Abstract
Abstract 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 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.
Highlights
Treatment of malignant brain tumour is still among the most difficult challenges in cancer remedies [1]
There are several commercial image analysis software platforms, these proprietary systems are typically built for specific applications and, due to their restrictive licenses, lack flexibility and extensibility, which are two main factors in developing cancer research toolkits
Before the tumour boundaries are automatically segmented using our Slicer-DeepSeg toolkit, a preprocessing stage is essential since magnetic resonance imaging (MRI) data are acquired using different clinical protocols, come from different MRI scanners, and multiple institutions
Summary
Treatment of malignant brain tumour is still among the most difficult challenges in cancer remedies [1]. There are several commercial image analysis software platforms, these proprietary systems are typically built for specific applications and, due to their restrictive licenses, lack flexibility and extensibility, which are two main factors in developing cancer research toolkits. Some initiatives have been launched for open-source medical research toolkits, such as 3D Slicer [4], MITK [5], ITK-Snap [6], and NifTK [7]. These software programs provide manual or semiautomated tumour segmentation for neurosurgical planning and this process is associated with large processing time
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