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首页> 外文会议>International Conference on Medical Image Computing and Computer-Assisted Intervention >Automated Infarct Segmentation from Follow-up Non-Contrast CT Scans in Patients with Acute Ischemic Stroke Using Dense Multi-Path Contextual Generative Adversarial Network
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Automated Infarct Segmentation from Follow-up Non-Contrast CT Scans in Patients with Acute Ischemic Stroke Using Dense Multi-Path Contextual Generative Adversarial Network

机译：急性缺血性脑卒中患者随访非对比度CT扫描的自动梗塞分割，使用致密的多路径上下文生成对抗网络

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摘要

Cerebral infarct volume measured in follow-up non-contrast CT (NCCT) scans is an important radiologic outcome measure evaluating the effectiveness of endovascular therapy of acute ischemic stroke (AIS) patients. In this paper, a dense Multi-Path Contextual Generative Adversarial Network (MPC-GAN) is proposed to automatically segment ischemic infarct volume from NCCT images of AIS patients. The developed MPC-GAN approach makes use of a dense multi-path U-Net as generator regularized by a discriminator network. Both generator and discriminator take contextual information as inputs, such as bilateral intensity difference, infarct location probability, and distance to cerebrospinal fluid (CSF). We collected 100 NCCT images with manual segmentations. Of 100 patients, 60 patients were used to train the MPC-GAN, 10 patients were used to tune the parameters, and the remained 30 patients were used for validation. Quantitative results in comparison with manual segmentations show that the proposed MPC-GAN achieved a dice coefficient (DC) of 72.6%, outperforming some state-of-the-art segmentation methods, such as U-Net, U-Net based GAN, and random forest based segmentation method.
机译：在随访非对比度CT（NCCT）扫描中测量的脑梗塞体积是评估急性缺血性卒中（AIS）患者血管内治疗的有效性的重要放射学结果。本文提出了一种致密的多路径上下文生成对抗网络（MPC-GaN），从AIS患者的NCCT图像自动分段缺血梗塞体积。发达的MPC-GaN方法利用密集的多路径U-Net作为由鉴别器网络规范的发电机。发电机和鉴别器都将上下文信息作为输入，例如双侧强度差，梗塞位置概率和与脑脊液（CSF）的距离。我们用手动分割收集了100个NCCT图像。在100名患者中，60名患者用于训练MPC-GaN，10名患者用于调整参数，剩余的30名患者用于验证。与手动分段相比的定量结果表明，所提出的MPC-GaN实现了72.6％的骰子系数（DC），优于一些最先进的分段方法，例如U-Net，基于U-Net的GaN，以及随机林基分割方法。

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