Intra-patient colon surface registration based on tæniæ coli

Julien Lamy; Ronald M. Summers

doi:10.1117/12.709780

29 March 2007 Intra-patient colon surface registration based on t&tildeae;ni&tildeae; coli

Julien Lamy, Ronald M. Summers

Proceedings Volume 6514, Medical Imaging 2007: Computer-Aided Diagnosis; 65140C (2007) https://doi.org/10.1117/12.709780
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

CT colonography, a prevalent tool to diagnose colon cancer in its early stages, is often limited by bad distention, or retained fluids, which will cause segments of the colon to be impossible to process by CAD tools. By scanning patients in both prone and supine positions, collapsed segments and retained fluids will not be in the same place in both images, increasing the length of the colon that can be processed correctly. In order to fully use these two scans, they must be registered, so that a lesion identified on one of them can be mapped to the other, thus increasing sensitivity and specificity of CAD tools. The surface of the colon is however large (more than half a million vertices on our images), and has no canonical shape, which makes atlases and other widely used registration algorithms non optimal. We present in this paper a fast method to register the colon surface between prone and supine scans using landmarks present on the colon, the teniae coli. Our method is composed of three steps. First, we register the body, based on manually placed landmarks. Then we register the three teni&tildeae; coli, and, from this registration, we compute a deformation field for each vertex of the colon surface. We tested our method on 5 cases, by measuring the RMS error after body registration, quantifying the intrisic movement of the colon, and after colon surface registration. The RMS error was reduced from 1.8 cm to 0.49 cm, a reduction of 71%.

Citation Download Citation

Julien Lamy and Ronald M. Summers "Intra-patient colon surface registration based on t&tildeae;ni&tildeae; coli", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65140C (29 March 2007); https://doi.org/10.1117/12.709780

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