Ultrasound volume projection image quality selection by ranking from convolutional RankNet

Juan Lyu, Sai Ho Ling, S. Banerjee, J. Y. Zheng, K. L. Lai, D. Yang, Y. P. Zheng, Xiaojun Bi, Steven Su, Uphar Chamoli

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Periodic inspection and assessment are important for scoliosis patients. 3D ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. With the generation of a 3D ultrasound volume projection spine image using our Scolioscan system, a series of 2D coronal ultrasound images are produced at different depths with different qualities. Selecting a high quality image from these 2D images is the crucial task for further scoliosis measurement. However, adjacent images are similar and difficult to distinguish. To learn the nuances between these images, we propose selecting the best image automatically, based on their quality rankings. Here, the ranking algorithm we use is a pairwise learning-to-ranking network, RankNet. Then, to extract more efficient features of input images and to improve the discriminative ability of the model, we adopt the convolutional neural network as the backbone due to its high power of image exploration. Finally, by inputting the images in pairs into the proposed convolutional RankNet, we can select the best images from each case based on the output ranking orders. The experimental result shows that convolutional RankNet achieves better than 95.5% top-3 accuracy, and we prove that this performance is beyond the experience of a human expert.

Original languageEnglish
Article number101847
JournalComputerized Medical Imaging and Graphics
Volume89
DOIs
Publication statusPublished - Apr 2021

Keywords

  • 3D ultrasound imaging
  • Convolutional RankNet
  • Image selection
  • Scoliosis

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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