Fine-grain segmentation of the intervertebral discs from MR spine images using deep convolutional neural networks: BSU-Net

Sewon Kim, Won C. Bae, Koichi Masuda, Christine B. Chung, Dosik Hwang

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

We propose a new deep learning network capable of successfully segmenting intervertebral discs and their complex boundaries from magnetic resonance (MR) spine images. The existing U-network (U-net) is known to perform well in various segmentation tasks in medical images; however, its performance with respect to details of segmentation such as boundaries is limited by the structural limitations of a max-pooling layer that plays a key role in feature extraction process in the U-net. We designed a modified convolutional and pooling layer scheme and applied a cascaded learning method to overcome these structural limitations of the max-pooling layer of a conventional U-net. The proposed network achieved 3% higher Dice similarity coefficient (DSC) than conventional U-net for intervertebral disc segmentation (89.44% vs. 86.44%, respectively; p < 0.001). For intervertebral disc boundary segmentation, the proposed network achieved 10.46% higher DSC than conventional U-net (54.62% vs. 44.16%, respectively; p < 0.001).

Original languageEnglish
Article number1656
JournalApplied Sciences (Switzerland)
Volume8
Issue number9
DOIs
Publication statusPublished - 2018 Sep 14

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Fingerprint Dive into the research topics of 'Fine-grain segmentation of the intervertebral discs from MR spine images using deep convolutional neural networks: BSU-Net'. Together they form a unique fingerprint.

  • Cite this