Open-source software platform for medical image segmentation applications

17 November 2017 Open-source software platform for medical image segmentation applications

R. Namías, J. P. D'Amato, M. del Fresno

Proceedings Volume 10572, 13th International Conference on Medical Information Processing and Analysis; 105721J (2017) https://doi.org/10.1117/12.2283487
Event: 13th International Symposium on Medical Information Processing and Analysis, 2017, San Andres Island, Colombia

Abstract

Segmenting 2D and 3D images is a crucial and challenging problem in medical image analysis. Although several image segmentation algorithms have been proposed for different applications, no universal method currently exists. Moreover, their use is usually limited when detection of complex and multiple adjacent objects of interest is needed. In addition, the continually increasing volumes of medical imaging scans require more efficient segmentation software design and highly usable applications. In this context, we present an extension of our previous segmentation framework which allows the combination of existing explicit deformable models in an efficient and transparent way, handling simultaneously different segmentation strategies and interacting with a graphic user interface (GUI). We present the object-oriented design and the general architecture which consist of two layers: the GUI at the top layer, and the processing core filters at the bottom layer. We apply the framework for segmenting different real-case medical image scenarios on public available datasets including bladder and prostate segmentation from 2D MRI, and heart segmentation in 3D CT. Our experiments on these concrete problems show that this framework facilitates complex and multi-object segmentation goals while providing a fast prototyping open-source segmentation tool.

Citation Download Citation

R. Namías, J. P. D'Amato, and M. del Fresno "Open-source software platform for medical image segmentation applications", Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 105721J (17 November 2017); https://doi.org/10.1117/12.2283487

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