A1 Journal article (refereed)
AnatomySketch : An Extensible Open-Source Software Platform for Medical Image Analysis Algorithm Development (2022)

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. Journal of Digital Imaging, 35(6), 1623-1633. https://doi.org/10.1007/s10278-022-00660-5

JYU authors or editors

Publication details

All authors or editors: Zhuang, Mingrui; Chen, Zhonghua; Wang, Hongkai; Tang, Hong; He, Jiang; Qin, Bobo; Yang, Yuxin; Jin, Xiaoxian; Yu, Mengzhu; Jin, Baitao; et al.

Journal or series: Journal of Digital Imaging

ISSN: 0897-1889

eISSN: 1618-727X

Publication year: 2022

Publication date: 29/06/2022

Volume: 35

Issue number: 6

Pages range: 1623-1633

Publisher: Springer

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1007/s10278-022-00660-5

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/82356

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.

Keywords: image analysis; visualisation; medicine (science); computer programmes; algorithms; programming; artificial intelligence; human-machine systems; human-computer interaction; machine learning; deep learning

Free keywords: medical image analysis; image annotation; user interaction; algorithm development; deep learning; AnatomySketch

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

Preliminary JUFO rating: 1