Cross-modality synthesis from CT to PET using FCN and GAN networks for improved automated lesion detection
Cross-modality synthesis from CT to PET using FCN and GAN networks for improved automated lesion detection
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DOI：
10.1016/j.engappai.2018.11.013
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发表时间：
2019-02-01
影响因子：
8
通讯作者：
Greenspan, Hayit
中科院分区：
文献类型：
--
作者：
Ben-Cohen, Avi;Klang, Eyal;Greenspan, Hayit
在这项工作中，我们提出了一种新的系统，使用CT扫描生成虚拟PET图像。我们结合联合收割机的全卷积网络（FCN）与条件生成对抗网络（GAN），从给定的输入CT数据生成模拟PEI数据。合成的PET可用于减少病变检测解决方案中的假阳性。在临床上，这种解决方案可以在仅CT的环境中实现病变检测和药物治疗评估，从而减少对更昂贵的放射性PET/CT扫描的需求。我们的数据集包括：来自Sheba医疗中心的60个PET/CT扫描。我们使用了23次扫描用于训练，37次用于测试。定性比较了不同方案实现合成的效果。使用现有的病变检测软件进行定量评价，将合成的PET结合作为假阳性减少层，用于检测肝脏中的恶性病变。目前的结果看起来很有希望，显示每个病例的平均假阳性率从2.9降低到2.1，降低了28%。建议的解决办法是全面的，可以扩展到其他身体器官和不同的模式。
In this work we present a novel system for generation of virtual PET images using CT scans. We combine a fully convolutional network (FCN) with a conditional generative adversarial network (GAN) to generate simulated PEI data from given input CT data. The synthesized PET can be used for false-positive reduction in lesion detection solutions. Clinically, such solutions may enable lesion detection and drug treatment evaluation in a CT-only environment, thus reducing the need for the more expensive and radioactive PET/CT scan. Our dataset include: 60 PET/CT scans from Sheba Medical center. We used 23 scans for training and 37 for testing. Different scheme; to achieve the synthesized output were qualitatively compared. Quantitative evaluation was conducted using an existing lesion detection software, combining the synthesized PET as a false positive reduction layer for the detection of malignant lesions in the liver. Current results look promising showing a 28% reduction in the average false positive per case from 2.9 to 2.1. The suggested solution is comprehensive and can be expanded to additional body organs, and different modalities.