A framework based on hidden Markov trees for multimodal PET/CT image co-segmentation
Autor: | Dimitris Visvikis, Wojciech Pieczynski, Mathieu Hatt, Philippe Lambin, Didier Benoit, Houda Hanzouli-Ben Salah, Jerome Lapuyade-Lahorgue, Emmanuel Monfrini, Julien Bert, Angela van Baardwijk |
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Přispěvatelé: | Laboratoire de Traitement de l'Information Medicale (LaTIM), Institut National de la Santé et de la Recherche Médicale (INSERM)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Maastricht Radiation Oncology Clinic (MAASTRO), Maastricht University [Maastricht], Traitement de l'Information Pour Images et Communications (TIPIC-SAMOVAR), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Centre National de la Recherche Scientifique (CNRS), RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, Radiotherapie, Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM) |
Jazyk: | angličtina |
Rok vydání: | 2017 |
Předmět: |
QUANTITATION
Computer science CELL LUNG-CANCER Bayesian inference Wavelet Analysis Image processing [SDV.IB.MN]Life Sciences [q-bio]/Bioengineering/Nuclear medicine computed tomography (CT) computer.software_genre TRACER UPTAKE CLASSIFICATION 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Wavelet Voxel Positron Emission Tomography Computed Tomography Image Processing Computer-Assisted [INFO.INFO-IM]Computer Science [cs]/Medical Imaging medicine Humans Segmentation F-18-FDG PET RECONSTRUCTION positron emission tomography (PET) BRAIN Hidden Markov model PET-CT medicine.diagnostic_test business.industry segmentation Pattern recognition General Medicine Image segmentation CT IMAGES Markov Chains Contourlet wavelet and contourlet analysis TUMOR DELINEATION MODEL Positron emission tomography [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] 030220 oncology & carcinogenesis Artificial intelligence business Nuclear medicine computer hidden Markov trees (HMT) |
Zdroj: | HAL Medical Physics Medical Physics, American Association of Physicists in Medicine, 2017 Medical Physics, 44(11), 5835-5848. Wiley |
ISSN: | 0094-2405 |
DOI: | 10.1002/mp.12531 |
Popis: | PurposeThe purpose of this study was to investigate the use of a probabilistic quad-tree graph (hidden Markov tree, HMT) to provide fast computation, robustness and an interpretational framework for multimodality image processing and to evaluate this framework for single gross tumor target (GTV) delineation from both positron emission tomography (PET) and computed tomography (CT) images.MethodsWe exploited joint statistical dependencies between hidden states to handle the data stack using multi-observation, multi-resolution of HMT and Bayesian inference. This framework was applied to segmentation of lung tumors in PET/CT datasets taking into consideration simultaneously the CT and the PET image information. PET and CT images were considered using either the original voxels intensities, or after wavelet/contourlet enhancement. The Dice similarity coefficient (DSC), sensitivity (SE), positive predictive value (PPV) were used to assess the performance of the proposed approach on one simulated and 15 clinical PET/CT datasets of non-small cell lung cancer (NSCLC) cases. The surrogate of truth was a statistical consensus (obtained with the Simultaneous Truth and Performance Level Estimation algorithm) of three manual delineations performed by experts on fused PET/CT images. The proposed framework was applied to PET-only, CT-only and PET/CT datasets, and were compared to standard and improved fuzzy c-means (FCM) multimodal implementations.ResultsA high agreement with the consensus of manual delineations was observed when using both PET and CT images. Contourlet-based HMT led to the best results with a DSC of 0.92 0.11 compared to 0.89 +/- 0.13 and 0.90 +/- 0.12 for Intensity-based HMT and Wavelet-based HMT, respectively. Considering PET or CT only in the HMT led to much lower accuracy. Standard and improved FCM led to comparatively lower accuracy than HMT, even when considering multimodal implementations.ConclusionsWe evaluated the accuracy of the proposed HMT-based framework for PET/CT image segmentation. The proposed method reached good accuracy, especially with pre-processing in the contourlet domain. |
Databáze: | OpenAIRE |
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