瑞士洛桑联邦理工学院Sahand Jamal Rahi研究组利用深度学习和定向增强技术自动追踪移动和变形中线虫内的神经元。相关论文于2023年12月5日在线发表于国际学术期刊《自然—方法学》。

研究人员表示，从三维（3D）功能成像中读出神经元活动需要分割和追踪单个神经元。在行动的动物身上，如果大脑移动和变形，这就具有挑战性。传统的方法是利用代表不同大脑姿态的图像的地面实况（GT）注释来训练卷积神经网络。对于三维图像来说，这种方法非常耗费人力。

研究人员引入了“目标增强”，这是一种从少量手动注释自动合成人工注释的方法。这个方法（“Targettrack”）可学习大脑的内部变形，通过变形GT注释来合成新姿势的注释。这就减少了人工标注和校对的需要。图形用户界面允许端到端应用该方法。研究人员在神经元被标记为关键点或三维体积的记录上演示了Targettrack。通过分析暴露在气味脉冲下自由移动的动物，研究人员发现了神经元间动态的丰富模式，包括神经元夹带的开关。

附：英文原文

Title: Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation

Author: Park, Core Francisco, Barzegar-Keshteli, Mahsa, Korchagina, Kseniia, Delrocq, Ariane, Susoy, Vladislav, Jones, Corinne L., Samuel, Aravinthan D. T., Rahi, Sahand Jamal

Issue&Volume: 2023-12-05

Abstract: Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convolutional neural network with ground-truth (GT) annotations of images representing different brain postures. For 3D images, this is very labor intensive. We introduce ‘targeted augmentation’, a method to automatically synthesize artificial annotations from a few manual annotations. Our method (‘Targettrack’) learns the internal deformations of the brain to synthesize annotations for new postures by deforming GT annotations. This reduces the need for manual annotation and proofreading. A graphical user interface allows the application of the method end-to-end. We demonstrate Targettrack on recordings where neurons are labeled as key points or 3D volumes. Analyzing freely moving animals exposed to odor pulses, we uncover rich patterns in interneuron dynamics, including switching neuronal entrainment on and off.

DOI: 10.1038/s41592-023-02096-3

Source: https://www.nature.com/articles/s41592-023-02096-3