A Mask R-CNN Model with Improved Region Proposal Network for Medical Ultrasound Image

Abstract

In medical ultrasound image processing, it is often necessary to select the ROI before segmentation to obtain better segmentation accuracy. With the development of deep learning, the technology of object detection can well implement the function of automatically selecting ROI. The combination of object detection and image segmentation has also been proposed, such as Mask R-CNN, an end-to-end image segmentation model. However, the ROI selection by the algorithm above cannot meet the needs of medical image segmentation. Because its RPN layer is inherited from Faster R-CNN, a target classification framework. What we need is a region that can cover the whole object area with the details of edge. This information has an important influence for the further segmentation. Therefore, this paper improves the selection criteria of the anchor in the RPN layer, making the improved RPN layer more suitable for image segmentation tasks. Finally, the experimental results show that the improved model can achieve higher segmentation accuracy with the appropriate parameters selected.