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arXiv 2007.14790eess.IVcs.CV
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基于神经结构搜索的高效OCT图像分割

Efficient OCT Image Segmentation Using Neural Architecture Search

West Virginia University(西弗吉尼亚大学);Rockefeller Neuroscience Institute(洛克菲勒神经科学研究所);Lane Department of Computer Science and Electrical Engineering, West Virginia University(西弗吉尼亚大学莱恩计算机科学与电气工程学院);Ophthalmology and Visual Sciences, West Virginia University(西弗吉尼亚大学眼科学与视觉科学系)

Saba Heidari Gheshlaghi, Omid Dehzangi, Ali Dabouei, Annahita Amireskandari, Ali Rezai, Nasser M Nasrabadi

摘要:

在这项工作中,我们提出了一个神经结构搜索(NAS)的视网膜层分割光学相干断层扫描(OCT)扫描。我们将Unet架构作为NAS框架中的骨干,用于在我们收集和预处理的OCT图像数据集中分割视网膜层。在预处理阶段,我们对原始OCT扫描图像进行了超分辨率和图像处理技术,以提高原始图像的质量。对于我们的搜索策略,不同的原始操作,建议找到下采样和上采样单元块,并应用二进制门的方法,使搜索策略实际的任务在手。我们在我们的内部OCT数据集上经验性地评估了我们的方法。实验结果表明,自适应NAS-Unet架构大大优于竞争对手的人类设计的架构,达到95.4%的平均交集超过联合度量和78.7%的骰子相似系数。

英文摘要:

In this work, we propose a Neural Architecture Search (NAS) for retinal layer segmentation in Optical Coherence Tomography (OCT) scans. We incorporate the Unet architecture in the NAS framework as its backbone for the segmentation of the retinal layers in our collected and pre-processed OCT image dataset. At the pre-processing stage, we conduct super resolution and image processing techniques on the raw OCT scans to improve the quality of the raw images. For our search strategy, different primitive operations are suggested to find the down- & up-sampling cell blocks, and the binary gate method is applied to make the search strategy practical for the task in hand. We empirically evaluated our method on our in-house OCT dataset. The experimental results demonstrate that the self-adapting NAS-Unet architecture substantially outperformed the competitive human-designed architecture by achieving 95.4% in mean Intersection over Union metric and 78.7% in Dice similarity coefficient.

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