A. A. Sovetsky, A. Matveyev, A. A. Zykov, V. Zaitsev, L. Matveev
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引用次数: 0
摘要
近年来,计算机视觉方法呈指数级增长。训练人工智能模型通常需要标注数据。为了提高这一过程的效率,人们可以使用半自动语义注释工具,在这些工具中,一些简化的方法(基于一些预训练模型或可见特征参数)得以实现,并通过手动调整来隔离特定对象。OCT 信号包含特定斑点结构和信号衰减模式的信息。这些模式的参数与有形的组织属性(如散射体空间分布)相对应,因此可用于构建半自动语义注释工具。利用 OCT 信号模拟方法,我们评估了斑点模式和衰减系数的参数,并为 OCT 扫描提出了新的语义注释工具。我们展示了半自动三维分割和注释的性能。该工具既可作为人工智能应用的辅助工具,也可作为半自动扫描分割和进一步表征的独立工具。
OCT-specific signal features for semi-automatic semantic scans annotation and segmentation
Computer vision approaches have grown exponentially in recent years. Training AI models often requires annotated data. To increase effectiveness of this procedure one can use semi-automatic semantic annotation tools where some simplified approaches (based either on some pretrained models or visible features parameters) are implemented and manually tuned to isolate specific objects. OCT-signals contain information-bearing specific speckle structure and signal attenuation patterns. The parameters of these patterns corresponds to tangible tissue properties (such as scatterers spatial distributions), therefore can be used to construct semi-automatic semantic annotation tools. Using OCT-signal simulation approaches we evaluate the parameters of speckle patterns and attenuation coefficients and propose novel semantic annotation tools for OCT scans. We demonstrate the performance of semi-automatic 3D segmentation and annotation. This tool can be used as a supportive tool for AI applications as well as independent tool for semi-automatic scans segmentations and further characterization.