TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Zeineldin, Ramy
A1  - Weimann, Pauline
A1  - Karar, Mohamed
A1  - Mathis-Ullrich, Franziska
A1  - Burgert, Oliver
T1  - Slicer-DeepSeg: Open-source deep learning toolkit for brain tumour segmentation
JF  - Current directions in biomedical engineering
N2  - 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.
KW  - 3D slicer
KW  - brain tumour segmentation
KW  - deep learning
KW  - image-guided neurosurgery
KW  - MRI
Y1  - 2021
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-31873
SN  - 2364-5504
SS  - 2364-5504
U6  - https://doi.org/10.1515/cdbme-2021-1007
DO  - https://doi.org/10.1515/cdbme-2021-1007
VL  - 7
IS  - 1
SP  - 5
S1  - 5
PB  - De Gruyter
CY  - Berlin
ER  -