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 journalArticlepeer-review

17 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

Bibliographical note

Funding Information:
Funding: This research was supported in parts by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (2016R1A2B4015016) in support of Dosik Hwang, and National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health in support of Won C. Bae (Grant Number R01 AR066622). The contents of this paper are the sole responsibility of the authors and do not necessarily represent the official views of the sponsoring institutions.

Publisher Copyright:
© 2018 by the authors.

All Science Journal Classification (ASJC) codes

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

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