Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications.: Automatic PET image segmentation for oncology applications
| Autor: | Philippe Lambin, Olivier Pradier, Catherine Cheze Le Rest, Andre Dekker, M Oellers, Patrice Descourt, Mathieu Hatt, Dimitris Visvikis, Dirk De Ruysscher |
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| Přispěvatelé: | RS: GROW - School for Oncology and Reproduction, Radiotherapie, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Mines-Télécom [Paris] (IMT), Médecine nucléaire, Hôpital Morvan [Brest]-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), MAASTRO clinic, Maastro Lab, Institut de cancérologie et d'hématologie, Brittany Region grant program 1202-2004 -French National Research Agency (ANR-06-CIS6-004-03,ANR-08-ETEC-005-01) -Cancéropôle Grand Ouest (R05014NG) |
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
Oncology
MESH: Tumor Burden Cancer Research medicine.medical_treatment Image-guided radiotherapy Medical Oncology 030218 nuclear medicine & medical imaging 0302 clinical medicine MESH: Fluorodeoxyglucose F18 Neoplasms MESH: Neoplasms MESH: Radiotherapy Dosage MESH: Radiotherapy Planning Computer-Assisted MESH: Medical Oncology Contouring Radiation medicine.diagnostic_test Radiotherapy Dosage Thresholding MESH: Positron-Emission Tomography Markov Chains Tumor Burden 3. Good health Positron emission tomography 030220 oncology & carcinogenesis MESH: Fuzzy Logic Tomography Automatic segmentation MESH: Radiopharmaceuticals Algorithms medicine.medical_specialty MESH: Bayes Theorem MESH: Algorithms [SDV.IB.MN]Life Sciences [q-bio]/Bioengineering/Nuclear medicine 03 medical and health sciences Fuzzy Logic MESH: Markov Chains Fluorodeoxyglucose F18 Internal medicine Dose painting medicine Humans Dosimetry Radiology Nuclear Medicine and imaging Heterogeneous functional volumes delineation MESH: Humans business.industry Radiotherapy Planning Computer-Assisted Bayes Theorem Image segmentation Radiation therapy Positron-Emission Tomography Radiopharmaceuticals Nuclear medicine business Emission computed tomography |
| Zdroj: | International Journal of Radiation Oncology Biology Physics, 77(1), 301-308. Elsevier Science International Journal of Radiation Oncology-Biology-Physics International Journal of Radiation Oncology-Biology-Physics, Elsevier, 2010, 77 (1), pp.301-8. ⟨10.1016/j.ijrobp.2009.08.018⟩ |
| ISSN: | 0360-3016 |
| Popis: | International audience; PURPOSE: Accurate contouring of positron emission tomography (PET) functional volumes is now considered crucial in image-guided radiotherapy and other oncology applications because the use of functional imaging allows for biological target definition. In addition, the definition of variable uptake regions within the tumor itself may facilitate dose painting for dosimetry optimization. METHODS AND MATERIALS: Current state-of-the-art algorithms for functional volume segmentation use adaptive thresholding. We developed an approach called fuzzy locally adaptive Bayesian (FLAB), validated on homogeneous objects, and then improved it by allowing the use of up to three tumor classes for the delineation of inhomogeneous tumors (3-FLAB). Simulated and real tumors with histology data containing homogeneous and heterogeneous activity distributions were used to assess the algorithm's accuracy. RESULTS: The new 3-FLAB algorithm is able to extract the overall tumor from the background tissues and delineate variable uptake regions within the tumors, with higher accuracy and robustness compared with adaptive threshold (T(bckg)) and fuzzy C-means (FCM). 3-FLAB performed with a mean classification error of less than 9% +/- 8% on the simulated tumors, whereas binary-only implementation led to errors of 15% +/- 11%. T(bckg) and FCM led to mean errors of 20% +/- 12% and 17% +/- 14%, respectively. 3-FLAB also led to more robust estimation of the maximum diameters of tumors with histology measurements, with |
| Databáze: | OpenAIRE |
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