Department of Computer Science and Engineering, NIT Durgapur, 713209, India.
Department of Information Technology, Sikkim Manipal Institute of Technology, 737136, India.
Comput Methods Programs Biomed. 2022 Nov;226:107157. doi: 10.1016/j.cmpb.2022.107157. Epub 2022 Sep 28.
This paper has introduced a patch-based, residual, asymmetric, encoder-decoder CNN that solves two major problems in acute ischemic stroke lesion segmentation from CT and CT perfusion data using deep neural networks. First, the class imbalance is encountered since the lesion core size covers less than 5% of the volume of the entire brain. Second, deeper neural networks face the drawback of vanishing gradients, and this degrades the learning ability of the network.
The neural network architecture has been designed for better convergence and faster inference time without compromising performance to address these difficulties. It uses a training strategy combining Focal Tversky and Binary cross-entropy loss functions to overcome the class imbalance issue. The model comprises only four resolution steps with a total of 11 convolutional layers. A base filter of 8, used for the residual connection with two convolutional blocks at the encoder side, is doubled after each resolution step. Simultaneously, the decoder consists of residual blocks with one convolutional layer and a constant number of 8 filters in each resolution step. This proposition allows for a lighter build with fewer trainable parameters as well as aids in avoiding overfitting by allowing the decoder to decode only necessary information.
The presented method has been evaluated through submission on the publicly accessible platform of the Ischemic Stroke Lesion Segmentation (ISLES) 2018 medical image segmentation challenge achieving the second-highest testing dice similarity coefficient (DSC). The experimental results demonstrate that the proposed model achieves comparable performance to other submitted strategies in terms of DSC Precision, Recall, and Absolute Volume Difference (AVD).
Through the proposed approach, the two major research gaps are coherently addressed while achieving high challenge scores by solving the mentioned problems. Our model can serve as a tool for clinicians and radiologists to hasten decision-making and detect strokes efficiently.
本文介绍了一种基于补丁的、残差的、非对称的编码器-解码器卷积神经网络，该网络使用深度神经网络解决了从 CT 和 CT 灌注数据中分割急性缺血性脑卒中病变的两个主要问题。首先，由于病变核心大小不到整个大脑体积的 5%，因此会遇到类不平衡问题。其次，更深的神经网络面临着梯度消失的缺点，这会降低网络的学习能力。
为了解决这些困难，该神经网络架构被设计为更好地收敛和更快的推理时间，而不会影响性能。它使用结合了 Focal Tversky 和二值交叉熵损失函数的训练策略来克服类不平衡问题。该模型仅包含四个分辨率步骤，总共有 11 个卷积层。在编码器端，使用 8 的基本滤波器进行残差连接，并在每个分辨率步骤后将其翻倍。同时，解码器由具有一个卷积层的残差块组成，在每个分辨率步骤中具有相同数量的 8 个滤波器。这种方案允许构建更轻量级的模型，具有更少的可训练参数，并通过允许解码器仅解码必要的信息来避免过拟合。
该方法通过在可公开访问的 ISLES 2018 医学图像分割挑战赛平台上提交评估，获得了第二高的测试骰子相似系数（DSC）。实验结果表明，在所提出的模型在 DSC 精度、召回率和绝对体积差异（AVD）方面与其他提交的策略相比具有相当的性能。
通过所提出的方法，在解决所提到的问题的同时，一致地解决了两个主要的研究差距，并在挑战中获得了较高的分数。我们的模型可以作为临床医生和放射科医生的工具，以加快决策过程并有效地检测中风。
