Beyin Bilgisayarlı Tomografi Görüntülerinde Derin Öğrenme Tabanlı İskemik İnme Hastalığı Segmentasyonu

Simge Uçkun, Mahmut Ağrali, Volkan Kiliç
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引用次数: 1

Abstract

Stroke is brain cell death because of either lack of blood flow (ischemic) or bleeding (hemorrhagic) that prevents the brain from functioning properly in both conditions. Ischemic stroke is a common type of stroke caused by a blockage in the cerebrovascular system that prevents blood from flowing to brain regions and directly blocks blood vessels. Computed tomography (CT) scanning is frequently used in the evaluation of stroke, and rapid and accurate diagnosis of ischemic stroke with CT images is critical for determining the appropriate treatment. The manual diagnosis of ischemic stroke can be error-prone due to several factors, such as the busy schedules of specialists and the large number of patients admitted to healthcare facilities. Therefore, in this paper, a deep learning-based interface was developed to automatically diagnose the ischemic stroke through segmentation on CT images leading to a reduction on the diagnosis time and workload of specialists. Convolutional Neural Networks (CNNs) allow automatic feature extraction in ischemic stroke segmentation, utilized to mark the disease regions from CT images. CNN-based architectures, such as U-Net, U-Net VGG16, U-Net VGG19, Attention U-Net, and ResU-Net, were used to benchmark the ischemic stroke disease segmentation. To further improve the segmentation performance, ResU-Net was modified, adding a dilation convolution layer after the last layer of the architecture. In addition, data augmentation was performed to increase the number of images in the dataset, including the ground truths for the ischemic stroke disease region. Based on the experimental results, our modified ResU-Net with a dilation convolution provides the highest performance for ischemic stroke segmentation in dice similarity coefficient (DSC) and intersection over union (IoU) with 98.45 % and 96.95 %, respectively. The experimental results show that our modified ResU-Net outperforms the state-of-the-art approaches for ischemic stroke disease segmentation. Moreover, the modified architecture has been deployed into a new desktop application called BrainSeg, which can support specialists during the diagnosis of the disease by segmenting ischemic stroke.
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中风是由于缺乏血液流动(缺血性)或出血(出血性)而导致的脑细胞死亡,这两种情况下大脑都无法正常工作。缺血性中风是一种常见的中风类型,由脑血管系统堵塞引起,阻止血液流向大脑区域,直接阻塞血管。计算机断层扫描(CT)经常用于脑卒中的评估,通过CT图像快速准确地诊断缺血性脑卒中对于确定适当的治疗方法至关重要。由于多种因素,例如专家的繁忙日程和医疗机构收治的大量患者,人工诊断缺血性中风可能容易出错。因此,本文开发了一种基于深度学习的接口,通过对CT图像的分割来自动诊断缺血性中风,从而减少了专家的诊断时间和工作量。卷积神经网络(cnn)在缺血性卒中分割中实现了特征的自动提取,用于从CT图像中标记疾病区域。采用基于cnn的U-Net、U-Net VGG16、U-Net VGG19、Attention U-Net、ResU-Net等架构对缺血性脑卒中疾病分割进行基准测试。为了进一步提高分割性能,对ResU-Net进行了改进,在架构的最后一层之后增加了一个扩张卷积层。此外,还进行了数据增强,以增加数据集中的图像数量,包括缺血性中风疾病区域的基本事实。实验结果表明,基于扩张卷积的改进的ResU-Net在骰子相似系数(DSC)和交联(IoU)上的分割效果最好,分别达到98.45%和96.95%。实验结果表明,我们改进的ResU-Net在缺血性卒中疾病分割方面优于目前最先进的方法。此外,修改后的架构已经部署到一个名为BrainSeg的新桌面应用程序中,该应用程序可以通过分割缺血性中风来支持专家进行疾病诊断。
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