Non-rigid registration methods assessment of 3D CT images for head-neck radiotherapy

Adriane Parraga; Johanna Pettersson; Altamiro Susin; Mathieu De Craene; Benoît Macq

doi:10.1117/12.709368

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5 March 2007 Non-rigid registration methods assessment of 3D CT images for head-neck radiotherapy

Adriane Parraga, Johanna Pettersson, Altamiro Susin, Mathieu De Craene, Benoît Macq

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Proceedings Volume 6512, Medical Imaging 2007: Image Processing; 65121H (2007) https://doi.org/10.1117/12.709368
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

Intensity Modulated Radiotherapy is a new technique enabling the sculpting of the 3D radiation dose. It enables to modulate the delivery of the dose inside the malignant areas and constrain the radiation plan for protecting important functional areas. It also raises the issues of adequacy and accuracy of the selection and delineation of the target volumes. The delineation in the patient image of the tumor volume is highly time-consuming and requires considerable expertise. In this paper we focus on atlas based automatic segmentation of head and neck patients and compare two non-rigid registration methods: B-Spline and Morphons. To assess the quality of each method, we took a set of four 3D CT patient's images previously segmented by a doctor with the organs at risk. After a preliminary affine registration, both non-rigid registration algorithms were applied to match the patient and atlas images. Each deformation field, resulted from the non-rigid deformation, was applied on the masks corresponding to segmented regions in the atlas. The atlas based segmentation masks were compared to manual segmentations performed by an expert. We conclude that Morphons has performed better for matching all structures being considered, improving in average 11% the segmentation.

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Adriane Parraga, Johanna Pettersson, Altamiro Susin, Mathieu De Craene, and Benoît Macq "Non-rigid registration methods assessment of 3D CT images for head-neck radiotherapy", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65121H (5 March 2007); https://doi.org/10.1117/12.709368

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