22 December 2015 WebMedSA: a web-based framework for segmenting and annotating medical images using biomedical ontologies
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Proceedings Volume 9681, 11th International Symposium on Medical Information Processing and Analysis; 968110 (2015) https://doi.org/10.1117/12.2214324
Event: 11th International Symposium on Medical Information Processing and Analysis (SIPAIM 2015), 2015, Cuenca, Ecuador
Abstract
Advances in medical imaging have fostered medical diagnosis based on digital images. Consequently, the number of studies by medical images diagnosis increases, thus, collaborative work and tele-radiology systems are required to effectively scale up to this diagnosis trend. We tackle the problem of the collaborative access of medical images, and present WebMedSA, a framework to manage large datasets of medical images. WebMedSA relies on a PACS and supports the ontological annotation, as well as segmentation and visualization of the images based on their semantic description. Ontological annotations can be performed directly on the volumetric image or at different image planes (e.g., axial, coronal, or sagittal); furthermore, annotations can be complemented after applying a segmentation technique. WebMedSA is based on three main steps: (1) RDF-ization process for extracting, anonymizing, and serializing metadata comprised in DICOM medical images into RDF/XML; (2) Integration of different biomedical ontologies (using L-MOM library), making this approach ontology independent; and (3) segmentation and visualization of annotated data which is further used to generate new annotations according to expert knowledge, and validation. Initial user evaluations suggest that WebMedSA facilitates the exchange of knowledge between radiologists, and provides the basis for collaborative work among them.
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Francisco Vega, Wilson Pérez, Andrés Tello, Victor Saquicela, Mauricio Espinoza, Lizandro Solano-Quinde, Maria-Esther Vidal, Alexandra La Cruz, "WebMedSA: a web-based framework for segmenting and annotating medical images using biomedical ontologies", Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 968110 (22 December 2015); doi: 10.1117/12.2214324; https://doi.org/10.1117/12.2214324
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