{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:12:02Z","timestamp":1740136322172,"version":"3.37.3"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T00:00:00Z","timestamp":1714694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100001818","name":"Research to Prevent Blindness","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001818","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["5R01EY029313-05"],"award-info":[{"award-number":["5R01EY029313-05"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Automated image acquisition can significantly improve the throughput of serial section scanning electron microscopy (ssSEM). However, image quality can vary from image to image depending on autofocusing and beam stigmation. Automatically evaluating the quality of images is, therefore, important for efficiently generating high-quality serial section scanning electron microscopy (ssSEM) datasets. We tested several convolutional neural networks for their ability to reproduce user-generated evaluations of ssSEM image quality. We found that a modification of ResNet-50 that we term quality evaluation Network (QEN) reliably predicts user-generated quality scores. Running QEN in parallel to ssSEM image acquisition therefore allows users to quickly identify imaging problems and flag images for retaking. We have publicly shared the Python code for evaluating images with QEN, the code for training QEN, and the training dataset.<\/jats:p>","DOI":"10.1093\/mam\/ozae033","type":"journal-article","created":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T17:52:06Z","timestamp":1714758726000},"page":"501-507","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating the Quality of Serial EM Sections with Deep Learning"],"prefix":"10.1093","volume":"30","author":[{"given":"Mahsa","family":"Bank Tavakoli","sequence":"first","affiliation":[{"name":"Department of Ophthalmology and Visual Sciences, Washington University in St. Louis , Euclid Ave., St. Louis, MO 63110 , USA"},{"name":"Department of Neuroscience, Washington University in St. Louis , 660 S. Euclid Ave., St. Louis, MO 63110 , USA"},{"name":"Department of Biomedical Engineering, Washington University in St. Louis , 1 Brookings Drive, St. Louis, MO 63130 , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2657-2416","authenticated-orcid":false,"given":"Josh L","family":"Morgan","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology and Visual Sciences, Washington University in St. Louis , Euclid Ave., St. Louis, MO 63110 , USA"},{"name":"Department of Neuroscience, Washington University in St. Louis , 660 S. Euclid Ave., St. Louis, MO 63110 , USA"},{"name":"Department of Biomedical Engineering, Washington University in St. Louis , 1 Brookings Drive, St. Louis, MO 63130 , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,5,3]]},"reference":[{"year":"2017","author":"Cheng","key":"2024070417214791200_ozae033-B1"},{"year":"2020","author":"Cui","key":"2024070417214791200_ozae033-B2"},{"key":"2024070417214791200_ozae033-B3","first-page":"215","volume-title":"Medical Image Quality Assessment","author":"Ding","year":"2018"},{"key":"2024070417214791200_ozae033-B4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fncir.2022.753496","article-title":"Reconstructing neural circuits using multiresolution correlated light and electron microscopy","volume":"16","author":"Friedrichsen","year":"2022","journal-title":"Front Neural Circuits"},{"key":"2024070417214791200_ozae033-B5","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.patcog.2018.04.016","article-title":"Blind image quality prediction by exploiting multi-level deep representations","volume":"81","author":"Gao","year":"2018","journal-title":"Pattern Recognit"},{"year":"2020","author":"Hao","key":"2024070417214791200_ozae033-B6"},{"key":"2024070417214791200_ozae033-B7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fncir.2014.00068","article-title":"Imaging ATUM ultrathin section libraries with wafermapper: A multi-scale approach to EM reconstruction of neural circuits","volume":"8","author":"Hayworth","year":"2014","journal-title":"Front Neural Circuits"},{"year":"2015","author":"He","key":"2024070417214791200_ozae033-B8"},{"year":"2016","author":"He","key":"2024070417214791200_ozae033-B9"},{"key":"2024070417214791200_ozae033-B10","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1109\/TNNLS.2014.2336852","article-title":"Blind image quality assessment via deep learning","volume":"26","author":"Hou","year":"2015","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2024070417214791200_ozae033-B11","first-page":"1","article-title":"Autokeras: An automl library for deep learning","volume":"24","author":"Jin","year":"2023","journal-title":"J Mach Learn Res"},{"year":"2014","author":"Kang","key":"2024070417214791200_ozae033-B12"},{"year":"2018","author":"Kim","key":"2024070417214791200_ozae033-B13"},{"key":"2024070417214791200_ozae033-B14","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/TNNLS.2018.2829819","article-title":"Deep CNN-based blind image quality predictor","volume":"30","author":"Kim","year":"2019","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"year":"2017","author":"Kingma","key":"2024070417214791200_ozae033-B15"},{"key":"2024070417214791200_ozae033-B16","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1017\/S1431927612004710","article-title":"ATUM-based SEM for high-speed large-volume biological reconstructions","volume":"18","author":"Schalek","year":"2012","journal-title":"Microsc Microanal"},{"year":"2015","author":"Srivastava","key":"2024070417214791200_ozae033-B17"},{"year":"2016","author":"Sun","key":"2024070417214791200_ozae033-B18"},{"key":"2024070417214791200_ozae033-B19","doi-asserted-by":"crossref","first-page":"123788","DOI":"10.1109\/ACCESS.2019.2938900","article-title":"A survey of DNN methods for blind image quality assessment","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"2024070417214791200_ozae033-B20","first-page":"1","article-title":"CNN-based medical ultrasound image quality assessment","volume":"2021","author":"Zhang","year":"2021","journal-title":"Complexity"}],"container-title":["Microscopy and Microanalysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/mam\/article-pdf\/30\/3\/501\/58442740\/ozae033.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/mam\/article-pdf\/30\/3\/501\/58442740\/ozae033.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T17:25:49Z","timestamp":1720113949000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/mam\/article\/30\/3\/501\/7664316"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,3]]},"references-count":20,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,5,3]]},"published-print":{"date-parts":[[2024,7,4]]}},"URL":"https:\/\/doi.org\/10.1093\/mam\/ozae033","relation":{},"ISSN":["1431-9276","1435-8115"],"issn-type":[{"type":"print","value":"1431-9276"},{"type":"electronic","value":"1435-8115"}],"subject":[],"published-other":{"date-parts":[[2024,6]]},"published":{"date-parts":[[2024,5,3]]}}}