Joint tumor growth prediction and tumor segmentation on therapeutic follow-up PET images

Autor: Pierre Vera, Su Ruan, Hongmei Mi, Caroline Petitjean
Přispěvatelé: Equipe Quantification en Imagerie Fonctionnelle (QuantIF-LITIS), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Université Le Havre Normandie (ULH), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Normandie Université (NU), Service de médecine nucléaire [Rouen], CRLCC Haute Normandie-Centre de Lutte Contre le Cancer Henri Becquerel Normandie Rouen (CLCC Henri Becquerel)
Rok vydání: 2015
Předmět:
medicine.medical_specialty
Lung Neoplasms
[SDV]Life Sciences [q-bio]
medicine.medical_treatment
Health Informatics
Context (language use)
030218 nuclear medicine & medical imaging
03 medical and health sciences
Imaging
Three-Dimensional
0302 clinical medicine
Predictive Value of Tests
Humans
Medicine
[INFO]Computer Science [cs]
Radiology
Nuclear Medicine and imaging
Medical physics
Segmentation
Tumor growth
Radiation treatment planning
ComputingMilieux_MISCELLANEOUS
Radiological and Ultrasound Technology
medicine.diagnostic_test
business.industry
Pattern recognition
Computer Graphics and Computer-Aided Design
Radiation therapy
Positron emission tomography
Positron-Emission Tomography
030220 oncology & carcinogenesis
Disease Progression
Computer Vision and Pattern Recognition
Artificial intelligence
business
Joint (audio engineering)
Algorithms
Tumor segmentation
Zdroj: Medical Image Analysis
Medical Image Analysis, Elsevier, 2015, 23 (1), pp.84-91
ISSN: 1361-8415
1361-8423
DOI: 10.1016/j.media.2015.04.016
Popis: Tumor response to treatment varies among patients. Patient-specific prediction of tumor evolution based on medical images during the treatment can help to build and adapt patient's treatment planning in a non-invasive way. Personalized tumor growth modeling allows patient-specific prediction by estimating model parameters based on individual's images. The model parameters are often estimated by optimizing a cost function constructed based on the tumor delineations. In this paper, we propose a joint framework for tumor growth prediction and tumor segmentation in the context of patient's therapeutic follow ups. Throughout the treatment, a series of sequential positron emission tomography (PET) images are acquired for tumor response monitoring. We propose to take into account the predicted information, which is used in combination with the random walks (RW) algorithm, to develop an automatic tumor segmentation method on PET images. Moreover, we propose an iterative scheme of RW, making the segmentation more performant. Furthermore, the obtained segmentation is applied to the process of model parameter estimation so as to get the model based prediction of tumor evolution. We evaluate our methods on 7 lung tumor patients, totaling 29 PET exams, under radiotherapy by comparing the obtained tumor prediction and tumor segmentation with manual tumor delineation by expert. Our system produces promising results when compared to the state-of-the-art methods.
Databáze: OpenAIRE
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