Ali Tahir Mohammad, Nawaz Ali, Ur Rehman Attique, Ahmad Rana Zeeshan, Javed Abdul Rehman, Gadekallu Thippa Reddy, Chen Chin-Ling, Wu Chih-Ming
Department of Computer Science, GULF University for Science and Technology, Mishref, Kuwait.
Department of Software Engineering, University of Sialkot, Sialkot, Pakistan.
Front Oncol. 2022 Jun 1;12:873268. doi: 10.3389/fonc.2022.873268. eCollection 2022.
Magnetic resonance imaging is the most generally utilized imaging methodology that permits radiologists to look inside the cerebrum using radio waves and magnets for tumor identification. However, it is tedious and complex to identify the tumorous and nontumorous regions due to the complexity in the tumorous region. Therefore, reliable and automatic segmentation and prediction are necessary for the segmentation of brain tumors. This paper proposes a reliable and efficient neural network variant, i.e., an attention-based convolutional neural network for brain tumor segmentation. Specifically, an encoder part of the UNET is a pre-trained VGG19 network followed by the adjacent decoder parts with an attention gate for segmentation noise induction and a denoising mechanism for avoiding overfitting. The dataset we are using for segmentation is BRATS'20, which comprises four different MRI modalities and one target mask file. The abovementioned algorithm resulted in a dice similarity coefficient of 0.83, 0.86, and 0.90 for enhancing, core, and whole tumors, respectively.
磁共振成像是最常用的成像方法，它使放射科医生能够利用无线电波和磁体观察大脑内部以识别肿瘤。然而，由于肿瘤区域的复杂性，识别肿瘤和非肿瘤区域既繁琐又复杂。因此，对于脑肿瘤分割来说，可靠且自动的分割和预测是必要的。本文提出了一种可靠且高效的神经网络变体，即用于脑肿瘤分割的基于注意力的卷积神经网络。具体而言，UNET的编码器部分是一个预训练的VGG19网络，其后是相邻的解码器部分，带有用于分割噪声诱导的注意力门和用于避免过拟合的去噪机制。我们用于分割的数据集是BRATS'20，它包含四种不同的MRI模态和一个目标掩码文件。上述算法在增强肿瘤、核心肿瘤和整个肿瘤方面分别产生了0.83、0.86和0.90的骰子相似系数。
