Image classification-based brain tumour tissue segmentation

Alqazzaz, Salma; Sun, Xianfang; Yang, Hong; Yang, Yingxia; Xu, Ronghua; Nokes, Leonard Derek Martin; Yang, Xin

doi:10.1007/s11042-020-09661-4

Cited by 14 publications

(6 citation statements)

References 31 publications

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“…These methods have enabled them to complete tasks faster, with increased accuracy and precision. Nonetheless, it is challenging to automatically distinguish different parts of a brain tumor from healthy tissue due to their irregular shapes, sizes, and appearances [3]. Furthermore, cancerous cells can appear in various regions within the brain.…”

Section: Introductionmentioning

confidence: 99%

NeuroMorphNet: A Multiscale Convolutional Neural Network for High-Precision Brain Tumor Segmentation in 3D Medical Images

Guennich,

Othmani,

Ltifi

2023

Preprint

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The use of high-precision automatic algorithms to segment brain tumors offers the potential for improved disease diagnosis, treatment monitoring, as well as the possibility of large-scale pathological studies. In this study, we present a new 9-layer multiscale architecture dedicated to the semantic segmentation of 3D medical images, with a particular focus on brain tumor images, using convolutional neural networks. Our innovative solution draws inspiration from the Deepmedic architecture while incorporating significant enhancements. The use of variable-sized filters between layers and the early incorporation of residual connections from the very first layer greatly enhance the accuracy of 3D medical image segmentation. Additionally, the reduction in the number of layers in our dual-pathway network optimizes efficiency while maintaining exceptional performance. This combination of innovations, with Deepmedic as a starting point, positions our solution as a major advancement in the field of 3D medical image segmentation, offering an optimal balance between accuracy and efficiency for clinical applications.

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Section: Introductionmentioning

confidence: 99%

NeuroMorphNet: A Multiscale Convolutional Neural Network for High-Precision Brain Tumor Segmentation in 3D Medical Images

Guennich,

Othmani,

Ltifi

2023

Preprint

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“…Hence, In order to accomplish accurate and effective segmentation, automated segmentation methods have been created that make use of cutting-edge machine learning techniques like deep learning (DL) and convolutional neural networks (CNNs). Recent advancements in deep learning (DL) have led in development of new architectures such as U-Net, which has shown promising results in brain tumour segmentation [6]. These methods often require large, annotated datasets for training, which can be challenging to obtain.…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning algorithms have been the focus of current studies to boost the precision of brain tumour segmentation. [4] [6]. There are many glioma zones, including enhancing, non-enhancing, necrotic core, and peritumoural edema tumour core can also be accurately segmented using collection of 3D U-Net models.…”

Section: Introductionmentioning

confidence: 99%

Residual Edge Attention in U-Net for Brain Tumour Segmentation

Kumar,

Ajay,

Vardhini

et al. 2023

IJRITCC

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Identification and delineation of the tumour area in images of the brain constitute the crucial job of brain tumour segmentation in medical imaging. This task is crucial for diagnosis, treatment organizing, and keeping a track of brain tumours. Medical imaging methods like magnetic resonance imaging (MRI) or computed tomography scans are frequently used to divide brain tumours in real time. (CT). These imaging techniques provides high-resolution images for the brain that allows doctor to identify and locate tumours. There are several approaches to brain tumour segmentation, including manual segmentation by a radiologist, semi-automated segmentation using software tools that require some manual intervention, and fully automated segmentation using artificial intelligence (AI) algorithms. In this probing work, For segmenting brain tumours, we had anticipated Residual Edge Attention in U-Net design (ResEA-U-Net). Residual Edge Attention (ResEA) is a novel approach that enhances the performance of the U-Net architecture for brain tumour segmentation. The U-Net is often used in deep learning architecture for medical MRI brain image segmentation tasks, but it suffers from limited receptive field and feature reuse. To address this limitation, ResEA is expected to capture wide-range dependencies and enable network to focus on important regions of the image. The ResEA block contains of a residual block and an attention block that are connected in series. The residual block is created to improve the gradient flow and feature reuse, while the attention block focuses on important regions of the image by assigning higher weights to informative edges. The expected approach to evaluated on the BraTS data, which contain images of magnetic resonance of brain tumours. Experimental outcomes demonstrate that the ResEA-U-Net outperforms the baseline U-Net and other state-of-the-art methods. Overall, the suggested ResEA-U-Net architecture is a promising approach for brain tumour segmentation because it improves segmentation accuracy and lowers segmentation false positive rate, which can be essential for precise detection and therapy planning.

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“…Brain tumors are the growth of abnormal cells or a mass in a brain. Several studies discussed the application of Convolutional Neural Network (CNN) model as a deep learning architecture for MRI-based brain tumor segmentation by using magnetic resonance FLAIR images [2], multimodal MRI scans [3][4][5], and automatic semantic segmentation [6]. The study [7] presented 3D convolutional neural networks for tumor segmentation using long-range 2D context, which was then updated to more accurately classify and detect brain cancer cells in MRI and computerized tomography (CT) images using nano-contrast agents [8], and using dense residual refine networks for automatic brain tumor segmentation in [9,10].…”

Section: Introductionmentioning

confidence: 99%

Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

Jwaid

Al-Husseini

Sabry

2021

EEJET

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Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.

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Image classification-based brain tumour tissue segmentation

Cited by 14 publications

References 31 publications

NeuroMorphNet: A Multiscale Convolutional Neural Network for High-Precision Brain Tumor Segmentation in 3D Medical Images

NeuroMorphNet: A Multiscale Convolutional Neural Network for High-Precision Brain Tumor Segmentation in 3D Medical Images

Residual Edge Attention in U-Net for Brain Tumour Segmentation

Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

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