.e., sagittal, coronal, axial) individually. In the second scenario, our proposed network was trained using all the planes of each 3D volume. Our proposed schematic can overcome the initial manual selection of previous semi-automatic algorithm.

翻译：子宫的线上分解可以帮助在子宫癌放射治疗期间向目标组织(子宫颈癌)准确地提供剂量的有效图像指导。 3D超声波(美国)可以用来图像子宫,然而,在美国图像中找到子宫边界的位置是一项艰巨的任务,因为子宫的日位置和形状变化很大,膀胱填充方面差异很大,美国3D图像的局限性,如海拔方向的低分辨率和成像偏差。以前关于子宫分解的研究主要侧重于开发半自动算法,需要专家临床专家手工初始化。由于对自动3D子宫分解的研究有限,目前研究的目的是利用最近的深层次学习算法克服半自动算法的手工初始初始初始初始初始初始化需求。因此,我们开发了基于两种情景培训的基于2DUNet的网络。在第一个情景中,我们培训了每平面的3个不同的网络(i.e.agital 3-diral), 使用我们所培训的单机的初始式双向轨道。