School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, 212100, China.
School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, 212100, China.
Comput Biol Med. 2022 Sep;148:105693. doi: 10.1016/j.compbiomed.2022.105693. Epub 2022 Jun 2.
In this paper, we propose a novel U-Net with fully connected residual blocks (FCRB U-Net) for the fetal cerebellum Ultrasound image segmentation task. FCRB U-Net, an improved convolutional neural network (CNN) based on U-Net, replaces the double convolution operation in the original model with the fully connected residual block and embeds an effective channel attention module to enhance the extraction of valid features. Moreover, in the decoding stage, a feature reuse module is employed to form a fully connected decoder to make full use of deep features. FCRB U-Net can effectively alleviate the problem of the loss of feature information during the convolution process and improve segmentation accuracy. Experimental results demonstrate that the proposed approach is effective and promising in the field of fetal cerebellar segmentation in actual Ultrasound images. The average IoU value and mean Dice index reach 86.72% and 90.45%, respectively, which are 3.07% and 5.25% higher than that of the basic U-Net.
在本文中，我们提出了一种新的基于全连接残差块（FCRB U-Net）的 U-Net 模型，用于胎儿小脑超声图像分割任务。FCRB U-Net 是一种基于 U-Net 的改进卷积神经网络（CNN），它用全连接残差块替代了原始模型中的双卷积操作，并嵌入了有效的通道注意力模块，以增强有效特征的提取。此外，在解码阶段，使用特征复用模块形成全连接解码器，以充分利用深层特征。FCRB U-Net 可以有效缓解卷积过程中特征信息丢失的问题，提高分割精度。实验结果表明，该方法在实际超声图像胎儿小脑分割领域是有效且有前景的。所提出方法的平均 IoU 值和平均 Dice 指数分别达到 86.72%和 90.45%，分别比基本 U-Net 高 3.07%和 5.25%。
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