TY - JOUR U1 - Wissenschaftlicher Artikel 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 -