ACORBA:测量拟南芥根尖角动态的自动化工作流程。

Nelson B C Serre, Matyáš Fendrych
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引用次数: 4

摘要

许多实验室都在研究植物感知重力并使其根系生长朝向重力方向的能力。众所周知,人工分析图像数据容易受到人为偏见的影响。有几种半自动工具可用于分析平板扫描仪的图像,但没有解决方案可以自动测量垂直级显微镜图像的根弯曲角度随时间的变化。为了解决这些问题,我们开发了ACORBA,这是一种自动化软件,可以通过垂直显微镜和平板扫描仪的图像测量根部弯曲角度随时间的变化。ACORBA也有半自动化模式的相机或立体显微镜图像。它代表了一种基于传统图像处理和深度机器学习分割的灵活方法来测量根角随时间的变化。由于软件是自动化的,它限制了人类的互动,并且是可复制的。ACORBA将通过减少劳动和提高根系向倾斜性图像分析的可重复性来支持植物生物学家社区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

ACORBA: Automated workflow to measure <i>Arabidopsis thaliana</i> root tip angle dynamics.

ACORBA: Automated workflow to measure <i>Arabidopsis thaliana</i> root tip angle dynamics.

ACORBA: Automated workflow to measure <i>Arabidopsis thaliana</i> root tip angle dynamics.

ACORBA: Automated workflow to measure Arabidopsis thaliana root tip angle dynamics.
Plants respond to the surrounding environment in countless ways. One of these responses is their ability to sense and orient their root growth toward the gravity vector. Root gravitropism is studied in many laboratories as a hallmark of auxin-related phenotypes. However, manual analysis of images and microscopy data is known to be subjected to human bias. This is particularly the case for manual measurements of root bending as the selection lines to calculate the angle are set subjectively. Therefore, it is essential to develop and use automated or semi-automated image analysis to produce reproducible and unbiased data. Moreover, the increasing usage of vertical-stage microscopy in plant root biology yields gravitropic experiments with an unprecedented spatiotemporal resolution. To this day, there is no available solution to measure root bending angle over time for vertical-stage microscopy. To address these problems, we developed ACORBA (Automatic Calculation Of Root Bending Angles), a fully automated software to measure root bending angle over time from vertical-stage microscope and flatbed scanner images. Moreover, the software can be used semi-automated for camera, mobile phone or stereomicroscope images. ACORBA represents a flexible approach based on both traditional image processing and deep machine learning segmentation to measure root angle progression over time. By its automated nature, the workflow is limiting human interactions and has high reproducibility. ACORBA will support the plant biologist community by reducing time and labor and by producing quality results from various kinds of inputs. Significance statement ACORBA is implementing an automated and semi-automated workflow to quantify root bending and waving angles from images acquired with a microscope, a scanner, a stereomicroscope or a camera. It will support the plant biology community by reducing time and labor and by producing trustworthy and reproducible quantitative data.
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