Boundary Delineation of MRI Images for Lumbar Spinal Stenosis Detection Through Semantic Segmentation Using Deep Neural Networks

@article{AlKafri2019BoundaryDO,
  title={Boundary Delineation of MRI Images for Lumbar Spinal Stenosis Detection Through Semantic Segmentation Using Deep Neural Networks},
  author={Ala S. Al-Kafri and Sud Sudirman and Abir Jaafar Hussain and Dhiya Al-Jumeily and Friska Natalia and Hira Meidia and Nunik Afriliana and Wasfi Al-Rashdan and Mohammad Bashtawi and Mohammed Al-Jumaily},
  journal={IEEE Access},
  year={2019},
  volume={7},
  pages={43487-43501}
}
We propose a methodology to aid clinicians in performing lumbar spinal stenosis detection through semantic segmentation and delineation of magnetic resonance imaging (MRI) scans of the lumbar spine using deep learning. Our dataset contains MRI studies of 515 patients with symptomatic back pains. Each study is annotated by expert radiologists with notes regarding the observed characteristics and condition of the lumbar spine. We have developed a ground truth dataset, containing image labels of… CONTINUE READING

Citations

Publications citing this paper.

References

Publications referenced by this paper.
SHOWING 1-10 OF 44 REFERENCES

Development of Ground Truth Data for Automatic Lumbar Spine MRI Image Segmentation

  • 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
  • 2018
VIEW 2 EXCERPTS

Similar Papers

Loading similar papers…