PURPOSE: A co-registered multimodality pattern analysis segmentation system (COMPASS) was developed to automatically delineate the radiation targets in head-and-neck cancer (HNC) using both (18)F-fluoro-deoxy glucose-positron emission tomography (PET) and computed tomography (CT) images. The performance of the COMPASS was compared with the results of existing threshold-based methods and radiation oncologist-drawn contours. METHODS AND MATERIALS: The COMPASS extracted texture features from corresponding PET and CT voxels. Using these texture features, a decision-tree-based K-nearest-neighbor classifier labeled each voxel as either "normal" or "abnormal." The COMPASS was applied to the PET/CT images of 10 HNC patients. Automated segmentation results were validated against the manual segmentations of three radiation oncologists using the volume, sensitivity, and specificity. The performance of the COMPASS was compared with three PET-based threshold methods: standard uptake value of 2.5, 50% maximal intensity, and signal/background ratio. RESULTS: The tumor delineations of the COMPASS were both quantitatively and qualitatively more similar to those of the radiation oncologists than the delineations from the other methods. The specificity was 95% +/- 2%, 84% +/- 9%, 98% +/- 3%, and 96% +/- 4%, and the sensitivity was 90% +/- 12%, 93% +/- 10%, 48% +/- 20%, and 68% +/- 25% for the COMPASS, for a standard uptake value of 2.5, 50% maximal intensity, and signal/background ratio, respectively. The COMPASS distinguished HNC from adjacent normal tissues with high physiologic uptake and consistently defined tumors with large variability in (18)F-fluoro-deoxy glucose uptake, which are often problematic with the threshold-based methods. CONCLUSION: Automated segmentation using texture analysis of PET/CT images has the potential to provide accurate delineation of HNC. This could lead to reduced interobserver variability, reduced uncertainty in target delineation, and improved treatment planning accuracy.
PURPOSE: A co-registered multimodality pattern analysis segmentation system (COMPASS) was developed to automatically delineate the radiation targets in head-and-neck cancer (HNC) using both (18)F-fluoro-deoxy glucose-positron emission tomography (PET) and computed tomography (CT) images. The performance of the COMPASS was compared with the results of existing threshold-based methods and radiation oncologist-drawn contours. METHODS AND MATERIALS: The COMPASS extracted texture features from corresponding PET and CT voxels. Using these texture features, a decision-tree-based K-nearest-neighbor classifier labeled each voxel as either "normal" or "abnormal." The COMPASS was applied to the PET/CT images of 10 HNC patients. Automated segmentation results were validated against the manual segmentations of three radiation oncologists using the volume, sensitivity, and specificity. The performance of the COMPASS was compared with three PET-based threshold methods: standard uptake value of 2.5, 50% maximal intensity, and signal/background ratio. RESULTS: The tumor delineations of the COMPASS were both quantitatively and qualitatively more similar to those of the radiation oncologists than the delineations from the other methods. The specificity was 95% +/- 2%, 84% +/- 9%, 98% +/- 3%, and 96% +/- 4%, and the sensitivity was 90% +/- 12%, 93% +/- 10%, 48% +/- 20%, and 68% +/- 25% for the COMPASS, for a standard uptake value of 2.5, 50% maximal intensity, and signal/background ratio, respectively. The COMPASS distinguished HNC from adjacent normal tissues with high physiologic uptake and consistently defined tumors with large variability in (18)F-fluoro-deoxy glucose uptake, which are often problematic with the threshold-based methods. CONCLUSION: Automated segmentation using texture analysis of PET/CT images has the potential to provide accurate delineation of HNC. This could lead to reduced interobserver variability, reduced uncertainty in target delineation, and improved treatment planning accuracy.
Authors: Tony Shepherd; Mika Teras; Reinhard R Beichel; Ronald Boellaard; Michel Bruynooghe; Volker Dicken; Mark J Gooding; Peter J Julyan; John A Lee; Sébastien Lefèvre; Michael Mix; Valery Naranjo; Xiaodong Wu; Habib Zaidi; Ziming Zeng; Heikki Minn Journal: IEEE Trans Med Imaging Date: 2012-06-04 Impact factor: 10.048
Authors: James Y Chen; F Jacob Seagull; Paul Nagy; Paras Lakhani; Elias R Melhem; Eliot L Siegel; Nabile M Safdar Journal: J Digit Imaging Date: 2011-02 Impact factor: 4.056
Authors: Jorge Oldan; Miao He; Teresa Wu; Alvin C Silva; Jing Li; J Ross Mitchell; William M Pavlicek; Michael C Roarke; Amy K Hara Journal: J Digit Imaging Date: 2014-12 Impact factor: 4.056
Authors: Mathieu Hatt; John A Lee; Charles R Schmidtlein; Issam El Naqa; Curtis Caldwell; Elisabetta De Bernardi; Wei Lu; Shiva Das; Xavier Geets; Vincent Gregoire; Robert Jeraj; Michael P MacManus; Osama R Mawlawi; Ursula Nestle; Andrei B Pugachev; Heiko Schöder; Tony Shepherd; Emiliano Spezi; Dimitris Visvikis; Habib Zaidi; Assen S Kirov Journal: Med Phys Date: 2017-05-18 Impact factor: 4.071