CNN-Res used a U-shaped structure, so the network has encryption and decryption paths. The residual units are embedded in the encoder path. In this model, to reduce gradient descent, the residual units were used, and to extract more complex information in images, multimodal MRI data were applied. In the link between the encryption and decryption subnets, the bottleneck strategy was used, which reduced the number of parameters and training time compared to similar research.
CNN-Res was evaluated on two distinct datasets. First, it was examined on a dataset collected from the Neuroscience Center of Tabriz University of Medical Sciences, where the average Dice coefficient was equal to 85.43%. Then, to compare the efficiency and performance of the model with other similar works, CNN-Res was evaluated on the popular SPES 2015 competition dataset where the average Dice coefficient was 79.23%.
This study presented a new and accurate method for the segmentation of MRI medical images using a deep convolutional neural network called CNN-Res, which directly predicts segment maps from raw input pixels.
方法:CNN-Res使用U形结构,所以网络有加密和解密路径。残差单元嵌入在编码器路径中。在这个模型中,为了减少梯度下降,使用了剩余单位,并在图像中提取更复杂的信息,应用多模态MRI数据。在加密和解密子网之间的链接中,使用了瓶颈策略,与同类研究相比,减少了参数数量和训练时间。
结果:CNN-Res在两个不同的数据集上进行了评估。首先,它是在大不里士医学院神经科学中心收集的数据集上检查的,其中平均骰子系数等于85.43%。然后,为了将模型的效率和性能与其他类似作品进行比较,CNN-Res在流行的SPES2015比赛数据集上进行了评估,其中平均骰子系数为79.23%。
结论:这项研究提出了一种使用称为CNN-Res的深度卷积神经网络对MRI医学图像进行分割的新的准确方法。它直接从原始输入像素预测分段图。