Hierarchical Vertex Regression-Based Segmentation of Head and Neck CT Images for Radiotherapy Planning

Overview

Journal IEEE Trans Image Process

Date 2018 May 15

PMID 29757737

Citations 15

Authors

Zhensong Wang

Lifang Wei

Li Wang

Yaozong Gao

Wufan Chen

Dinggang Shen

Affiliations

Soon will be listed here.

Abstract

Segmenting organs at risk from head and neck CT images is a prerequisite for the treatment of head and neck cancer using intensity modulated radiotherapy. However, accurate and automatic segmentation of organs at risk is a challenging task due to the low contrast of soft tissue and image artifact in CT images. Shape priors have been proved effective in addressing this challenging task. However, conventional methods incorporating shape priors often suffer from sensitivity to shape initialization and also shape variations across individuals. In this paper, we propose a novel approach to incorporate shape priors into a hierarchical learning-based model. The contributions of our proposed approach are as follows: 1) a novel mechanism for critical vertices identification is proposed to identify vertices with distinctive appearances and strong consistency across different subjects; 2) a new strategy of hierarchical vertex regression is also used to gradually locate more vertices with the guidance of previously located vertices; and 3) an innovative framework of joint shape and appearance learning is further developed to capture salient shape and appearance features simultaneously. Using these innovative strategies, our proposed approach can essentially overcome drawbacks of the conventional shape-based segmentation methods. Experimental results show that our approach can achieve much better results than state-of-the-art methods.

Citing Articles

Nested block self-attention multiple resolution residual network for multiorgan segmentation from CT.

Jiang J, Elguindi S, Berry S, Onochie I, Cervino L, Deasy J Med Phys. 2022; 49(8):5244-5257.

PMID: 35598077 PMC: 9908007. DOI: 10.1002/mp.15765.

Deep learning-based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients.

Kawahara D, Tsuneda M, Ozawa S, Okamoto H, Nakamura M, Nishio T J Appl Clin Med Phys. 2022; 23(5):e13579.

PMID: 35263027 PMC: 9121028. DOI: 10.1002/acm2.13579.

Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review.

Qiu B, van der Wel H, Kraeima J, Glas H, Guo J, Borra R J Pers Med. 2021; 11(7).

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Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study.

Nikolov S, Blackwell S, Zverovitch A, Mendes R, Livne M, De Fauw J J Med Internet Res. 2021; 23(7):e26151.

PMID: 34255661 PMC: 8314151. DOI: 10.2196/26151.

Mandible Segmentation of Dental CBCT Scans Affected by Metal Artifacts Using Coarse-to-Fine Learning Model.

Qiu B, van der Wel H, Kraeima J, Glas H, Guo J, Borra R J Pers Med. 2021; 11(6).

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