PDF(Pubmed)
Abstract：
Accurate segmentation of stroke lesions on MRI images is very important for neurologists in the planning of post-stroke care. Segmentation helps clinicians to better diagnose and evaluation of any treatment risks. However, manual segmentation of brain lesions relies on the experience of neurologists and is also a very tedious and time-consuming process. So, in this study, we proposed a novel deep convolutional neural network (CNN-Res) that automatically performs the segmentation of ischemic stroke lesions from multimodal MRIs.
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.
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.
摘要：
背景：在MRI图像上准确分割中风病变对于神经科医生在中风后护理计划中非常重要。分割有助于临床医生更好地诊断和评估任何治疗风险。然而,脑部病变的手动分割依赖于神经科医生的经验，也是一个非常繁琐和耗时的过程。所以,在这项研究中，我们提出了一种新的深度卷积神经网络(CNN-Res),该网络可自动从多模态MRI中分割缺血性卒中病变.
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。
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。
