Three-Dimensional Segmentation of Brain Tumors in Magnetic Resonance Imaging Based on Improved Continuous Max-Flow

Lu, 任璐 Ren; Qiang, 李锵 Li; Xin, 关欣 Guan; Jie, 马杰 Ma

doi:10.3788/lop55.111011

Cited by 4 publications

(2 citation statements)

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“…For image classification, AlexNet was one of the first popular deep learning frameworks [ 10 ]. It has 5 convolutional layers, followed by 3 fully connected layers, and at the time it was performed at the cutting edge on ImageNet [ 11 , 12 ].…”

Section: Background Studymentioning

confidence: 99%

Multiconvolutional Transfer Learning for 3D Brain Tumor Magnetic Resonance Images

Sangeetha

Muthukumaran

Deeba

et al. 2022

Computational Intelligence and Neuroscience

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The difficulty or cost of obtaining data or labels in applications like medical imaging has progressed less quickly. If deep learning techniques can be implemented reliably, automated workflows and more sophisticated analysis may be possible in previously unexplored areas of medical imaging. In addition, numerous characteristics of medical images, such as their high resolution, three-dimensional nature, and anatomical detail across multiple size scales, can increase the complexity of their analysis. This study employs multiconvolutional transfer learning (MCTL) for applying deep learning to small medical imaging datasets in an effort to address these issues. Multiconvolutional transfer learning is a model based on transfer learning that enables deep learning with small datasets. In order to learn new features on a smaller target dataset, an initial baseline is used in the transfer learning process. In this study, 3D MRI images of brain tumors are classified using a convolutional autoencoder method. In order to use unenhanced Magnetic Resonance Imaging (MRI) for clinical diagnosis, expensive and invasive contrast-enhancing procedures must be performed. MCTL has been shown to increase accuracy by 1.5%, indicating that small targets are more easily detected with MCTL. This research can be applied to a wide range of medical imaging and diagnostic procedures, including improving the accuracy of brain tumor severity diagnosis through the use of MRI.

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Section: Background Studymentioning

confidence: 99%

Multiconvolutional Transfer Learning for 3D Brain Tumor Magnetic Resonance Images

Sangeetha

Muthukumaran

Deeba

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…The inherent variability in the presentation of brain tumors-manifested in disparities in size, shape, and location across different patients-compounds the difficulty of the task. Additionally, delineating the boundaries between normal soft tissues and pathological tissues in MR images can be nebulous, as highlighted by Ren Lu et al [7]. Hence, while automated techniques hold promise, ensuring their accuracy remains an intricate academic and clinical pursuit.…”

Section: Introductionmentioning

confidence: 99%

A Symmetrical Approach to Brain Tumor Segmentation in MRI Using Deep Learning and Threefold Attention Mechanism

Rahman,

Zhang,

Bhutto

2023

Symmetry

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The symmetrical segmentation of brain tumor images is crucial for both clinical diagnosis and computer-aided prognosis. Traditional manual methods are not only asymmetrical in terms of efficiency but also prone to errors and lengthy processing. A significant barrier to the process is the complex interplay between the deep learning network for MRI brain tumor imaging and the harmonious compound of both local and global feature information, which can throw off the balance in segmentation accuracy. Addressing this asymmetry becomes essential for precise diagnosis. In answer to this challenge, we introduce a balanced, end-to-end solution for brain tumor segmentation, incorporating modifications that mirror the U-Net architecture, ensuring a harmonious flow of information. Beginning with symmetric enhancement of the visual quality of MRI brain images, we then apply a symmetrical residual structure. By replacing the convolutional modules in both the encoder and decoder sections with deep residual modules, we establish a balance that counters the vanishing gradient problem commonly faced when the network depth increases. Following this, a symmetrical threefold attention block is integrated. This addition ensures a balanced fusion of local and global image features, fine-tuning the network to symmetrically discern and learn essential image characteristics. This harmonious integration remarkably amplifies the network’s precision in segmenting MRI brain tumors. We further validate the equilibrium achieved by our proposed model using three brain tumor segmentation datasets and four metrics and by juxtaposing our model against 21 traditional and learning-based counterparts. The results confirm that our balanced approach significantly elevates performance in the segmentation of MRI brain tumor images without an asymmetrical increase in computational time.

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Research on the magnetic resonance imaging brain tumor segmentation algorithm based on DO‐UNet

Huang

Liu

2022

Int J Imaging Syst Tech

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With the social and economic development and the improvement of people's living standards, smart medical care is booming, and medical image processing is becoming more and more popular in research, of which brain tumor segmentation is an important branch of medical image processing. However, the manual segmentation method of brain tumors requires a lot of time and effort from the doctor and has a great impact on the treatment of patients. In order to solve this problem, we propose a DO‐UNet model for magnetic resonance imaging brain tumor image segmentation based on attention mechanism and multi‐scale feature fusion to realize fully automatic segmentation of brain tumors. Firstly, we replace the convolution blocks in the original U‐Net model with the residual modules to prevent the gradient disappearing. Secondly, the multi‐scale feature fusion is added to the skip connection of U‐Net to fuse the low‐level features and high‐level features more effectively. In addition, in the decoding stage, we add an attention mechanism to increase the weight of effective information and avoid information redundancy. Finally, we replace the traditional convolution in the model with DO‐Conv to speed up the network training and improve the segmentation accuracy. In order to evaluate the model, we used the BraTS2018, BraTS2019, and BraTS2020 datasets to train the improved model and validate it online, respectively. Experimental results show that the DO‐UNet model can effectively improve the accuracy of brain tumor segmentation and has good segmentation performance.

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Three-Dimensional Segmentation of Brain Tumors in Magnetic Resonance Imaging Based on Improved Continuous Max-Flow

Cited by 4 publications

References 0 publications

Multiconvolutional Transfer Learning for 3D Brain Tumor Magnetic Resonance Images

Multiconvolutional Transfer Learning for 3D Brain Tumor Magnetic Resonance Images

A Symmetrical Approach to Brain Tumor Segmentation in MRI Using Deep Learning and Threefold Attention Mechanism

Research on the magnetic resonance imaging brain tumor segmentation algorithm based on DO‐UNet

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