3D cell tracking in a living organism has a crucial role in live cell image
analysis. Cell tracking in C. elegans has two difficulties. First, cell
migration in a consecutive frame is large since they move their head during
scanning. Second, cell detection is often inconsistent in consecutive frames
due to touching cells and low-contrast images, and these inconsistent
detections affect the tracking performance worse. In this paper, we propose a
cell tracking method to address these issues, which has two main contributions.
First, we introduce cell position heatmap-based non-rigid alignment with
test-time fine-tuning, which can warp the detected points to near the positions
at the next frame. Second, we propose a pairwise detection method, which uses
the information of detection results at the previous frame for detecting cells
at the current frame. The experimental results demonstrate the effectiveness of
each module, and the proposed method achieved the best performance in
comparison.

本论文提出了一种基于细胞位置热图的非刚性对齐和基于前一帧检测结果的成对检测方法，用于解决 C. elegans 中细胞追踪问题，在实验中展示了每个模块的有效性，并取得了最佳性能。

基于细胞位置热图对矮线虫的细胞跟踪与一对一检测

Cell Tracking in C. elegans with Cell Position Heatmap-Based Alignment  and Pairwise Detection

Determining cell identities in imaging sequences is an important yet
challenging task. The conventional method for cell identification is via cell
tracking, which is complex and can be time-consuming. In this study, we propose
an innovative approach to cell identification during early C. elegans
embryogenesis using machine learning. We employed random forest, MLP, and LSTM
models, and tested cell classification accuracy on 3D time-lapse confocal
datasets spanning the first 4 hours of embryogenesis. By leveraging a small
number of spatial-temporal features of individual cells, including cell
trajectory and cell fate information, our models achieve an accuracy of over
90%, even with limited data. We also determine the most important feature
contributions and can interpret these features in the context of biological
knowledge. Our research demonstrates the success of predicting cell identities
in 4D imaging sequences directly from simple spatio-temporal features.

利用机器学习方法，我们提出了一种创新的方法来在早期 C. elegans 胚胎发育期间对细胞进行识别，通过利用细胞轨迹和细胞命运等少量时间空间特征，我们的模型能够在有限数据下实现 90% 以上的准确率，并能解释最重要的特征对生物知识的意义。

基于轨迹和细胞命运信息的细胞身份预测

Prediction of Cellular Identities from Trajectory and Cell Fate  Information

Grand efforts in neuroscience are working toward mapping the connectomes of
many new species, including the near completion of the Drosophila melanogaster.
It is important to ask whether these models could benefit artificial
intelligence. In this work we ask two fundamental questions: (1) where and when
biological connectomes can provide use in machine learning, (2) which design
principles are necessary for extracting a good representation of the
connectome. Toward this end, we translate the motor circuit of the C. Elegans
nematode into artificial neural networks at varying levels of biophysical
realism and evaluate the outcome of training these networks on motor and
non-motor behavioral tasks. We demonstrate that biophysical realism need not be
upheld to attain the advantages of using biological circuits. We also establish
that, even if the exact wiring diagram is not retained, the architectural
statistics provide a valuable prior. Finally, we show that while the C. Elegans
locomotion circuit provides a powerful inductive bias on locomotion problems,
its structure may hinder performance on tasks unrelated to locomotion such as
visual classification problems.

本文通过将矛线虫的运动回路转化为具有不同生理真实性水平的人工神经网络，评估这些网络在动态和非动态行为任务上的训练结果。研究表明，即使不保持生物特性的真实性，也可以获得使用生物电路的优势。建立生物电路结构的统计学提供了有价值的先验知识。同时，矛线虫的运动回路对于运动问题具有强大的归纳偏差，但其结构可能会妨碍其他与运动无关的任务。