Web-based AI system for medical image segmentation
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Image segmentation is a crucial step in the diagnosis of brain tumours, and machine learning has emerged as a promising tool for tumour characterisation from medical imaging data. Despite their enormous potential in automatic segmentation of brain tumours from complex MRI scans, the implementation and use of machine learning algorithms can often present practical challenges to medical imaging
researchers. This paper introduces a web-based GUI application designed to integrate all the components needed in deep learning workflows, allowing medical imaging researchers to seamlessly train and infer on data stored on in-house servers or on local machines. Our platform simplifies the process of training and inferring on MRI data using state-of-the-art models, supports integration with XNAT servers, and incorporates powerful tools for visualizing inference results.
researchers. This paper introduces a web-based GUI application designed to integrate all the components needed in deep learning workflows, allowing medical imaging researchers to seamlessly train and infer on data stored on in-house servers or on local machines. Our platform simplifies the process of training and inferring on MRI data using state-of-the-art models, supports integration with XNAT servers, and incorporates powerful tools for visualizing inference results.
Date Issued
2023-12-02
Date Acceptance
2023-05-29
Citation
Medical Image Understanding and Analysis, 2023, 14122
ISBN
978-3-031-48593-0
Publisher
Springer
Journal / Book Title
Medical Image Understanding and Analysis
Volume
14122
Copyright Statement
This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-48593-0_17. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms
For the
purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.
For the
purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.
License URL
Identifier
https://link.springer.com/chapter/10.1007/978-3-031-48593-0_17
Source
27th Conference on Medical Image Understanding and Analysis (MIUA 2023)
Publication Status
Published
Start Date
2023-07-19
Finish Date
2023-07-21
Coverage Spatial
Aberdeen, UK
Date Publish Online
2023-12-02