论文标题
使用不完整注释的细胞超微结构的神经网络分割
Neural Network Segmentation of Cell Ultrastructure Using Incomplete Annotation
论文作者
论文摘要
胰腺β细胞是糖尿病研究中的重要目标。对于β细胞超微结构的可扩展建模，我们研究了通过软X射线断层扫描获得的全细胞成像数据的自动分割。在研究过程中，针对数据的不同子集手动生产了完整和部分超微结构注释。为了更有效地使用现有注释，我们提出了一种方法，该方法可以将部分标记的数据应用于完整标签分段。对于实验验证，我们应用方法来培训具有12个完全注释的数据和12个部分注释数据的卷积神经网络，并显示出对仅使用完全注释数据的标准培训的有希望的改进。
The Pancreatic beta cell is an important target in diabetes research. For scalable modeling of beta cell ultrastructure, we investigate automatic segmentation of whole cell imaging data acquired through soft X-ray tomography. During the course of the study, both complete and partial ultrastructure annotations were produced manually for different subsets of the data. To more effectively use existing annotations, we propose a method that enables the application of partially labeled data for full label segmentation. For experimental validation, we apply our method to train a convolutional neural network with a set of 12 fully annotated data and 12 partially annotated data and show promising improvement over standard training that uses fully annotated data alone.
