利用神经技术的智能级联 U-Net 模型进行脑肿瘤分割

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-04-03 DOI:10.3389/fncom.2024.1391025
Haewon Byeon, Mohannad Al-Kubaisi, Ashit Kumar Dutta, Faisal Alghayadh, Mukesh Soni, Manisha Bhende, Venkata Chunduri, K. Suresh Babu, Rubal Jeet
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引用次数: 0

摘要

神经学专家指出,脑肿瘤对人类健康构成严重威胁。脑肿瘤的临床识别和治疗在很大程度上依赖于精确的分割。脑肿瘤的大小、形态和位置各不相同,因此准确的自动分割是神经科学领域的一个巨大障碍。U-Net 凭借其计算智能和简洁的设计,近来已成为解决医学图像分割问题的首选模型。局部感受野受限、空间信息丢失和上下文信息不足等问题仍然困扰着人工智能。卷积神经网络(CNN)和梅尔谱图是这一咳嗽识别技术的基础。首先,我们在各种复杂的设置中组合语音,并改进音频数据。然后,我们对数据进行预处理,确保其长度一致,并从中创建一个梅尔频谱图。为解决这些问题,我们提出了一种用于脑肿瘤分割(BTS)的新型模型--智能级联 U 网(ICU-Net)。它建立在动态卷积的基础上,并使用非局部关注机制。为了重建更详细的脑肿瘤空间信息,主要设计了一个两级级联的 3DU-Net 。本文的目标是找出最佳可学习参数,使数据的可能性最大化。在网络具备收集人工智能远距离依赖关系的能力后,期望最大化被应用于级联网络的横向联系,使其能够更有效地利用上下文数据。最后,为了增强网络捕捉局部特征的能力,我们使用了具有局部自适应能力的动态卷积来替代级联网络的标准卷积。我们将结果与其他典型方法进行了比较,并利用公开的 BraTS 2019/2020 数据集进行了广泛测试。根据实验数据,建议的方法在涉及 BTS 的任务中表现良好。肿瘤核心(TC)、完整肿瘤和增强肿瘤分割 BraTS 2019/2020 验证集的 Dice 分数分别为 0.897/0.903、0.826/0.828 和 0.781/0.786,表明该方法在 BTS 中表现优异。
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Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model
According to experts in neurology, brain tumours pose a serious risk to human health. The clinical identification and treatment of brain tumours rely heavily on accurate segmentation. The varied sizes, forms, and locations of brain tumours make accurate automated segmentation a formidable obstacle in the field of neuroscience. U-Net, with its computational intelligence and concise design, has lately been the go-to model for fixing medical picture segmentation issues. Problems with restricted local receptive fields, lost spatial information, and inadequate contextual information are still plaguing artificial intelligence. A convolutional neural network (CNN) and a Mel-spectrogram are the basis of this cough recognition technique. First, we combine the voice in a variety of intricate settings and improve the audio data. After that, we preprocess the data to make sure its length is consistent and create a Mel-spectrogram out of it. A novel model for brain tumor segmentation (BTS), Intelligence Cascade U-Net (ICU-Net), is proposed to address these issues. It is built on dynamic convolution and uses a non-local attention mechanism. In order to reconstruct more detailed spatial information on brain tumours, the principal design is a two-stage cascade of 3DU-Net. The paper’s objective is to identify the best learnable parameters that will maximize the likelihood of the data. After the network’s ability to gather long-distance dependencies for AI, Expectation–Maximization is applied to the cascade network’s lateral connections, enabling it to leverage contextual data more effectively. Lastly, to enhance the network’s ability to capture local characteristics, dynamic convolutions with local adaptive capabilities are used in place of the cascade network’s standard convolutions. We compared our results to those of other typical methods and ran extensive testing utilising the publicly available BraTS 2019/2020 datasets. The suggested method performs well on tasks involving BTS, according to the experimental data. The Dice scores for tumor core (TC), complete tumor, and enhanced tumor segmentation BraTS 2019/2020 validation sets are 0.897/0.903, 0.826/0.828, and 0.781/0.786, respectively, indicating high performance in BTS.
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
期刊最新文献
A combinatorial deep learning method for Alzheimer's disease classification-based merging pretrained networks. Multi-scale asynchronous correlation and 2D convolutional autoencoder for adolescent health risk prediction with limited fMRI data. Decoding the application of deep learning in neuroscience: a bibliometric analysis. Optimizing extubation success: a comparative analysis of time series algorithms and activation functions. Editorial: Understanding and bridging the gap between neuromorphic computing and machine learning, volume II.
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