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A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images.
Computational and Mathematical Methods in Medicine ( IF 2.809 ) Pub Date : 2020-05-05 , DOI: 10.1155/2020/4519483
Min Xu 1, 2, 3 , Pengjiang Qian 3, 4 , Jiamin Zheng 4 , Hongwei Ge 2 , Raymond F Muzic 5
Affiliation  

We propose a new method for fast organ classification and segmentation of abdominal magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is a new type of high-tech imaging examination fashion in recent years. Recognition of specific target areas (organs) based on MR images is one of the key issues in computer-aided diagnosis of medical images. Artificial neural network technology has made significant progress in image processing based on the multimodal MR attributes of each pixel in MR images. However, with the generation of large-scale data, there are few studies on the rapid processing of large-scale MRI data. To address this deficiency, we present a fast radial basis function artificial neural network (Fast-RBF) algorithm. The importance of our efforts is as follows: (1) The proposed algorithm achieves fast processing of large-scale image data by introducing the -insensitive loss function, the structural risk term, and the core-set principle. We apply this algorithm to the identification of specific target areas in MR images. (2) For each abdominal MRI case, we use four MR sequences (fat, water, in-phase (IP), and opposed-phase (OP)) and the position coordinates (, ) of each pixel as the input of the algorithm. We use three classifiers to identify the liver and kidneys in the MR images. Experiments show that the proposed method achieves a higher precision in the recognition of specific regions of medical images and has better adaptability in the case of large-scale datasets than the traditional RBF algorithm.

中文翻译:

一种基于径向基神经网络的新型快速训练方法,用于识别MR图像中的器官。

我们提出了一种快速的器官分类和腹部磁共振(MR)图像分割的新方法。磁共振成像(MRI)是近年来的一种新型高科技成像检查方式。基于MR图像的特定目标区域(器官)的识别是医学图像的计算机辅助诊断中的关键问题之一。人工神经网络技术已经基于MR图像中每个像素的多峰MR属性在图像处理方面取得了重大进展。然而,随着大规模数据的产生,关于大规模MRI数据的快速处理的研究很少。为了解决这一缺陷,我们提出了一种快速径向基函数人工神经网络(Fast-RBF)算法。我们努力的重要性如下:-不敏感的损失函数,结构风险项和核心集原则。我们将此算法应用于MR图像中特定目标区域的识别。(2)对于每个腹部MRI的情况下,我们使用四个MR序列(脂肪,水,同相(IP),以及相对的相(OP))和位置坐标( 的每个像素的作为该算法的输入。我们使用三个分类器来识别MR图像中的肝脏和肾脏。实验表明,与传统的RBF算法相比,该方法在识别医学图像特定区域方面具有更高的精度,并且在大规模数据集的情况下具有更好的适应性。
更新日期:2020-05-05
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