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dc.contributor.authorZhuang, Mingrui
dc.contributor.authorChen, Zhonghua
dc.contributor.authorWang, Hongkai
dc.contributor.authorTang, Hong
dc.contributor.authorHe, Jiang
dc.contributor.authorQin, Bobo
dc.contributor.authorYang, Yuxin
dc.contributor.authorJin, Xiaoxian
dc.contributor.authorYu, Mengzhu
dc.contributor.authorJin, Baitao
dc.contributor.authorLi, Taijing
dc.contributor.authorKettunen, Lauri
dc.date.accessioned2022-07-18T09:42:01Z
dc.date.available2022-07-18T09:42:01Z
dc.date.issued2022
dc.identifier.citationZhuang, M., Chen, Z., Wang, H., Tang, H., He, J., Qin, B., Yang, Y., Jin, X., Yu, M., Jin, B., Li, T., & Kettunen, L. (2022). AnatomySketch : An Extensible Open-Source Software Platform for Medical Image Analysis Algorithm Development. <i>Journal of Digital Imaging</i>, <i>Early online</i>. <a href="https://doi.org/10.1007/s10278-022-00660-5" target="_blank">https://doi.org/10.1007/s10278-022-00660-5</a>
dc.identifier.otherCONVID_148838911
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/82356
dc.description.abstractThe development of medical image analysis algorithm is a complex process including the multiple sub-steps of model training, data visualization, human–computer interaction and graphical user interface (GUI) construction. To accelerate the development process, algorithm developers need a software tool to assist with all the sub-steps so that they can focus on the core function implementation. Especially, for the development of deep learning (DL) algorithms, a software tool supporting training data annotation and GUI construction is highly desired. In this work, we constructed AnatomySketch, an extensible open-source software platform with a friendly GUI and a flexible plugin interface for integrating user-developed algorithm modules. Through the plugin interface, algorithm developers can quickly create a GUI-based software prototype for clinical validation. AnatomySketch supports image annotation using the stylus and multi-touch screen. It also provides efficient tools to facilitate the collaboration between human experts and artificial intelligent (AI) algorithms. We demonstrate four exemplar applications including customized MRI image diagnosis, interactive lung lobe segmentation, human-AI collaborated spine disc segmentation and Annotation-by-iterative-Deep-Learning (AID) for DL model training. Using AnatomySketch, the gap between laboratory prototyping and clinical testing is bridged and the development of MIA algorithms is accelerated. The software is opened at https://github.com/DlutMedimgGroup/AnatomySketch-Software.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofseriesJournal of Digital Imaging
dc.rightsCC BY 4.0
dc.subject.othermedical image analysis
dc.subject.otherimage annotation
dc.subject.otheruser interaction
dc.subject.otheralgorithm development
dc.subject.otherdeep learning
dc.subject.otherAnatomySketch
dc.titleAnatomySketch : An Extensible Open-Source Software Platform for Medical Image Analysis Algorithm Development
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202207183920
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineLaskennallinen tiedefi
dc.contributor.oppiaineComputing, Information Technology and Mathematicsfi
dc.contributor.oppiaineSecure Communications Engineering and Signal Processingfi
dc.contributor.oppiaineComputational Scienceen
dc.contributor.oppiaineComputing, Information Technology and Mathematicsen
dc.contributor.oppiaineSecure Communications Engineering and Signal Processingen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.relation.issn0897-1889
dc.relation.volumeEarly online
dc.type.versionpublishedVersion
dc.rights.copyright© 2022 the Authors
dc.rights.accesslevelopenAccessfi
dc.subject.ysoalgoritmit
dc.subject.ysokuva-analyysi
dc.subject.ysotietokoneohjelmat
dc.subject.ysoohjelmointi
dc.subject.ysosyväoppiminen
dc.subject.ysokoneoppiminen
dc.subject.ysovisualisointi
dc.subject.ysotekoäly
dc.subject.ysoihminen-konejärjestelmät
dc.subject.ysoihmisen ja tietokoneen vuorovaikutus
dc.subject.ysolääketiede
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
jyx.subject.urihttp://www.yso.fi/onto/yso/p16870
jyx.subject.urihttp://www.yso.fi/onto/yso/p26592
jyx.subject.urihttp://www.yso.fi/onto/yso/p4887
jyx.subject.urihttp://www.yso.fi/onto/yso/p39324
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p7938
jyx.subject.urihttp://www.yso.fi/onto/yso/p2616
jyx.subject.urihttp://www.yso.fi/onto/yso/p6680
jyx.subject.urihttp://www.yso.fi/onto/yso/p38007
jyx.subject.urihttp://www.yso.fi/onto/yso/p469
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1007/s10278-022-00660-5
jyx.fundinginformationThis work was supported in part by the National Key Research and Development Program No. 2020YFB1711500, 2020YFB1711501 and 2020YFB1711503; the general program of National Natural Science Fund of China (No. 81971693, 61971445 and 61971089); Dalian City Science and Technology Innovation Funding (No. 2018J12GX042); the Fundamental Research Funds for the Central Universities (No. DUT19JC01 and DUT20YG122); the funding of Liaoning Key Lab of IC & BME System and Dalian Engineering Research Center for Artificial


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