论文标题
使用2D+3D CNN训练策略和稀疏注释的数据，对荧光显微镜中神经元体的语义分割
Semantic Segmentation of Neuronal Bodies in Fluorescence Microscopy Using a 2D+3D CNN Training Strategy with Sparsely Annotated Data
论文作者
论文摘要
人脑皮层的3D高分辨率荧光显微镜成像中神经元结构的语义分割可以利用双维CNN，从而在神经元定位中产生良好的结果，但导致表面重建不正确。另一方面，3D CNN将需要大规模手动注释的体积数据，因此需要大量的人类努力。与2D CNN相比，仅使用稀疏注释的半监督替代策略遭受较长的培训时间和实现模型的容量往往会增加容量，需要更多的基础真实数据才能获得相似的结果。为了克服这些问题，我们提出了一种在稀疏2D注释上训练本机3D CNN模型的两阶段策略，其中2D CNN模型推断出缺失的标签，并在损失计算过程中以加权方式与手动注释相结合。
Semantic segmentation of neuronal structures in 3D high-resolution fluorescence microscopy imaging of the human brain cortex can take advantage of bidimensional CNNs, which yield good results in neuron localization but lead to inaccurate surface reconstruction. 3D CNNs, on the other hand, would require manually annotated volumetric data on a large scale and hence considerable human effort. Semi-supervised alternative strategies which make use only of sparse annotations suffer from longer training times and achieved models tend to have increased capacity compared to 2D CNNs, needing more ground truth data to attain similar results. To overcome these issues we propose a two-phase strategy for training native 3D CNN models on sparse 2D annotations where missing labels are inferred by a 2D CNN model and combined with manual annotations in a weighted manner during loss calculation.
