Top

International Journal of Computer Assisted Radiology and Surgery

Published in:

01-02-2017 | Original Article

Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume

Authors: Qier Meng, Takayuki Kitasaka, Yukitaka Nimura, Masahiro Oda, Junji Ueno, Kensaku Mori

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 2/2017

Abstract

Purpose

Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree.

Methods

This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree.

Results

A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate.

Conclusion

A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

World Health Organization (Feb 2015) Cancer, fact sheet N deg 297. http://www.who.int/mediacentre/factsheets/fs297/en/

Kuhnigk JM, Hahn H, Hindennach M, Dicken V, Krass S, Peitgen HO (2003) Lung lobe segmentation by anatomy-guided 3D watershed transform. Proc SPIE Med Imaging 5032:1482–1490CrossRef

Mori K, Nakada Y, Kitasaka T, Suenaga Y, Takabatake H, Mori M, Natori H (2008) Lung lobe and segmental lobe extraction from 3D chest CT datasets based on figure decomposition and Voronoi division. Proc SPIE Med Imaging 6914:69144K-1–69144K-12CrossRef

Hu S, Hoffman E, Reinhardt J (2001) Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans Med Imaging 20(6):490–498PubMedCrossRef

Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T (2001) Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans Med Imaging 20(7):595–604PubMedCrossRef

Chen B, Kitasaka T, Honma H, Takabatake H, Mori M, Natori H, Mori K (2012) Automatic segmentation of pulmonary blood vessels and nodules based on local intensity structure analysis and surface propagation in 3D chest CT images. Int J Comput Assist Radiol Surg 7(3):465–482PubMedCrossRef

Li B, Christensen GE, Hoffman EA, McLennan G, Reinhardt JM (2008) Pulmonary CT image registration and warping for tracking tissue deformation during the respiratory cycle through 3D consistent image registration. Med Phys 35(12):5575–5583PubMedPubMedCentralCrossRef

Kiraly AP, Higgins WE, McLennan G, Hoffman EA, Reinhardt JM (2002) Three dimensional human airway segmentation methods for clinical virtual bronchoscopy. Acad Radiol 9(10):1153–1168PubMedCrossRef

Lo P, Ginneken B, Reinhardt J, Yavarna T, Jong P, Irving B, Fetita C, Ortner M, Pinho R, Sijbers J, Feuerstein M, Fabijanska A, Bauer C, Beichel R, Mendoza C, Wiemker R, Lee J, Reeves A, Born R, Weinheimer O, Rikxoort E, Tschirren J, Mori K, Odry B, Naidich D, Hartmann I, Hoffman E, Prokop M, Pedersen J, Bruijne M (2012) Extraction of airways from CT (EXACT’09). IEEE Trans Med Imaging 31(11):2093–2107PubMedCrossRef

10.

Mori K, Hasegawa J, Toriwaki J, Anno H, Kataba K (1995) Automated extraction and visualization of bronchus from 3D CT images of lung. Proc CVRMIed 95:542–548

11.

Aykac D, Hoffman E, McLennan G, Reinhardt J (2003) Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images. IEEE Trans Med Imaging 22(8):940–950PubMedCrossRef

12.

Sonka M, Park W, Hoffman E (1996) Rule-based detection of intrathoracic airway trees. IEEE Trans Med Imaging 15(3):314–326PubMedCrossRef

13.

Singh H, Crawford M, Curtin JP, Zwiggelaar R (2004) Automated 3D segmentation of the lung airway tree using gain-based region growing approach. In: MICCAI. Lecture notes in computer science, pp 975–982

14.

Kitasaka T, Mori K, Hasegawa J, Toriwaki J (2002) A method for extraction of bronchus regions from 3D branch tracing and image sharpening for airway tree chest X-ray images by analyzing structural features of the bronchus. Forma 17:321–338

15.

Tschirren J, Hoffman EA, McLennan G, Sonka M (2005) Intrathoracic airway trees: segmentation and airway morphology analysis from low dose CT scans. IEEE Trans Med Imaging 24(12):1529–1539PubMedPubMedCentralCrossRef

16.

