A Multimodality Segmentation Framework for Automatic Target Delineation in Head and Neck Radiotherapy

Overview

Journal Med Phys

Specialty Biophysics

Date 2015 Sep 3

PMID 26328980

Citations 18

Authors

Jinzhong Yang

Beth M Beadle

Adam S Garden

David L Schwartz

Michalis Aristophanous

Affiliations

Soon will be listed here.

Abstract

Purpose: To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy.

Methods: Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation-maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the "ground truth" for quantitative evaluation.

Results: The median multichannel segmented GTV of the primary tumor was 15.7 cm(3) (range, 6.6-44.3 cm(3)), while the PET segmented GTV was 10.2 cm(3) (range, 2.8-45.1 cm(3)). The median physician-defined GTV was 22.1 cm(3) (range, 4.2-38.4 cm(3)). The median difference between the multichannel segmented and physician-defined GTVs was -10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was -19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was 0.75 (range, 0.55-0.84), and the median sensitivity and positive predictive value between them were 0.76 and 0.81, respectively.

Conclusions: The authors developed an automated multimodality segmentation algorithm for tumor volume delineation and validated this algorithm for head and neck cancer radiotherapy. The multichannel segmented GTV agreed well with the physician-defined GTV. The authors expect that their algorithm will improve the accuracy and consistency in target definition for radiotherapy.

Citing Articles

Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.

Erdur A, Rusche D, Scholz D, Kiechle J, Fischer S, Llorian-Salvador O Strahlenther Onkol. 2024; 201(3):236-254.

PMID: 39105745 PMC: 11839850. DOI: 10.1007/s00066-024-02262-2.

Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.

Zhao Y, Wang X, Phan J, Chen X, Lee A, Yu C Med Phys. 2024; 51(10):7295-7307.

PMID: 38896829 PMC: 11479854. DOI: 10.1002/mp.17260.

Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning.

Sahlsten J, Jaskari J, Wahid K, Ahmed S, Glerean E, He R Commun Med (Lond). 2024; 4(1):110.

PMID: 38851837 PMC: 11162474. DOI: 10.1038/s43856-024-00528-5.

Head and Neck Cancer Segmentation in FDG PET Images: Performance Comparison of Convolutional Neural Networks and Vision Transformers.

Xiong X, Smith B, Graves S, Graham M, Buatti J, Beichel R Tomography. 2023; 9(5):1933-1948.

PMID: 37888743 PMC: 10611182. DOI: 10.3390/tomography9050151.

The Pattern of Metastatic Breast Cancer: A Prospective Head-to-Head Comparison of [F]FDG-PET/CT and CE-CT.

Gram-Nielsen R, Christensen I, Naghavi-Behzad M, Dahlsgaard-Wallenius S, Jakobsen N, Gerke O J Imaging. 2023; 9(10).

PMID: 37888329 PMC: 10607582. DOI: 10.3390/jimaging9100222.

References

Chen A, Farwell D, Luu Q, Chen L, Vijayakumar S, Purdy J . Misses and near-misses after postoperative radiation therapy for head and neck cancer: Comparison of IMRT and non-IMRT techniques in the CT-simulation era. Head Neck. 2010; 32(11):1452-9. DOI: 10.1002/hed.21343. View

Li H, Thorstad W, Biehl K, Laforest R, Su Y, Shoghi K . A novel PET tumor delineation method based on adaptive region-growing and dual-front active contours. Med Phys. 2008; 35(8):3711-21. PMC: 3304493. DOI: 10.1118/1.2956713. View

Breen S, Publicover J, De Silva S, Pond G, Brock K, OSullivan B . Intraobserver and interobserver variability in GTV delineation on FDG-PET-CT images of head and neck cancers. Int J Radiat Oncol Biol Phys. 2007; 68(3):763-70. DOI: 10.1016/j.ijrobp.2006.12.039. View

Belhassen S, Zaidi H . A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET. Med Phys. 2010; 37(3):1309-24. DOI: 10.1118/1.3301610. View

