Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Slicer-DeepSeg: Open-Source Deep Learning Toolkit for Brain Tumour Segmentation

Slicer-DeepSeg: Open-Source Deep Learning Toolkit for Brain Tumour Segmentation AbstractPurpose Computerized medical imaging processing assists neurosurgeons to localize tumours precisely. It plays a key role in recent image-guided neurosurgery. Hence, we developed a new open-source toolkit, namely Slicer-DeepSeg, for efficient and automatic brain tumour segmentation based on deep learning methodologies for aiding clinical brain research. Methods Our developed toolkit consists of three main components. First, Slicer-DeepSeg extends the 3D Slicer application and thus provides support for multiple data input/ output data formats and 3D visualization libraries. Second, Slicer core modules offer powerful image processing and analysis utilities. Third, the Slicer-DeepSeg extension provides a customized GUI for brain tumour segmentation using deep learning-based methods. Results The developed Slicer- DeepSeg was validated using a public dataset of high-grade glioma patients. The results showed that our proposed platform’s performance considerably outperforms other 3D Slicer cloud-based approaches. Conclusions Developed Slicer-DeepSeg allows the development of novel AIassisted medical applications in neurosurgery. Moreover, it can enhance the outcomes of computer-aided diagnosis of brain tumours. Open-source Slicer-DeepSeg is available at github.com/razeineldin/Slicer-DeepSeg. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Current Directions in Biomedical Engineering de Gruyter

Slicer-DeepSeg: Open-Source Deep Learning Toolkit for Brain Tumour Segmentation

Download PDF
5 pages

Loading next page...
 
/lp/de-gruyter/slicer-deepseg-open-source-deep-learning-toolkit-for-brain-tumour-5zBmSAExPL
Publisher
de Gruyter
Copyright
© 2021 by Walter de Gruyter Berlin/Boston
eISSN
2364-5504
DOI
10.1515/cdbme-2021-1007
Publisher site
See Article on Publisher Site

Abstract

AbstractPurpose Computerized medical imaging processing assists neurosurgeons to localize tumours precisely. It plays a key role in recent image-guided neurosurgery. Hence, we developed a new open-source toolkit, namely Slicer-DeepSeg, for efficient and automatic brain tumour segmentation based on deep learning methodologies for aiding clinical brain research. Methods Our developed toolkit consists of three main components. First, Slicer-DeepSeg extends the 3D Slicer application and thus provides support for multiple data input/ output data formats and 3D visualization libraries. Second, Slicer core modules offer powerful image processing and analysis utilities. Third, the Slicer-DeepSeg extension provides a customized GUI for brain tumour segmentation using deep learning-based methods. Results The developed Slicer- DeepSeg was validated using a public dataset of high-grade glioma patients. The results showed that our proposed platform’s performance considerably outperforms other 3D Slicer cloud-based approaches. Conclusions Developed Slicer-DeepSeg allows the development of novel AIassisted medical applications in neurosurgery. Moreover, it can enhance the outcomes of computer-aided diagnosis of brain tumours. Open-source Slicer-DeepSeg is available at github.com/razeineldin/Slicer-DeepSeg.

Journal

Current Directions in Biomedical Engineering – de Gruyter

Published: Aug 1, 2021

Keywords: 3D Slicer; Brain tumour segmentation; Deep learning; Image-guided neurosurgery; MRI.

There are no references for this article.