@article {68250, title = {Using Simulated Training Data of Voxel-Level Generative Models to Improve 3D Neuron Reconstruction.}, journal = {IEEE Transactions on Medical Imaging}, volume = {PP}, year = {2022}, month = {2022 Jul 14}, abstract = {

Reconstructing neuron morphologies from fluorescence microscope images plays a critical role in neuroscience studies. It relies on image segmentation to produce initial masks either for further processing or final results to represent neuronal morphologies. This has been a challenging step due to the variation and complexity of noisy intensity patterns in neuron images acquired from microscopes. Whereas progresses in deep learning have brought the goal of accurate segmentation much closer to reality, creating training data for producing powerful neural networks is often laborious. To overcome the difficulty of obtaining a vast number of annotated data, we propose a novel strategy of using two-stage generative models to simulate training data with voxel-level labels. Trained upon unlabeled data by optimizing a novel objective function of preserving predefined labels, the models are able to synthesize realistic 3D images with underlying voxel labels. We showed that these synthetic images could train segmentation networks to obtain even better performance than manually labeled data. To demonstrate an immediate impact of our work, we further showed that segmentation results produced by networks trained upon synthetic data could be used to improve existing neuron reconstruction methods.

}, issn = {1558-254X}, doi = {10.1109/TMI.2022.3191011}, author = {Liu, Chao and Wang, Deli and Zhang, Han and Wu, Wei and Sun, Wenzhi and Zhao, Ting and Zheng, Nenggan} } @article {67905, title = {PyNeval: A Python Toolbox for Evaluating Neuron Reconstruction Performance.}, journal = {Frontiers in Neuroinformatics}, volume = {15}, year = {2021}, month = {2022 Jan 28}, pages = {767936}, abstract = {

Quality assessment of tree-like structures obtained from a neuron reconstruction algorithm is necessary for evaluating the performance of the algorithm. The lack of user-friendly software for calculating common metrics motivated us to develop a Python toolbox called PyNeval, which is the first open-source toolbox designed to evaluate reconstruction results conveniently as far as we know. The toolbox supports popular metrics in two major categories, geometrical metrics and topological metrics, with an easy way to configure custom parameters for each metric. We tested the toolbox on both synthetic data and real data to show its reliability and robustness. As a demonstration of the toolbox in real applications, we used the toolbox to improve the performance of a tracing algorithm successfully by integrating it into an optimization procedure.

}, issn = {1662-5196}, doi = {10.3389/fninf.2021.767936}, author = {Zhang, Han and Liu, Chao and Yu, Yifei and Dai, Jianhua and Zhao, Ting and Zheng, Nenggan} } @article {65107, title = {A neuronal pathway that commands deceleration in Drosophila larval light-avoidance.}, journal = {Neuroscience Bulletin}, volume = {35}, year = {2019}, month = {2019 Dec 1}, abstract = {

When facing a sudden danger or aversive condition while engaged in on-going forward motion, animals transiently slow down and make a turn to escape. The neural mechanisms underlying stimulation-induced deceleration in avoidance behavior are largely unknown. Here, we report that in Drosophila larvae, light-induced deceleration was commanded by a continuous neural pathway that included prothoracicotropic hormone neurons, eclosion hormone neurons, and tyrosine decarboxylase 2 motor neurons (the PET pathway). Inhibiting neurons in the PET pathway led to defects in light-avoidance due to insufficient deceleration and head casting. On the other hand, activation of PET pathway neurons specifically caused immediate deceleration in larval locomotion. Our findings reveal a neural substrate for the emergent deceleration response and provide a new understanding of the relationship between behavioral modules in animal avoidance responses.

}, issn = {1995-8218}, doi = {10.1007/s12264-019-00349-w}, author = {Gong, Caixia and Ouyang, Zhenhuan and Zhao, Weiqiao and Wang, Jie and Li, Kun and Zhou, Peipei and Zhao, Ting and Zheng, Nenggan and Gong, Zhefeng} }