Emily C Marron, Jonathan Backues, Andrew M Ross, Steven K Backues
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引用次数: 0
摘要
在酵母 TEM 图像中分割自噬体是测量自噬体大小和数量变化以更好地了解大自噬/自噬的一项关键技术。手动分割这些图像非常耗时,尤其是因为需要数百张图像才能进行精确测量。在这里,我们描述了一个经过验证的 Cellpose 2.0 模型,它能以与人类专家相当的准确度分割这些图像。该模型可用于全自动分割,无需人工勾画身体轮廓;也可用于模型辅助分割,允许人工监督,但速度仍是目前人工方法的五倍。该模型专门用于酵母 TEM 图像中自噬体的分割,但在其他系统中工作的研究人员也可以使用类似的方法生成自己的 Cellpose 2.0 模型,尝试自动分割。我们的模型及其使用说明在此介绍给自噬群体:缩写:AB,自噬体;AvP,平均精确度;GUI,图形用户界面;IoU,交叉联合;MVB,多囊体;ROI,感兴趣区;TEM,透射电子显微镜;WT,野生型。
Accurate automated segmentation of autophagic bodies in yeast vacuoles using cellpose 2.0.
Segmenting autophagic bodies in yeast TEM images is a key technique for measuring changes in autophagosome size and number in order to better understand macroautophagy/autophagy. Manual segmentation of these images can be very time consuming, particularly because hundreds of images are needed for accurate measurements. Here we describe a validated Cellpose 2.0 model that can segment these images with accuracy comparable to that of human experts. This model can be used for fully automated segmentation, eliminating the need for manual body outlining, or for model-assisted segmentation, which allows human oversight but is still five times as fast as the current manual method. The model is specific to segmentation of autophagic bodies in yeast TEM images, but researchers working in other systems can use a similar process to generate their own Cellpose 2.0 models to attempt automated segmentations. Our model and instructions for its use are presented here for the autophagy community.Abbreviations: AB, autophagic body; AvP, average precision; GUI, graphical user interface; IoU, intersection over union; MVB, multivesicular body; ROI, region of interest; TEM, transmission electron microscopy; WT,wild type.