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Accurate segmentation for medical images is important for clinical diagnosis.Existing automatic segmentation methods are mainly based on fully supervisedlearning and have an extremely high demand for precise annotations, which arevery costly and time-consuming to obtain. To address this problem, we proposedan automatic CT segmentation method based on weakly supervised learning, bywhich one could train an accurate segmentation model only with weak annotationsin 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 asegmentation model. Some data pre-processing methods are used to improveperformance. The method was validated on four datasets containing three typesof organs with a total of 627 CT volumes. For liver, spleen and kidneysegmentation, 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.

Automatic CT Segmentation from Bounding Box Annotations using Convolutional Neural Networks