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.
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.
The developed Slicer-DeepSeg was validated using a public dataset of high-grade glioma patients. The results showed that our proposed platform considerably outperforms other 3D Slicer cloud-based approaches.
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.
First, download the 3D Slicer from here. Select the version the corresponds to your operating system.
Second, click on the "Install Slicer Extensions" button, and choosing Slicer-DeepSeg to be downloaded.
[2022_01_01] Update: The Slicer-DeepSeg has not been added to the official 3D Slicer extension repository yet. However, it can be manually installed in the developper mode according to the following instructions. Please follow the first 10 slides, but choose Select Extension instead of Create Extension as ilustrated in the tutorial.
In order to demonstrate the capabilities of using Slicer-DeepSeg extension with 3D Slicer for addressing brain cancer research problems, a sample high-grade glioma (HGG) case from the BraTS 2020 dataset was employed.
Slicer-DeepSeg can be selected from the machine learning category in the modules list in 3D Slicer. The default parameter settings include two different pre-trained deep learning models based on the input MRI image modalities. The first model is our previous work, DeepSeg which requires only the T2-FLAIR MRI data as an input and automatically predicts the tumour region. The second model is the winning approach in the segmentation task of MICCAI BraTS 2020 challenge, nnU-Net, which requires the four MRI modalities like the BraTS challenge: FLAIR, T1, T1ce, and T2.
After the Slicer-DeepSeg installation, the user can choose one model, specifies its input data, creates a new segmentation volume, and presses the “apply” button. Then, an automatic pre-processing stage, including resampling, cropping and registration, is applied before the resultant tumour region is predicted using the specified pre-trained deep neural networks. Finally, the segmented tumour is displayed in both Slicer 2- and 3D scenes as presented in the following Fig:
Visualization of the brain tumour boundaries in MRI using Slicer-DeepSeg extension.
This project is licensed under the MIT License - see the LICENSE.txt file for details
The work has been published in the Current Directions in Biomedical Engineering, after the presentation in the German Society for Computer and Robot-Assisted Surgery (CURAC 2021). If you find this extension usefull, feel free to use it (or part of it) in your project and please cite the following paper:
@article{ZeineldinWeimannKararMathisUllrichBurgert+2021+30+34,
author = {Ramy A. Zeineldin and Pauline Weimann and Mohamed E. Karar and Franziska Mathis-Ullrich and Oliver Burgert},
doi = {doi:10.1515/cdbme-2021-1007},
url = {https://doi.org/10.1515/cdbme-2021-1007},
title = {Slicer-DeepSeg: Open-Source Deep Learning Toolkit for Brain Tumour Segmentation},
journal = {Current Directions in Biomedical Engineering},
number = {1},
volume = {7},
year = {2021},
pages = {30--34}
}
Slicer-DeepSeg, like 3D Slicer, is for research purposes and not intended for clinical use. Therefore, The user assumes full responsibility to comply with the appropriate regulations.