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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

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Abstract

Tumor delineation accuracy directly affects the effectiveness of radiotherapy. This study presents a methodology that minimizes potential errors during the automated segmentation of tumors in PET images. Iterative blind deconvolution was implemented in a region of interest encompassing the tumor with the number of iterations determined from contrast-to-noise ratios. The active contour and random forest classification-based segmentation method was evaluated using three distinct image databases that included both synthetic and real heterogeneous tumors. Ground truths about tumor volumes were known precisely. The volumes of the tumors were in the range of 0.49–26.34 cm3, 0.64–1.52 cm3, and 40.38–203.84 cm3 respectively. Widely available software tools, namely, MATLAB, MIPAV, and ITK-SNAP were utilized. When using the active contour method, image restoration reduced mean errors in volumes estimation from 95.85 to 3.37%, from 815.63 to 17.45%, and from 32.61 to 6.80% for the three datasets. The accuracy gains were higher using datasets that include smaller tumors for which PVE is known to be more predominant. Computation time was reduced by a factor of about 10 in the smaller deconvolution region. Contrast-to-noise ratios were improved for all tumors in all data. The presented methodology has the potential to improve delineation accuracy in particular for smaller tumors at practically feasible computational times.

Evaluation of accurate lesion volumes using CNR-guided and ROI-based restoration method for PET images

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Abbreviations

3D-OSEM:

3D ordered subset expectation maximization

ACRF:

Active contour and random forest classification-based segmentation

AT:

Adaptive thresholding

CE:

Classification error

CNR:

Contrast-to-noise ratio

DSC:

Dice similarity coefficient

FCM:

Fuzzy C-means

FNF:

False negative volume fraction

FOV:

Field of view

FPF:

False positive volume fraction

FWHM:

Full width at half maximum

MAE:

Mean absolute error

MET:

Maximum entropy thresholding

MSE:

Mean square error

NCAT:

NURBS-based cardiac-torso

NEMA:

National Electrical Manufacturers Association image quality phantom

PSF:

Point spread function

PSNR:

Peak signal to noise ratio

PVC:

Partial volume correction

PVE:

Partial volume effect

RC:

Recovery coefficients

RG:

Region growing

ROI:

Region of interest

RTP:

Radiation treatment planning

SNR:

Signal to noise ratio

SSIM:

Structural similarity index

SUV:

Standardized uptake value

TPF:

True positive volume fraction

VE%:

Volume error percentage

VOI:

Volume of interest

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Acknowledgments

The authors sincerely acknowledge the National Cancer Institute and the National Institute of Health for RIDER PHANTOM PET-CT data obtained from the publicly available Cancer Imaging Archive (TCIA). In addition, the authors would like to thank Sandrine Tomei, Anthonin Reilhac, Dimitris Visvikis, Nicolas Boussion, Christophe Odet, Francesco Giammarile, and Carole Lartizien for simulated PET oncology images obtained from the OncoPET_DB database. Finally, the authors also thank Panagiotis Papadimitroulas, George Loudos, Amandine Le Maitre, Mathieu Hatt, Florent Tixier, Nikos Efthimiou, George C. Nikiforidis, Dimitris Visvikis, and George C. Kagadis for simulated clinical images obtained from an oncology database.

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  1. Institute of Biomedical Engineering, Boğaziçi University, Kandilli Kampüs, Çengelköy, 34684, Istanbul, Turkey

    Alpaslan Koç & Albert Güveniş

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  1. Alpaslan Koç
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Koç, A., Güveniş, A. 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. Med Biol Eng Comput 58, 335–355 (2020). https://doi.org/10.1007/s11517-019-02094-8

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