Automated Euler number of the alveolar capillary network based on deep learning segmentation with verification by stereological methods

IF 1.5 4区 工程技术 Q3 MICROSCOPY
Julia Schmidt, Jonas Labode, Christoph Wrede, Yannick Regin, Jaan Toelen, Christian Mühlfeld
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Abstract

Diseases like bronchopulmonary dysplasia (BPD) affect the development of the pulmonary vasculature, including the alveolar capillary network (ACN). Since pulmonary development is highly dependent on angiogenesis and microvascular maturation, ACN investigations are essential. Therefore, efficient methods are needed for quantitative comparative studies. Here, the suitability of deep learning (DL) for processing serial block-face scanning electron microscopic (SBF-SEM) data by generating ACN segmentations, 3D reconstructions and performing automated quantitative analyses based on them, was tested. Since previous studies revealed inefficient ACN segmentation as the limiting factor in the overall workflow, a 2D DL-based approach was used with existing data, aiming at the reduction of necessary manual interaction. Automated quantitative analyses based on completed segmentations were performed subsequently. The results were compared to stereological estimations, assessing segmentation quality and result reliability. It was shown that the DL-based approach was suitable for generating segmentations on SBF-SEM data. This approach generated more complete initial ACN segmentations than an established method, despite the limited amount of available training data and the use of a 2D rather than a 3D approach. The quality of the completed ACN segmentations was assessed as sufficient. Furthermore, quantitative analyses delivered reliable results about the ACN architecture, automatically obtained contrary to manual stereological approaches. This study demonstrated that ACN segmentation is still the part of the overall workflow that requires improvement regarding the reduction of manual interaction to benefit from available automated software tools. Nevertheless, the results indicated that it could be advantageous taking further efforts to implement a 3D DL-based segmentation approach. As the amount of analysed data was limited, this study was not conducted to obtain representative data about BPD-induced ACN alterations, but to highlight next steps towards a fully automated segmentation and evaluation workflow, enabling larger sample sizes and representative studies.

Abstract Image

基于深度学习分割和立体方法验证的肺泡毛细血管网络自动欧拉数。
支气管肺发育不良(BPD)等疾病影响肺血管系统的发育,包括肺泡毛细血管网(ACN)。由于肺部发育高度依赖于血管生成和微血管成熟,ACN调查是必要的。因此,需要有效的方法进行定量比较研究。在这里,深度学习(DL)通过生成ACN分割、3D重建和基于它们进行自动定量分析来处理连续块面扫描电镜(SBF-SEM)数据的适用性进行了测试。由于之前的研究表明,ACN分割效率低下是整个工作流程中的限制因素,因此我们对现有数据使用了基于2D dl的方法,旨在减少必要的人工交互。随后,在完成分割的基础上进行自动定量分析。将结果与立体估计进行比较,评估分割质量和结果可靠性。结果表明,基于dl的方法可以很好地对SBF-SEM数据进行分割。尽管可用的训练数据数量有限,而且使用的是2D而不是3D方法,但这种方法产生的初始ACN分割比现有方法更完整。已完成的ACN分割的质量被评估为足够。此外,定量分析提供了关于ACN体系结构的可靠结果,自动获得与手动立体方法相反的结果。这项研究表明,ACN分割仍然是整个工作流程的一部分,需要改进,以减少人工交互,从而从可用的自动化软件工具中获益。然而,结果表明,进一步努力实现基于3D dl的分割方法可能是有利的。由于分析的数据量有限,本研究不是为了获得bpd引起的ACN改变的代表性数据,而是为了强调实现全自动分割和评估工作流的下一步工作,从而实现更大的样本量和代表性研究。
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来源期刊
Journal of microscopy
Journal of microscopy 工程技术-显微镜技术
CiteScore
4.30
自引率
5.00%
发文量
83
审稿时长
1 months
期刊介绍: The Journal of Microscopy is the oldest journal dedicated to the science of microscopy and the only peer-reviewed publication of the Royal Microscopical Society. It publishes papers that report on the very latest developments in microscopy such as advances in microscopy techniques or novel areas of application. The Journal does not seek to publish routine applications of microscopy or specimen preparation even though the submission may otherwise have a high scientific merit. The scope covers research in the physical and biological sciences and covers imaging methods using light, electrons, X-rays and other radiations as well as atomic force and near field techniques. Interdisciplinary research is welcome. Papers pertaining to microscopy are also welcomed on optical theory, spectroscopy, novel specimen preparation and manipulation methods and image recording, processing and analysis including dynamic analysis of living specimens. Publication types include full papers, hot topic fast tracked communications and review articles. Authors considering submitting a review article should contact the editorial office first.
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