Automatic Segmentation of Meniscus in Multispectral MRI Using Regions with Convolutional Neural Network (R-CNN)

Olmez E., Akdogan V., KORKMAZ M., Er O.

JOURNAL OF DIGITAL IMAGING, vol.33, no.4, pp.916-929, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 33 Issue: 4
  • Publication Date: 2020
  • Doi Number: 10.1007/s10278-020-00329-x
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, Applied Science & Technology Source, Biotechnology Research Abstracts, CINAHL, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE
  • Page Numbers: pp.916-929
  • Yozgat Bozok University Affiliated: Yes


The meniscus has a significant function in human anatomy, and Magnetic Resonance Imaging (MRI) has an essential role in meniscus examination. Due to a variety of MRI data, it is excessively difficult to segment the meniscus with image processing methods. An MRI data sequence contains multiple images, and the region features we are looking for may vary from each image in the sequence. Therefore, feature extraction becomes more difficult, and hence, explicitly programming for segmentation becomes more difficult. Convolutional Neural Network (CNN) extracts features directly from images and thus eliminates the need for manual feature extraction. Regions with Convolutional Neural Network (R-CNN) allow us to use CNN features in object detection problems by combining CNN features with Region Proposals. In this study, we designed and trained an R-CNN for detecting meniscus region in MRI data sequence. We used transfer learning for training R-CNN with a small amount of meniscus data. After detection of the meniscus region by R-CNN, we segmented meniscus by morphological image analysis using two different MRI sequences. Automatic detection of the meniscus region with R-CNN made the meniscus segmentation process easier, and the use of different contrast features of two different image sequences allowed us to differentiate the meniscus from its surroundings.