Automatic voxel-based CT segmentation
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D.F. Malan, C.P. Botha, R.G.H.H. Nelissen, and E.R. Valstar, Voxel classification of periprosthetic tissues in clinical computed tomography of loosened hip prostheses, in Proceedings of the 2010 IEEE International Symposium On Biomedical Imaging, pp. 1341--1344, 2010. |
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Abstract
We present an automated algorithm which classifies periprosthetic tissues in CT scans of patients with loosened hip prostheses. To our knowledge this is the first application of CT voxel classification to periprosthetic tissues of the hip. We use several image features including multi-scale image intensity, multi-scale image gradient and distance metrics. Seven classifier types were trained using five manually segmented clinical CT datasets, and their classification performance compared to manual segmentations using a leave-one-out scheme. Using this technique we are able to correctly segment the majority of each of the six tissue categories, in spite of low bone densities, metal-induced CT imaging artefacts and inter-patient and inter-scan variation. Our automated classifier forms a pragmatic first step towards eventual automatic tissue segmentation.
Images
CT is plagued by image artefacts when the field of view includes a metal prosthesis.
We compute several image feaures which, together, make classification easier.
More info
Project page: Minimally invasive refixation of hip prostheses.