Institut de Mathématiques de Toulouse (IMT), Université de Toulouse, Toulouse, France.
Imactiv-3D. Centre Pierre Potier, 1 place Pierre Potier, 31100, Toulouse, France.
Sci Rep. 2024 May 21;14(1):11604. doi: 10.1038/s41598-024-60916-8.
We present Svetlana (SuperVised sEgmenTation cLAssifier for NapAri), an open-source Napari plugin dedicated to the manual or automatic classification of segmentation results. A few recent software tools have made it possible to automatically segment complex 2D and 3D objects such as cells in biology with unrivaled performance. However, the subsequent analysis of the results is oftentimes inaccessible to non-specialists. The Svetlana plugin aims at going one step further, by allowing end-users to label the segmented objects and to pick, train and run arbitrary neural network classifiers. The resulting network can then be used for the quantitative analysis of biophysical phenoma. We showcase its performance through challenging problems in 2D and 3D and provide a comprehensive discussion on its strengths and limits.
我们介绍 Svetlana（用于 Napari 的监督分割分类器），这是一个开源的 Napari 插件，专门用于手动或自动分类分割结果。一些最近的软件工具已经使得用无与伦比的性能自动分割生物学中复杂的 2D 和 3D 物体（如细胞）成为可能。然而，结果的后续分析往往是无法访问的非专业人士。Svetlana 插件旨在更进一步，允许最终用户标记分割的对象，并选择、训练和运行任意神经网络分类器。然后，该网络可用于生物物理现象的定量分析。我们通过 2D 和 3D 中的挑战性问题展示了它的性能，并对其优缺点进行了全面的讨论。
