论文标题
使用卷积神经网络合成的对比合成的丘脑核分割方案
A Contrast Synthesized Thalamic Nuclei Segmentation Scheme using Convolutional Neural Networks
论文作者
论文摘要
丘脑核与几种神经系统疾病有关。与传统的mprage图像相比，已显示WMN-mprage图像可提供更好的丘脑内核对比度，但额外的获取导致检查时间增加。在这项工作中，我们研究了基于3D卷积神经网络（CNN）的技术，用于从常规的Mprage图像中对丘脑核的分析。开发了两个3D CNN，并使用mprage图像进行了比较丘脑核的划线：a）使用从mprage图像合成的WMN-mprage图像合成造影剂分割（SCS）的天然对比度分割（NCS）和b）合成的对比分割（SCS）。我们使用MPRAGE图像（n = 35）和使用基于多ATLA的拟释放技术生成的丘脑核标签培训了两个分割框架。对包括健康受试者和酒精使用障碍患者（AUD）的患者（n = 45）的队列评估了分割精度和临床实用性。与NCS网络相比，与NCS网络相比，SCS网络在内侧遗传核（p = .003）和丝粒核（p = .01）中产生较高的骰子评分（p = .003）和centromedian核（p = .01），与NCS网络相比，腹侧前侧（p = .001）和腹侧后侧（P = .01）核的体积差。平淡无奇的阿尔特曼分析表明，与真实体积和SCS网络预测的差异之间的变化系数较低，一致性的限制较大。与健康年龄匹配的对照相比，AUD患者的SCS网络表现出AUD患者腹侧侧面后核的显着萎缩（P = 0.01），与先前关于酒精中毒丘脑萎缩的研究一致，而NCS网络显示NCS网络显示腹侧后核的杂化性萎缩。分割之前基于CNN的对比度合成可以从常规Mprage图像中提供快速准确的丘脑核分割。
Thalamic nuclei have been implicated in several neurological diseases. WMn-MPRAGE images have been shown to provide better intra-thalamic nuclear contrast compared to conventional MPRAGE images but the additional acquisition results in increased examination times. In this work, we investigated 3D Convolutional Neural Network (CNN) based techniques for thalamic nuclei parcellation from conventional MPRAGE images. Two 3D CNNs were developed and compared for thalamic nuclei parcellation using MPRAGE images: a) a native contrast segmentation (NCS) and b) a synthesized contrast segmentation (SCS) using WMn-MPRAGE images synthesized from MPRAGE images. We trained the two segmentation frameworks using MPRAGE images (n=35) and thalamic nuclei labels generated on WMn-MPRAGE images using a multi-atlas based parcellation technique. The segmentation accuracy and clinical utility were evaluated on a cohort comprising of healthy subjects and patients with alcohol use disorder (AUD) (n=45). The SCS network yielded higher Dice scores in the Medial geniculate nucleus (P=.003) and Centromedian nucleus (P=.01) with lower volume differences for Ventral anterior (P=.001) and Ventral posterior lateral (P=.01) nuclei when compared to the NCS network. A Bland-Altman analysis revealed tighter limits of agreement with lower coefficient of variation between true volumes and those predicted by the SCS network. The SCS network demonstrated a significant atrophy in Ventral lateral posterior nucleus in AUD patients compared to healthy age-matched controls (P=0.01), agreeing with previous studies on thalamic atrophy in alcoholism, whereas the NCS network showed spurious atrophy of the Ventral posterior lateral nucleus. CNN-based contrast synthesis prior to segmentation can provide fast and accurate thalamic nuclei segmentation from conventional MPRAGE images.
