Volltext-Downloads (blau) und Frontdoor-Views (grau)

Slicer-DeepSeg: Open-source deep learning toolkit for brain tumour segmentation

  • Purpose 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 AI-assisted 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.

Download full text files

Export metadata

Additional Services

Search Google Scholar


Author of HS ReutlingenBurgert, Oliver; Weimann, Pauline; Zeineldin, Ramy
Erschienen in:Current directions in biomedical engineering
Publisher:De Gruyter
Place of publication:Berlin
Document Type:Journal article
Publication year:2021
Tag:3D slicer; MRI; brain tumour segmentation; deep learning; image-guided neurosurgery
Page Number:5
Article Number:20211107
DDC classes:570 Biowissenschaften, Biologie
Open access?:Ja
Licence (German):License Logo  Creative Commons - CC BY - Namensnennung 4.0 International