Literature DB >> 28603407

Simultaneous Tumor Segmentation, Image Restoration, and Blur Kernel Estimation in PET Using Multiple Regularizations.

Laquan Li1, Jian Wang1, Wei Lu2,3, Shan Tan1.   

Abstract

Accurate tumor segmentation from PET images is crucial in many radiation oncology applications. Among others, partial volume effect (PVE) is recognized as one of the most important factors degrading imaging quality and segmentation accuracy in PET. Taking into account that image restoration and tumor segmentation are tightly coupled and can promote each other, we proposed a variational method to solve both problems simultaneously in this study. The proposed method integrated total variation (TV) semi-blind de-convolution and Mumford-Shah segmentation with multiple regularizations. Unlike many existing energy minimization methods using either TV or L2 regularization, the proposed method employed TV regularization over tumor edges to preserve edge information, and L2 regularization inside tumor regions to preserve the smooth change of the metabolic uptake in a PET image. The blur kernel was modeled as anisotropic Gaussian to address the resolution difference in transverse and axial directions commonly seen in a clinic PET scanner. The energy functional was rephrased using the Γ-convergence approximation and was iteratively optimized using the alternating minimization (AM) algorithm. The performance of the proposed method was validated on a physical phantom and two clinic datasets with non-Hodgkin's lymphoma and esophageal cancer, respectively. Experimental results demonstrated that the proposed method had high performance for simultaneous image restoration, tumor segmentation and scanner blur kernel estimation. Particularly, the recovery coefficients (RC) of the restored images of the proposed method in the phantom study were close to 1, indicating an efficient recovery of the original blurred images; for segmentation the proposed method achieved average dice similarity indexes (DSIs) of 0.79 and 0.80 for two clinic datasets, respectively; and the relative errors of the estimated blur kernel widths were less than 19% in the transversal direction and 7% in the axial direction.

Entities:  

Keywords:  L2 regularization; TV regularization; blur kernel estimation; image restoration; tumor segmentation; variational method

Year:  2016        PMID: 28603407      PMCID: PMC5463621          DOI: 10.1016/j.cviu.2016.10.002

Source DB:  PubMed          Journal:  Comput Vis Image Underst        ISSN: 1077-3142            Impact factor:   3.876


  46 in total

1.  Recovery correction for quantitation in emission tomography: a feasibility study.

Authors:  L Geworski; B O Knoop; M L de Cabrejas; W H Knapp; D L Munz
Journal:  Eur J Nucl Med       Date:  2000-02

2.  Segmentation of PET volumes by iterative image thresholding.

Authors:  Walter Jentzen; Lutz Freudenberg; Ernst G Eising; Melanie Heinze; Wolfgang Brandau; Andreas Bockisch
Journal:  J Nucl Med       Date:  2007-01       Impact factor: 10.057

3.  A novel PET tumor delineation method based on adaptive region-growing and dual-front active contours.

Authors:  Hua Li; Wade L Thorstad; Kenneth J Biehl; Richard Laforest; Yi Su; Kooresh I Shoghi; Eric D Donnelly; Daniel A Low; Wei Lu
Journal:  Med Phys       Date:  2008-08       Impact factor: 4.071

4.  Partial volume effect correction in PET using regularized iterative deconvolution with variance control based on local topology.

Authors:  A S Kirov; J Z Piao; C R Schmidtlein
Journal:  Phys Med Biol       Date:  2008-04-25       Impact factor: 3.609

5.  Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-Small cell lung cancer.

Authors:  Ursula Nestle; Stephanie Kremp; Andrea Schaefer-Schuler; Christiane Sebastian-Welsch; Dirk Hellwig; Christian Rübe; Carl-Martin Kirsch
Journal:  J Nucl Med       Date:  2005-08       Impact factor: 10.057

6.  A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET.

Authors:  Saoussen Belhassen; Habib Zaidi
Journal:  Med Phys       Date:  2010-03       Impact factor: 4.071

7.  Performance characteristics of a whole-body PET scanner.

Authors:  T R DeGrado; T G Turkington; J J Williams; C W Stearns; J M Hoffman; R E Coleman
Journal:  J Nucl Med       Date:  1994-08       Impact factor: 10.057

8.  A new method for volume segmentation of PET images, based on possibility theory.

Authors:  Anne-Sophie Dewalle-Vignion; Nacim Betrouni; Renaud Lopes; Damien Huglo; Simon Stute; Maximilien Vermandel
Journal:  IEEE Trans Med Imaging       Date:  2010-10-14       Impact factor: 10.048

Review 9.  PET/CT in radiation oncology.

Authors:  Tinsu Pan; Osama Mawlawi
Journal:  Med Phys       Date:  2008-11       Impact factor: 4.071

10.  18F-FDG PET definition of gross tumor volume for radiotherapy of non-small cell lung cancer: is a single standardized uptake value threshold approach appropriate?

Authors:  Kenneth J Biehl; Feng-Ming Kong; Farrokh Dehdashti; Jian-Yue Jin; Sasa Mutic; Issam El Naqa; Barry A Siegel; Jeffrey D Bradley
Journal:  J Nucl Med       Date:  2006-11       Impact factor: 10.057

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  6 in total

1.  Design and evaluation of an accurate CNR-guided small region iterative restoration-based tumor segmentation scheme for PET using both simulated and real heterogeneous tumors.

Authors:  Alpaslan Koç; Albert Güveniş
Journal:  Med Biol Eng Comput       Date:  2019-12-17       Impact factor: 2.602

2.  Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network.

Authors:  Xiangming Zhao; Laquan Li; Wei Lu; Shan Tan
Journal:  Phys Med Biol       Date:  2018-12-21       Impact factor: 3.609

3.  Adaptive region-growing with maximum curvature strategy for tumor segmentation in 18F-FDG PET.

Authors:  Shan Tan; Laquan Li; Wookjin Choi; Min Kyu Kang; Warren D D'Souza; Wei Lu
Journal:  Phys Med Biol       Date:  2017-06-12       Impact factor: 3.609

4.  Variational PET/CT Tumor Co-segmentation Integrated with PET Restoration.

Authors:  Laquan Li; Wei Lu; Shan Tan
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2019-04-16

5.  Deep Learning for Variational Multimodality Tumor Segmentation in PET/CT.

Authors:  Laquan Li; Xiangming Zhao; Wei Lu; Shan Tan
Journal:  Neurocomputing       Date:  2019-04-24       Impact factor: 5.719

6.  Preprocessing of 18F-DMFP-PET Data Based on Hidden Markov Random Fields and the Gaussian Distribution.

Authors:  Fermín Segovia; Juan M Górriz; Javier Ramírez; Francisco J Martínez-Murcia; Diego Salas-Gonzalez
Journal:  Front Aging Neurosci       Date:  2017-10-09       Impact factor: 5.750

  6 in total

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