dc.contributor.author | Zhuang, Mingrui | |
dc.contributor.author | Chen, Zhonghua | |
dc.contributor.author | Wang, Hongkai | |
dc.contributor.author | Tang, Hong | |
dc.contributor.author | He, Jiang | |
dc.contributor.author | Qin, Bobo | |
dc.contributor.author | Yang, Yuxin | |
dc.contributor.author | Jin, Xiaoxian | |
dc.contributor.author | Yu, Mengzhu | |
dc.contributor.author | Jin, Baitao | |
dc.contributor.author | Li, Taijing | |
dc.contributor.author | Kettunen, Lauri | |
dc.date.accessioned | 2022-07-18T09:42:01Z | |
dc.date.available | 2022-07-18T09:42:01Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Zhuang, 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>35</i>(6), 1623-1633. <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.other | CONVID_148838911 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/82356 | |
dc.description.abstract | The 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.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Springer | |
dc.relation.ispartofseries | Journal of Digital Imaging | |
dc.rights | CC BY 4.0 | |
dc.subject.other | medical image analysis | |
dc.subject.other | image annotation | |
dc.subject.other | user interaction | |
dc.subject.other | algorithm development | |
dc.subject.other | deep learning | |
dc.subject.other | AnatomySketch | |
dc.title | AnatomySketch : An Extensible Open-Source Software Platform for Medical Image Analysis Algorithm Development | |
dc.type | article | |
dc.identifier.urn | URN:NBN:fi:jyu-202207183920 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Laskennallinen tiede | fi |
dc.contributor.oppiaine | Computing, Information Technology and Mathematics | fi |
dc.contributor.oppiaine | Secure Communications Engineering and Signal Processing | fi |
dc.contributor.oppiaine | Computational Science | en |
dc.contributor.oppiaine | Computing, Information Technology and Mathematics | en |
dc.contributor.oppiaine | Secure Communications Engineering and Signal Processing | en |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 1623-1633 | |
dc.relation.issn | 0897-1889 | |
dc.relation.numberinseries | 6 | |
dc.relation.volume | 35 | |
dc.type.version | publishedVersion | |
dc.rights.copyright | © 2022 the Authors | |
dc.rights.accesslevel | openAccess | fi |
dc.subject.yso | algoritmit | |
dc.subject.yso | visualisointi | |
dc.subject.yso | ihminen-konejärjestelmät | |
dc.subject.yso | lääketiede | |
dc.subject.yso | kuva-analyysi | |
dc.subject.yso | tietokoneohjelmat | |
dc.subject.yso | ohjelmointi | |
dc.subject.yso | syväoppiminen | |
dc.subject.yso | koneoppiminen | |
dc.subject.yso | tekoäly | |
dc.subject.yso | ihmisen ja tietokoneen vuorovaikutus | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p14524 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p7938 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p6680 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p469 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p16870 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p26592 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p4887 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p39324 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p21846 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p2616 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p38007 | |
dc.rights.url | https://creativecommons.org/licenses/by/4.0/ | |
dc.relation.doi | 10.1007/s10278-022-00660-5 | |
jyx.fundinginformation | This 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 | |
dc.type.okm | A1 | |