Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data.
Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data.
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DOI：
10.1109/tuffc.2020.2993779
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Lediju Bell MA
中科院分区：
文献类型：
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作者：
Nair AA;Washington KN;Tran TD;Reiter A;Lediju Bell MA
单平面波传输对于在扩展视场上需要高超声帧速率的自动成像任务是有希望的。然而，单个平面波声穿透通常产生次优的图像质量。为了解决这一限制，我们正在探索使用深度神经网络（DNN）作为延迟求和（DAS）波束成形的替代方案。这项工作的目标是直接从原始通道数据中获取信息，并同时生成用于自动超声任务的分割图和用于自动化的可解释监督的相应超声B模式图像。我们专注于可视化和分割组织周围的无回声目标，并忽略或淡化不太重要的周围结构。用Field II模拟训练的DNN用模拟、实验体模和训练期间未包括的体内数据集进行测试。对于未聚焦的输入声道数据（即，在应用接收时间延迟之前）、模拟、实验体模和体内测试数据集分别实现了0.92 ± 0.13、0.92 ± 0.03和0.77 ± 0.07的平均值±标准差Dice相似系数，以及0.95 ± 0.08、0.93 ± 0.08 0.75 ± 0.14。利用子孔径波束成形通道数据和对DNN架构的输入层的修改以接受这些数据，图像重建的保真度增加（例如，两个体内乳腺囊肿的多次采集的平均gCNR范围为0.89-0.96），但DNN显示帧速率从395 Hz降至287 Hz。总的来说，DNN成功地将从模拟数据中学习到的特征表示转换为体模和体内数据，这对于这种同时进行超声图像形成和分割的新方法来说是有希望的。
Single plane wave transmissions are promising for automated imaging tasks requiring high ultrasound frame rates over an extended field of view. However, a single plane wave insonification typically produces suboptimal image quality. To address this limitation, we are exploring the use of deep neural networks (DNNs) as an alternative to delay-and-sum (DAS) beamforming. The objectives of this work are to obtain information directly from raw channel data and to simultaneously generate both a segmentation map for automated ultrasound tasks and a corresponding ultrasound B-mode image for interpretable supervision of the automation. We focus on visualizing and segmenting anechoic targets surrounded by tissue and ignoring or deemphasizing less important surrounding structures. DNNs trained with Field II simulations were tested with simulated, experimental phantom, and in vivo data sets that were not included during training. With unfocused input channel data (i.e., prior to the application of receive time delays), simulated, experimental phantom, and in vivo test data sets achieved mean ± standard deviation Dice similarity coefficients of 0.92 ± 0.13, 0.92 ± 0.03, and 0.77 ± 0.07, respectively, and generalized contrast-to-noise ratios (gCNRs) of 0.95 ± 0.08, 0.93 ± 0.08, and 0.75 ± 0.14, respectively. With subaperture beamformed channel data and a modification to the input layer of the DNN architecture to accept these data, the fidelity of image reconstruction increased (e.g., mean gCNR of multiple acquisitions of two in vivo breast cysts ranged 0.89–0.96), but DNN display frame rates were reduced from 395 to 287 Hz. Overall, the DNNs successfully translated feature representations learned from simulated data to phantom and in vivo data, which is promising for this novel approach to simultaneous ultrasound image formation and segmentation.