论文标题
使用神经体系结构搜索有效的OCT图像分割
Efficient OCT Image Segmentation Using Neural Architecture Search
论文作者
论文摘要
在这项工作中，我们提出了一个神经体系结构搜索（NAS），用于视网膜层分割（OCT）扫描。我们将UNET体系结构纳入NAS框架中，作为其在收集和预处理的OCT图像数据集中分割视网膜层的主链。在预处理阶段，我们对原始OCT扫描进行超级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，建议使用不同的原始操作来找到下采样和向上采样的单元格，并应用了二进制门方法来使搜索策略用于手头任务。我们在内部OCT数据集上经验评估了我们的方法。实验结果表明，自我适应的NAS-UNET架构通过在平均值中实现95.4％的联合指标，而在骰子相似性系数中实现了95.4％的架构，从而超过了竞争性的人类设计结构。
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.
