The accurate tracking of live cells using video microscopy recordings remains
a challenging task for popular state-of-the-art image processing based object
tracking methods. In recent years, several existing and new applications have
attempted to integrate deep-learning based frameworks for this task, but most
of them still heavily rely on consecutive frame based tracking embedded in
their architecture or other premises that hinder generalized learning. To
address this issue, we aimed to develop a new deep-learning based tracking
method that relies solely on the assumption that cells can be tracked based on
their spatio-temporal neighborhood, without restricting it to consecutive
frames. The proposed method has the additional benefit that the motion patterns
of the cells can be learned completely by the predictor without any prior
assumptions, and it has the potential to handle a large number of video frames
with heavy artifacts. The efficacy of the proposed method is demonstrated
through multiple biologically motivated validation strategies and compared
against several state-of-the-art cell tracking methods.

基于深度学习的细胞追踪方法，通过细胞的时空领域实现准确追踪，并能学习细胞的运动模式，适用于处理大量带有重要特征的视频帧。

利用时间对称的深度学习方法增强细胞跟踪

Enhancing Cell Tracking with a Time-Symmetric Deep Learning Approach

Remotely sensed data are sparse, which means that data have missing values,
for instance due to cloud cover. This is problematic for applications and
signal processing algorithms that require complete data sets. To address the
sparse data issue, we present a new gap-fill algorithm. The proposed method
predicts each missing value separately based on data points in a
spatio-temporal neighborhood around the missing data point. The computational
workload can be distributed among several computers, making the method suitable
for large datasets. The prediction of the missing values and the estimation of
the corresponding prediction uncertainties are based on sorting procedures and
quantile regression. The algorithm was applied to MODIS NDVI data from Alaska
and tested with realistic cloud cover scenarios featuring up to 50% missing
data. Validation against established software showed that the proposed method
has a good performance in terms of the root mean squared prediction error. The
procedure is implemented and available in the open-source R package gapfill. We
demonstrate the software performance with a real data example and show how it
can be tailored to specific data. Due to the flexible software design, users
can control and redesign major parts of the procedure with little effort. This
makes it an interesting tool for gap-filling satellite data and for the future
development of gap-fill procedures.

本文介绍了一种新的基于排序程序和分位数回归的遥感数据缺失值填充算法，该算法在 MODIS NDVI 数据上经过了测试并证明了其预测性能良好。