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

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...
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Gespeichert in:

Autor*in:	Mi, Hongmei [verfasserIn] Petitjean, Caroline Vera, Pierre Ruan, Su

Format:	Artikel
Sprache:	Englisch

Erschienen:	2015

Rechteinformationen:	Nutzungsrecht: Copyright © 2015 Elsevier B.V. All rights reserved.

Übergeordnetes Werk:	Enthalten in: Medical image analysis - Amsterdam [u.a.] : Elsevier, 1996, 23(2015), 1, Seite 84-91
Übergeordnetes Werk:	volume:23 ; year:2015 ; number:1 ; pages:84-91

Links:	Volltext Link aufrufen

DOI / URN:	10.1016/j.media.2015.04.016

Katalog-ID:	OLC1960853503

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allfields	10.1016/j.media.2015.04.016 doi PQ20160617 (DE-627)OLC1960853503 (DE-599)GBVOLC1960853503 (PRQ)c1277-22817d8221aa15326491446ce162e7f032a54769a0efbcd773e1a19535fd50ec0 (KEY)0392983320150000023000100084jointtumorgrowthpredictionandtumorsegmentationonth DE-627 ger DE-627 rakwb eng 004 ZDB Mi, Hongmei verfasserin aut Joint tumor growth prediction and tumor segmentation on therapeutic follow-up PET images 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier 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. Nutzungsrecht: Copyright © 2015 Elsevier B.V. All rights reserved. Petitjean, Caroline oth Vera, Pierre oth Ruan, Su oth Enthalten in Medical image analysis Amsterdam [u.a.] : Elsevier, 1996 23(2015), 1, Seite 84-91 (DE-627)223260010 (DE-600)1356436-5 (DE-576)080160034 1361-8415 nnns volume:23 year:2015 number:1 pages:84-91 http://dx.doi.org/10.1016/j.media.2015.04.016 Volltext http://www.ncbi.nlm.nih.gov/pubmed/25988489 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_4219 AR 23 2015 1 84-91
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title_auth	Joint tumor growth prediction and tumor segmentation on therapeutic follow-up PET images
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Joint tumor growth prediction and tumor segmentation on therapeutic follow-up PET images
url	http://dx.doi.org/10.1016/j.media.2015.04.016 http://www.ncbi.nlm.nih.gov/pubmed/25988489
remote_bool	false
author2	Petitjean, Caroline Vera, Pierre Ruan, Su
author2Str	Petitjean, Caroline Vera, Pierre Ruan, Su
ppnlink	223260010
mediatype_str_mv	n
isOA_txt	false
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author2_role	oth oth oth
doi_str	10.1016/j.media.2015.04.016
up_date	2024-07-03T22:51:45.531Z
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