Application of Image Segmentation on Reconstructed Images (original) (raw)
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This paper is on detecting brain tumors using MRI images, and obtaining a 3D model of the detected tumor. With the developed software, image segmentation algorithms were applied to MRI images to separate tumor from healthy brain tissues. In the development phase, various image segmentation algorithms were tried, and high success rates were aimed. After obtaining an algorithm with a high success rate, a 3-dimensional image of the detected tumor will be generated using volume rendering. With this image, features of the tumor such as its location, shape and how it spreads in the brain can be observed.
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Automated Segmentation of MRI of Brain Tumors
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An automated brain tumor segmentation method was developed and validated against manual segmentation on 3D-MRI of 20 patients with meningiomas and low grade gliomas. The automated method allows the rapid identification (5-10 minutes operator time) of brain and tumor tissue with accuracy and reproducibility comparable to manual segmentation (3-5 hours operator time) making automated segmentation practical for low grade gliomas
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Volumetric Methods for 3D Brain Tumor MRI Segmentation: A Survey
The segmentation of brain tumors using MRI is a critical step in biomedical imaging. Timely detection of brain cancer can increase the life expectancy and post-treatment survival of patients. Brain tumor segmentation has traditionally been done manually by a skilled operator with clinical training. However, this takes a lot of time, and highly trained operators typically have intra-operator variability in their ratings. Additionally, the imaging of the various machines from various locations differs slightly, which affects the diagnosis results. To solve these problems, fully automated segmentation models are needed. Deep learning models have been increasingly popular in recent years, especially for applications involving medical imaging. These deep-learning models have exhibited state-of-the-art performance through self-learning features. In recent years the Brain Tumor Segmentation (BraTS) challenge has been the main source of research breakthroughs in brain tumor segmentation. At its initial stage, most brain tumor segmentation models focused on 2D MRI segmentation. Recently, deep learning-based MRI segmentation models successfully use patch-based three-dimensional (3D) segmentation networks. In this article, we will investigate the evolution of automatic brain tumor segmentation mainly using the most recent 3D segmentation models. We will evaluate the segmentation performance of each model with respect to older models by using the dice score similarity measure and Hausdorff’s distance.
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A Practical Approach to Automated Segmentation of Brain Tumours in MRI
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Expert brain tumor identification on multimodal Magnetic Resonance (MR) images is a very time-consuming process for medical experts. Therefore, throughout the last decade, significant effort has been invested in the development of novel approaches applying computer-aided techniques for brain tumor segmentation. Automated brain tumor segmentation aims to separate and label different tumor tissues, including: (1) active tumor cells, (2) necrotic core, and (3) edema from normal brain tissues of Gray Matter (GM), White Matter (WM), and Cerebrospinal fluid (CSF). Even though experts from the brain tumor research area can accurately characterize and identify brain tissue abnormalities, the automated process of tumor segmentation is not straightforward. Many accurate approaches are only evaluated on a single data type coming from a particular brain tumor type, and thus, being far away from a practical clinical application [1]. The aim of this work is to implement a software framework to ap...
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