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

  • Jun Liu
  • PengFei Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

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.

Keywords

Image segmentation Deep learning Mask R-CNN Ultrasound image Machine learning 

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China (Grant No.31201121, No.61373109 and No.61403287), the Natural Science Foundation of Hubei Province (Grant No.2014CFB288) and Open foundation of Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System (Grant Nos.ZNSS2013A0001 and ZNSS2013A004).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial SystemWuhanChina

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