DeepProjection: Specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning

The efficient extraction of image data from curved tissue sheets embedded in volumetric imaging data remains a serious and unsolved problem in quantitative studies of embryogenesis. Here we present DeepProjection (DP), a trainable projection algorithm based on deep learning. This algorithm is trained on user-generated training data to locally classify the 3D stack content and rapidly and robustly predict binary masks containing the target content, e.g., tissue boundaries, while masking highly fluorescent out-of-plane artifacts. A projection of the masked 3D stack then yields background-free 2D images with undistorted fluorescence intensity values. The binary masks can further be applied to other fluorescent channels or to extract the local tissue curvature. DP is designed as a first processing step than can be followed, for example, by segmentation to track cell fate. We apply DP to follow the dynamic movements of 2D-tissue sheets during dorsal closure in Drosophila embryos and of the p...

在胚胎发生的定量研究中，从体成像数据所嵌入的弯曲组织薄片内高效提取图像数据，仍是一个尚未攻克的重大科学难题。本文提出了基于深度学习的可训练投影算法DeepProjection（DP）。该算法可依托用户生成的训练数据完成训练，对三维堆叠图像的局部内容进行分类，并快速且稳健地预测包含目标内容（如组织边界）的二值掩码，同时遮蔽强荧光的平面外伪影。对掩码后的三维堆叠图像执行投影操作，即可得到无背景干扰、荧光强度值未发生畸变的二维图像。该二值掩码还可进一步应用于其他荧光通道，或用于提取局部组织的曲率信息。DP被设计为首个处理步骤，后续可衔接细胞命运追踪所需的图像分割等流程。我们将DP应用于追踪果蝇胚胎背闭合过程中二维组织薄片的动态运动，以及[原文未完整展示内容]