论文标题
使用深度学习自动检测和计数视网膜细胞核
Automatic detection and counting of retina cell nuclei using deep learning
论文作者
论文摘要
自动检测，分类，计算视网膜细胞和其他生物学物体的大小，数量和等级的能力在眼部疾病中至关重要，例如与年龄相关的黄斑变性（AMD）。在本文中，我们开发了一种基于深度学习技术和蒙版R-CNN模型的自动化工具，以分析大型传输电子显微镜图像（TEM）图像的数据集，并以高速和精度对视网膜细胞进行量化。我们考虑了外核层（ONL）细胞的三类：活，中间和杂种。我们使用24个样本的数据集训练了该模型。然后，我们使用另一组6个样本优化了超参数。这项研究的结果在应用于测试数据集后，证明我们的方法非常准确地可以自动检测，分类和计算视网膜ONL中的细胞核。使用一般指标测试了我们的模型的性能：一般平均平均精度（MAP）进行检测；以及精确，回忆，F1得分和对分类和计数的准确性。
The ability to automatically detect, classify, calculate the size, number, and grade of retinal cells and other biological objects is critically important in eye disease like age-related macular degeneration (AMD). In this paper, we developed an automated tool based on deep learning technique and Mask R-CNN model to analyze large datasets of transmission electron microscopy (TEM) images and quantify retinal cells with high speed and precision. We considered three categories for outer nuclear layer (ONL) cells: live, intermediate, and pyknotic. We trained the model using a dataset of 24 samples. We then optimized the hyper-parameters using another set of 6 samples. The results of this research, after applying to the test datasets, demonstrated that our method is highly accurate for automatically detecting, categorizing, and counting cell nuclei in the ONL of the retina. Performance of our model was tested using general metrics: general mean average precision (mAP) for detection; and precision, recall, F1-score, and accuracy for categorizing and counting.
