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

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.