Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, 69120, Heidelberg, Germany.
Heidelberg Institute for Theoretical Studies (HITS), Schloss-Wolfsbrunnenweg 35, 69118, Heidelberg, Germany.
Nat Commun. 2020 Nov 4;11(1):5577. doi: 10.1038/s41467-020-19303-w.
We present Biomedisa, a free and easy-to-use open-source online platform developed for semi-automatic segmentation of large volumetric images. The segmentation is based on a smart interpolation of sparsely pre-segmented slices taking into account the complete underlying image data. Biomedisa is particularly valuable when little a priori knowledge is available, e.g. for the dense annotation of the training data for a deep neural network. The platform is accessible through a web browser and requires no complex and tedious configuration of software and model parameters, thus addressing the needs of scientists without substantial computational expertise. We demonstrate that Biomedisa can drastically reduce both the time and human effort required to segment large images. It achieves a significant improvement over the conventional approach of densely pre-segmented slices with subsequent morphological interpolation as well as compared to segmentation tools that also consider the underlying image data. Biomedisa can be used for different 3D imaging modalities and various biomedical applications.
我们展示了 Biomedisa,这是一个免费且易于使用的开源在线平台,用于半自动分割大型容积图像。分割基于稀疏预分割切片的智能插值,同时考虑完整的基础图像数据。当可用的先验知识很少时,例如为深度神经网络的密集注释训练数据,Biomedisa 特别有价值。该平台可通过网络浏览器访问,无需复杂且繁琐的软件和模型参数配置,因此满足了没有大量计算专业知识的科学家的需求。我们证明了 Biomedisa 可以大大减少分割大型图像所需的时间和人力。与传统的密集预分割切片方法以及考虑基础图像数据的分割工具相比,它实现了显著的改进。Biomedisa 可用于不同的 3D 成像模式和各种生物医学应用。