GANPOP: Generative Adversarial Network Prediction of Optical Properties From Single Snapshot Wide-Field Images.
GANPOP: Generative Adversarial Network Prediction of Optical Properties From Single Snapshot Wide-Field Images.
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DOI：
10.1109/tmi.2019.2962786
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发表时间：
2020-06
影响因子：
10.6
通讯作者：
Durr NJ
中科院分区：
文献类型：
--
作者：
Chen MT;Mahmood F;Sweer JA;Durr NJ
我们提出了一个用于组织吸收和散射系数的广域、内容感知估计的深度学习框架，称为光学性质的生成对抗网络预测（GANPOP）。空间频域成像用于在660 nm处获得人体手和脚、新鲜切除的人类食管切除术样本和均匀组织幻象的真实光学特性。具有平场或结构化照明的物体图像与注册的光学属性映射配对，并用于训练从单个输入图像估计光学属性的条件生成对抗网络。我们通过使用标准化平均绝对误差（NMAE）度量，将GANPOP与单快照光学特性（SSOP）技术进行比较，对该方法进行基准测试。在人体胃肠道标本中，GANPOP使用单个结构光输入图像估计减少的散射和吸收系数的精度比SSOP高60%，而GANPOP使用单个平场照明图像的精度与SSOP相似。当应用于体内和离体猪组织时，GANPOP模型仅对人类标本和幻影的结构照明图像进行训练，估计光学特性比SSOP提高了约46%，表明对新的、看不见的组织类型的适应性。给定一个适当跨越目标域的训练集，GANPOP有可能实现快速准确的光学特性宽视场测量。
We present a deep learning framework for wide-field, content-aware estimation of absorption and scattering coefficients of tissues, called Generative Adversarial Network Prediction of Optical Properties (GANPOP). Spatial frequency domain imaging is used to obtain ground-truth optical properties at 660 nm from in vivo human hands and feet, freshly resected human esophagectomy samples, and homogeneous tissue phantoms. Images of objects with either flat-field or structured illumination are paired with registered optical property maps and are used to train conditional generative adversarial networks that estimate optical properties from a single input image. We benchmark this approach by comparing GANPOP to a single-snapshot optical property (SSOP) technique, using a normalized mean absolute error (NMAE) metric. In human gastrointestinal specimens, GANPOP with a single structured-light input image estimates the reduced scattering and absorption coefficients with 60% higher accuracy than SSOP while GANPOP with a single flat-field illumination image achieves similar accuracy to SSOP. When applied to both in vivo and ex vivo swine tissues, a GANPOP model trained solely on structured-illumination images of human specimens and phantoms estimates optical properties with approximately 46% improvement over SSOP, indicating adaptability to new, unseen tissue types. Given a training set that appropriately spans the target domain, GANPOP has the potential to enable rapid and accurate wide-field measurements of optical properties.