scholarly journals Slicer-DeepSeg: Open-Source Deep Learning Toolkit for Brain Tumour Segmentation

2021 ◽  
Vol 7 (1) ◽  
pp. 30-34
Author(s):  
Ramy A. Zeineldin ◽  
Pauline Weimann ◽  
Mohamed E. Karar ◽  
Franziska Mathis-Ullrich ◽  
Oliver Burgert
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Brain Tumour ◽  
Brain Research ◽  
3D Visualization ◽  
High Grade Glioma ◽  
3D Slicer ◽  
Tumour Segmentation ◽  
Multiple Data ◽  
Brain Tumour Segmentation

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.

A Review on Deep Learning Architecture and Methods for MRI Brain Tumour Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Angulakshmi M ◽  
Deepa M
Keyword(s):  
Deep Learning ◽  
Brain Tumour ◽  
Automatic Segmentation ◽  
Learning Methods ◽  
Domain Specific ◽  
Advantages And Disadvantages ◽  
Mri Brain ◽  
Tumour Segmentation ◽  
Learning Techniques ◽  
Brain Tumour Segmentation

Background: The automatic segmentation of brain tumour from MRI medical images is mainly covered in this review. Recently, state-of-the-art performance is provided by deep learning-based approaches in the field of image classification, segmentation, object detection, and tracking tasks. Introduction: The core feature deep learning approach is the hierarchical representation of features from images and thus avoiding domain-specific handcrafted features. Methods: In this review paper, we have dealt with a Review of Deep Learning Architecture and Methods for MRI Brain Tumour Segmentation. First, we have discussed basic architecture and approaches for deep learning methods. Secondly, we have discussed the literature survey of MRI brain tumour segmentation using deep learning methods and its multimodality fusion. Then, the advantages and disadvantages of each method analyzed and finally concluded the discussion with the merits and challenges of deep learning techniques. Results and Conclusion: The review of brain tumour identification using deep learning Techniques may help the research to have a better focus on it.

scholarly journals Segmentation of Brain Images by Optimizing Clustering of Convolution Based Features

2021 ◽  
Vol 229 ◽  
pp. 01034
Author(s):  
Vikas Kumar
Keyword(s):  
Brain Tumour ◽  
Brain Tumours ◽  
Normal Brain ◽  
Resonance Imaging ◽  
Comprehensive Overview ◽  
Brain Images ◽  
Non Invasive ◽  
Tumour Segmentation ◽  
Tissue Contrast ◽  
Brain Tumour Segmentation

Brain tumour segmentation aims to separate the various types of tumour tissues like active cells, necrotic core, and edema from normal brain tissues of substantia alba (WM), grey matter (GM), and spinal fluid (CSF). Magnetic Resonance Imaging based brain tumour segmentation studies are attracting more and more attention in recent years thanks to non-invasive imaging and good soft tissue contrast of resonance Imaging (MRI) images. With the event of just about two decades, the ingenious approaches applying computer-aided techniques for segmenting brain tumour are getting more and more mature and coming closer to routine clinical applications. the aim of this paper is to supply a comprehensive overview for MRIbased brain tumour segmentation methods. Firstly, a quick introduction to brain tumours and imaging modalities of brain tumours is given in this proposed research, convolution based optimization. These stepwise step refine the segmentation and improve the classification parameter with the assistance of particle swarmoptimization.

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