AimSeg: a machine-learning-aided tool for axon, inner tongue and myelin segmentation
AimSeg: a machine-learning-aided tool for axon, inner tongue and myelin segmentation
复制标题
AimSeg：用于轴突、内舌和髓磷脂分割的机器学习辅助工具
DOI：
10.1101/2023.01.02.522533
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Rondelli A
中科院分区：
文献类型：
--
作者：
Rondelli A
来自中枢和外周神经系统的轴突及其鞘鞘的电子显微镜（EM）图像用于评估髓鞘的形成、退化（脱髓鞘）和再生（髓鞘再生）。g-ratio是评估髓磷脂厚度和质量的黄金标准，传统上是通过手工测量EM图像来确定的，这是一项耗时且重复性有限的工作。这些测量在历史上也忽略了最内层的未致密髓鞘，即髓鞘舌。尽管如此，内舌已被证明对髓磷脂的生长很重要，一些研究报告说，某些条件可以引起髓磷脂的增大。忽略这一事实可能会使标准的g比分析产生偏差，而量化未压实的髓磷脂有可能为髓磷脂领域提供新的见解。在这方面，我们开发了AimSeg，一个生物图像分析工具，轴突，内舌和髓鞘分割。在机器学习分类器的帮助下，AimSeg既可以用作自动化工作流程，也可以用作用户辅助的分割工具。验证结果显示，对所有三种纤维组件进行分割的性能良好，辅助分割显示了在最小用户干预下进一步改进的潜力。与手工注释相比，这大大减少了分析时间。AimSeg还可以用于构建更大、高质量的地面真值数据集，以训练新的深度学习模型。AimSeg在斐济实施，可以使用在ilastik中训练的机器学习分类器。再加上用户友好的界面和量化未压实髓磷脂的能力，使AimSeg成为评估髓磷脂生长的独特工具。髓磷脂是由包裹在轴突周围的特殊细胞形成的，在神经的功能、保护和维持中起着重要作用。这些功能受到脱髓鞘疾病的干扰，如多发性硬化症。在这项工作中，我们提出了AimSeg，一种基于人工智能算法（机器学习）的新工具，用于评估电子显微镜图像上的髓鞘生长。虽然标准指标和以前的计算方法侧重于量化致密髓磷脂，但AimSeg还量化了内髓磷脂舌（未致密髓磷脂）。这种结构在很大程度上被忽视了，尽管它在髓磷脂生长过程中（无论是在发育过程中还是在成年大脑中）都起着重要作用，最近的研究也报道了与一些疾病相关的形态学变化。我们报告了AimSeg的性能，既可以作为全自动方法，也可以作为辅助分割工作流程，使用户能够实时管理结果，同时将人为干预减少到最低限度。因此，AimSeg作为一种新的生物图像分析工具，通过支持髓磷脂评估的标准指标和不同条件下未压实髓磷脂的量化，满足了评估髓磷脂生长的挑战。
Electron microscopy (EM) images of axons and their ensheathing myelin from both the central and peripheral nervous system are used for assessing myelin formation, degeneration (demyelination) and regeneration (remyelination). The g-ratio is the gold standard measure of assessing myelin thickness and quality, and traditionally is determined from measurements done manually from EM images – a time-consuming endeavour with limited reproducibility. These measurements have also historically neglected the innermost uncompacted myelin sheath, known as the inner myelin tongue. Nonetheless, the inner tongue has been shown to be important for myelin growth and some studies have reported that certain conditions can elicit its enlargement. Ignoring this fact may bias the standard g-ratio analysis, whereas quantifying the uncompacted myelin has the potential to provide novel insights in the myelin field. In this regard, we have developed AimSeg, a bioimage analysis tool for axon, inner tongue and myelin segmentation. Aided by machine learning classifiers trained on tissue undergoing remyelination, AimSeg can be used either as an automated workflow or as a user-assisted segmentation tool. Validation results show good performance segmenting all three fibre components, with the assisted segmentation showing the potential for further improvement with minimal user intervention. This results in a considerable reduction in time for analysis compared with manual annotation. AimSeg could also be used to build larger, high quality ground truth datasets to train novel deep learning models. Implemented in Fiji, AimSeg can use machine learning classifiers trained in ilastik. This, combined with a user-friendly interface and the ability to quantify uncompacted myelin, makes AimSeg a unique tool to assess myelin growth.Author SummaryMyelin is formed by specialised cells that wrap themselves around axons and has a major role in the function, protection, and maintenance of nerves. These functions are disturbed by demyelinating diseases, such as multiple sclerosis. In this work we present AimSeg, a new tool based on artificial intelligence algorithms (machine learning) to assess myelin growth on electron microscopy images. Whereas standard metrics and previous computational methods focus on quantifying compact myelin, AimSeg also quantifies the inner myelin tongue (uncompacted myelin). This structure has been largely overlooked despite the fact that it has an important role in the process of myelin growth (both during development and in the adult brain) and recent studies have reported morphological changes associated with some diseases. We report the performance of AimSeg, both as a fully automated approach and in an assisted segmentation workflow that enables the user to curate the results on-the-fly while reducing human intervention to the minimum. Therefore, AimSeg stands as a novel bioimage analysis tool that meets the challenges of assessing myelin growth by supporting both standard metrics for myelin evaluation and the quantification of the uncompacted myelin in different conditions.