Outils de contourage, utilisation et construction d’atlas anatomiques : exemples des cancers de la tête et du cou

Highly conformal irradiation techniques are associated with steep gradient doses. Accuracy and reproducibility of delineation are required to avoid geometric misses and to properly report dose-volume effects on organs at risk. Guidelines of the International Commission on Radiation Units have largely contributed to high quality treatments. The ICRU endeavors to collect and evaluate the latest data and information pertinent to the problems of radiation measurement and dosimetry. There remains a need for delineation guidelines and automatic segmentation tools in routine practice. Among these tools, atlas-based segmentation has been shown to provide promising results. The methodology used for head and neck cancer patients is illustrated.

[1]  Cameron S. Carter,et al.  Optimum template selection for atlas-based segmentation , 2007, NeuroImage.

[2]  Jean Meunier,et al.  Average Brain Models: A Convergence Study , 2000, Comput. Vis. Image Underst..

[3]  James V. Miller,et al.  Atlas Stratification , 2006, MICCAI.

[4]  Yoann Pointreau,et al.  Aide à la délinéation: quels outils pratiques ? , 2009 .

[5]  Grégoire Malandain,et al.  Atlas-based delineation of lymph node levels in head and neck computed tomography images. , 2008, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[6]  Max A. Viergever,et al.  Multi-Atlas-Based Segmentation With Local Decision Fusion—Application to Cardiac and Aortic Segmentation in CT Scans , 2009, IEEE Transactions on Medical Imaging.

[7]  Daniel Rueckert,et al.  Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy , 2009, NeuroImage.

[8]  Daniel Rueckert,et al.  Classifier Selection Strategies for Label Fusion Using Large Atlas Databases , 2007, MICCAI.

[9]  N. Ayache,et al.  Atlas-based automatic segmentation of MR images: validation study on the brainstem in radiotherapy context. , 2005, International journal of radiation oncology, biology, physics.

[10]  Grégoire Malandain,et al.  Efficient Selection of the Most Similar Image in a Database for Critical Structures Segmentation , 2007, MICCAI.

[11]  Eric Lartigau,et al.  Proposition de sélection et délimitation des volumes cibles microscopiques péritumoraux dans les cancers de la cavité buccale et de l'oropharynx (aires ganglionnaires exclues)Propositions for the selection and the delineation of peritumoral microscopic disease volumes in oral cavity and oropharyngea , 2005 .

[12]  William M. Wells,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.

[13]  O. Commowick,et al.  A pre-clinical assessment of an atlas-based automatic segmentation tool for the head and neck. , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[14]  Simon K. Warfield,et al.  Using Frankenstein's Creature Paradigm to Build a Patient Specific Atlas , 2009, MICCAI.

[15]  Guido Gerig,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005, 8th International Conference, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part II , 2005, MICCAI.

[16]  Torsten Rohlfing,et al.  Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains , 2004, NeuroImage.

[17]  Sébastien Ourselin,et al.  Computation of the mid-sagittal plane in 3-D brain images , 2002, IEEE Transactions on Medical Imaging.

[18]  François Cotton,et al.  La délimitation des volumes cibles en radiothérapie : application des techniques d’imagerie , 2009 .

[19]  William M. Wells,et al.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.

[20]  Pierre Hellier,et al.  Level Set Methods in an EM Framework for Shape Classification and Estimation , 2004, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[21]  Grégoire Malandain,et al.  Evaluation of an atlas-based automatic segmentation software for the delineation of brain organs at risk in a radiation therapy clinical context. , 2008, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[22]  Alfred O. Hero,et al.  Least Biased Target Selection in Probabilistic Atlas Construction , 2005, MICCAI.

[23]  K. Ang,et al.  CT-based delineation of lymph node levels and related CTVs in the node-negative neck: DAHANCA, EORTC, GORTEC, NCIC,RTOG consensus guidelines. , 2003, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.