Automatic CT Segmentation from Bounding Box Annotations using Convolutional Neural Networks
This reduces annotation costs for medical CT segmentation, making it more efficient for clinical use, though it is incremental as it builds on existing weakly supervised techniques.
The paper tackled the problem of costly precise annotations for medical image segmentation by proposing a weakly supervised method using bounding boxes, achieving accuracies of 95.19% for liver, 92.11% for spleen, and 91.45% for kidney segmentation on CT volumes.
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.