使用 cellpose 2.0 对酵母液泡中的自噬体进行精确的自动分割。

Autophagy Pub Date : 2024-09-01 Epub Date: 2024-05-18 DOI:10.1080/15548627.2024.2353458
Emily C Marron, Jonathan Backues, Andrew M Ross, Steven K Backues
{"title":"使用 cellpose 2.0 对酵母液泡中的自噬体进行精确的自动分割。","authors":"Emily C Marron, Jonathan Backues, Andrew M Ross, Steven K Backues","doi":"10.1080/15548627.2024.2353458","DOIUrl":null,"url":null,"abstract":"<p><p>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.<b>Abbreviations:</b> 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.</p>","PeriodicalId":93893,"journal":{"name":"Autophagy","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11346527/pdf/","citationCount":"0","resultStr":"{\"title\":\"Accurate automated segmentation of autophagic bodies in yeast vacuoles using cellpose 2.0.\",\"authors\":\"Emily C Marron, Jonathan Backues, Andrew M Ross, Steven K Backues\",\"doi\":\"10.1080/15548627.2024.2353458\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.<b>Abbreviations:</b> 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.</p>\",\"PeriodicalId\":93893,\"journal\":{\"name\":\"Autophagy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11346527/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Autophagy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/15548627.2024.2353458\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/5/18 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Autophagy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/15548627.2024.2353458","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/18 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A PRKN-independent mechanism regulating cardiac mitochondrial quality control. PINK1-deficiency facilitates mitochondrial iron accumulation and colon tumorigenesis. Deciphering melanophagy: role of the PTK2-ITCH-MLANA-OPTN cascade on melanophagy in melanocytes. HSP90 N-terminal inhibition promotes mitochondria-derived vesicles related metastasis by reducing TFEB transcription via decreased HSP90AA1-HCFC1 interaction in liver cancer. Efficient PHB2 (prohibitin 2) exposure during mitophagy depends on VDAC1 (voltage dependent anion channel 1).
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1