Hu Dingyuan, Qu Shiya, Jiang Yuhang, Han Chunyu, Liang Hongbin, Zhang Qingyan
School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Qianshan District, Anshan City, Liaoning Province, China.
Radiology, Ninth People's Hospital of Zhengzhou, Jinshui District, Zhengzhou City, Henan Province, China.
PLoS One. 2024 Mar 11;19(3):e0295536. doi: 10.1371/journal.pone.0295536. eCollection 2024.
Brain extraction is an important prerequisite for the automated diagnosis of intracranial lesions and determines, to a certain extent, the accuracy of subsequent lesion identification, localization, and segmentation. To address the problem that the current traditional image segmentation methods are fast in extraction but poor in robustness, while the Full Convolutional Neural Network (FCN) is robust and accurate but relatively slow in extraction, this paper proposes an adaptive mask-based brain extraction method, namely AMBBEM, to achieve brain extraction better. The method first uses threshold segmentation, median filtering, and closed operations for segmentation, generates a mask for the first time, then combines the ResNet50 model, region growing algorithm, and image properties analysis to further segment the mask, and finally complete brain extraction by multiplying the original image and the mask. The algorithm was tested on 22 test sets containing different lesions, and the results showed MPA = 0.9963, MIoU = 0.9924, and MBF = 0.9914, which were equivalent to the extraction effect of the Deeplabv3+ model. However, the method can complete brain extraction of approximately 6.16 head CT images in 1 second, much faster than Deeplabv3+, U-net, and SegNet models. In summary, this method can achieve accurate brain extraction from head CT images more quickly, creating good conditions for subsequent brain volume measurement and feature extraction of intracranial lesions.
脑提取是颅内病变自动诊断的重要前提，在一定程度上决定了后续病变识别、定位和分割的准确性。针对当前传统图像分割方法提取速度快但鲁棒性差，而全卷积神经网络（FCN）鲁棒性和准确性高但提取速度相对较慢的问题，本文提出一种基于自适应掩码的脑提取方法，即AMBBEM，以更好地实现脑提取。该方法首先采用阈值分割、中值滤波和闭运算进行分割，首次生成掩码，然后结合ResNet50模型、区域生长算法和图像属性分析对掩码进行进一步分割，最后通过将原始图像与掩码相乘完成脑提取。该算法在包含不同病变的22个测试集上进行了测试，结果显示MPA = 0.9963、MIoU = 0.9924、MBF = 0.9914，与Deeplabv3+模型的提取效果相当。然而，该方法每秒可完成约6.16幅头部CT图像的脑提取，比Deeplabv3+、U-net和SegNet模型快得多。综上所述，该方法能够更快地从头部CT图像中实现准确的脑提取，为后续脑容量测量和颅内病变特征提取创造了良好条件。