Feuerstein M, Kitasaka T, Mori K (2009) Adaptive branch tracing and image sharpening for airway tree extraction in 3-D chest CT. In: Proceeding of 2nd international workshop on pulmonary image analysis, pp 273–284

17.

Schlathoelter T, Lorenz C, Carlsena IC, Renischa S, Deschamps T (2002) Simultaneous segmentation and tree reconstruction of the airways for virtual bronchoscopy. Proc SPIE Med Imaging 4684:103–113CrossRef

18.

Lo P, Sporring J, Ashraf H, Pedersen J, Bruijne M (2010) Vessel-guided airway tree segmentation: a voxel classification approach. Med Image Anal 14(4):527–538PubMedCrossRef

19.

Lo P, de Bruijne M (2008) Voxel classification based airway tree segmentation. Proc SPIE Med Imaging 6914:69141KCrossRef

20.

Yano H, Feuerstein M, Kitasaka T, Mori K (2009) Study on bronchus region extraction from 3D chest CT images using loca1 intensity structure analysis and CT value distribution feature. In: IEICE, MI2009-13, pp 69–74

21.

Meng Q, Kitasaka T, Nimura Y, Oda M, Mori K (2015) A study on improvement of airway segmentation using hybrid method. In: The 3rd IAPR Asian conference on pattern recognition, pp 225–229

22.

Meng Q, Kitsaka T, Nimura Y, Oda M, Mori K (2016) Accurate airway segmentation based on intensity structure analysis and graphcut. In: Proceedings of SPIE, 9784, SPIE medical imaging: computer-aided diagnosis

23.

Pratt W (1991) Digital image processing, 2nd edn. Wiley, New York

24.

Sato Y, Westin C, Bhalerao A, Nakajima S, Shiraga N, Tamura S (2000) Tissue classification based on 3D local intensity structures for volume rendering. IEEE Trans Vis Comput Graph 6(2):160–180CrossRef

25.

Sato Y, Nakajima S, Shiraga N, Atsumi H, Yoshida S, Koller T, Gerig G, Kikinis R (1998) 3D multiscale line filter for segmentation and visialization of curvilinear structures in medical images. Med Image Anal 2(2):143–168PubMedCrossRef

26.

Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. Med Image Comput Comput Assist Interv (MICCAI) 1496:130–137

27.

Krissian K, Malandain G, Ayache N (2000) Model based detection of tubular structures in 3D images. Comput Vis Image Underst 80(2):130–171CrossRef

28.

Li Q, Sone S, Doi K (2003) Selective enhancement filters for nodules, vessels, and airway walls in two and three-dimensional CT images. Med Phys 30:20–40

29.

Hirano Y, Xu R, Tachibana R, Kido S (2011) A method for extracting airway tree by using a cavity enhancement filter. In: 4th international workshop on pulmonary image analysis, pp 91–99

30.

Lesage D, Angelini ED, Bloch I, Funka-Lea G (2009) A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med Image Anal 13(6):819–845PubMedCrossRef

31.

Chang C, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27

32.

Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23(11):1222–1239CrossRef

33.

Boykov Y, Kolmogorov V (2004) An experimental comparison of minexperimental comparison of minexperimental min-cutmax-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26(9):1124–1137

34.

Ali AM, El-Baz AS, Farag AA (2007) A novel framework for accurate lung segmentation using graph cuts. In: 4th IEEE international symposium on biomedical imaging, pp 908–911

Title: Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume
Authors: Qier Meng
Takayuki Kitasaka
Yukitaka Nimura
Masahiro Oda
Junji Ueno
Kensaku Mori
Publication date: 01-02-2017
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 2/2017
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-016-1492-2

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume

Abstract

Purpose

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 2/2017

Automatic 3D liver location and segmentation via convolutional neural network and graph cut

Touchless interaction with software in interventional radiology and surgery: a systematic literature review

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI

Online time and resource management based on surgical workflow time series analysis

Automated liver segmentation from a postmortem CT scan based on a statistical shape model

Fully automated MR liver volumetry using watershed segmentation coupled with active contouring