Paulino A, Koshy M, Howell R, Schuster D, Davis L . Comparison of CT- and FDG-PET-defined gross tumor volume in intensity-modulated radiotherapy for head-and-neck cancer. Int J Radiat Oncol Biol Phys. 2005; 61(5):1385-92. DOI: 10.1016/j.ijrobp.2004.08.037. View

Kirby N, Chuang C, Ueda U, Pouliot J . The need for application-based adaptation of deformable image registration. Med Phys. 2013; 40(1):011702. DOI: 10.1118/1.4769114. View

Mattes D, Haynor D, Vesselle H, Lewellen T, Eubank W . PET-CT image registration in the chest using free-form deformations. IEEE Trans Med Imaging. 2003; 22(1):120-8. DOI: 10.1109/TMI.2003.809072. View

Zhang J . The mean field theory in EM procedures for blind Markov random field image restoration. IEEE Trans Image Process. 1993; 2(1):27-40. DOI: 10.1109/83.210863. View

Sharp G, Fritscher K, Pekar V, Peroni M, Shusharina N, Veeraraghavan H . Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys. 2014; 41(5):050902. PMC: 4000389. DOI: 10.1118/1.4871620. View

10.

Hsu C, Liu C, Chen C . Automatic segmentation of liver PET images. Comput Med Imaging Graph. 2008; 32(7):601-10. DOI: 10.1016/j.compmedimag.2008.07.001. View

11.

Song Q, Bai J, Han D, Bhatia S, Sun W, Rockey W . Optimal co-segmentation of tumor in PET-CT images with context information. IEEE Trans Med Imaging. 2013; 32(9):1685-97. PMC: 3965345. DOI: 10.1109/TMI.2013.2263388. View

12.

Weiss E, Hess C . The impact of gross tumor volume (GTV) and clinical target volume (CTV) definition on the total accuracy in radiotherapy theoretical aspects and practical experiences. Strahlenther Onkol. 2003; 179(1):21-30. DOI: 10.1007/s00066-003-0976-5. View

13.

Nishioka T, Shiga T, Shirato H, Tsukamoto E, Tsuchiya K, Kato T . Image fusion between 18FDG-PET and MRI/CT for radiotherapy planning of oropharyngeal and nasopharyngeal carcinomas. Int J Radiat Oncol Biol Phys. 2002; 53(4):1051-7. DOI: 10.1016/s0360-3016(02)02854-7. View

14.

Zhang Y, Brady M, Smith S . Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging. 2001; 20(1):45-57. DOI: 10.1109/42.906424. View

15.

Geman S, Geman D . Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell. 2012; 6(6):721-41. DOI: 10.1109/tpami.1984.4767596. View

16.

Stapleford L, Lawson J, Perkins C, Edelman S, Davis L, McDonald M . Evaluation of automatic atlas-based lymph node segmentation for head-and-neck cancer. Int J Radiat Oncol Biol Phys. 2010; 77(3):959-66. DOI: 10.1016/j.ijrobp.2009.09.023. View

17.

Geets X, Daisne J, Arcangeli S, Coche E, De Poel M, Duprez T . Inter-observer variability in the delineation of pharyngo-laryngeal tumor, parotid glands and cervical spinal cord: comparison between CT-scan and MRI. Radiother Oncol. 2005; 77(1):25-31. DOI: 10.1016/j.radonc.2005.04.010. View

18.

Ashamalla H, Guirgius A, Bieniek E, Rafla S, Evola A, Goswami G . The impact of positron emission tomography/computed tomography in edge delineation of gross tumor volume for head and neck cancers. Int J Radiat Oncol Biol Phys. 2007; 68(2):388-95. DOI: 10.1016/j.ijrobp.2006.12.029. View

19.

Aristophanous M, Penney B, Martel M, Pelizzari C . A Gaussian mixture model for definition of lung tumor volumes in positron emission tomography. Med Phys. 2007; 34(11):4223-35. DOI: 10.1118/1.2791035. View

20.

Njeh C, Dong L, Orton C . Point/Counterpoint. IGRT has limited clinical value due to lack of accurate tumor delineation. Med Phys. 2013; 40(4):040601. DOI: 10.1118/1.4789492. View