  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.
