  Accurate segmentation for medical images is important for clinical diagnosis.
Existing automatic segmentation methods are mainly based on fully supervised
learning and have an extremely high demand for precise annotations, which are
very costly and time-consuming to obtain. To address this problem, we proposed
an automatic CT segmentation method based on weakly supervised learning, by
which one could train an accurate segmentation model only with weak annotations
in the form of bounding boxes. The proposed method is composed of two steps: 1)
generating pseudo masks with bounding box annotations by k-means clustering,
and 2) iteratively training a 3D U-Net convolutional neural network as a
segmentation model. Some data pre-processing methods are used to improve
performance. The method was validated on four datasets containing three types
of organs with a total of 627 CT volumes. For liver, spleen and kidney
segmentation, it achieved an accuracy of 95.19%, 92.11%, and 91.45%,
respectively. Experimental results demonstrate that our method is accurate,
efficient, and suitable for clinical use.
