Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
International Journal of Computer Assisted Radiology and Surgery
Published in: International Journal of Computer Assisted Radiology and Surgery 4/2014

01-07-2014 | Original Article

GPU accelerated segmentation and centerline extraction of tubular structures from medical images

Authors: Erik Smistad, Anne C. Elster, Frank Lindseth

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 4/2014

Login to get access

Abstract

Purpose

   To create a fast and generic method with sufficient quality for extracting tubular structures such as blood vessels and airways from different modalities (CT, MR and US) and organs (brain, lungs and liver) by utilizing the computational power of graphic processing units (GPUs).

Methods

   A cropping algorithm is used to remove unnecessary data from the datasets on the GPU. A model-based tube detection filter combined with a new parallel centerline extraction algorithm and a parallelized region growing segmentation algorithm is used to extract the tubular structures completely on the GPU. Accuracy of the proposed GPU method and centerline algorithm is compared with the ridge traversal and skeletonization/thinning methods using synthetic vascular datasets.

Results

   The implementation is tested on several datasets from three different modalities: airways from CT, blood vessels from MR, and 3D Doppler Ultrasound. The results show that the method is able to extract airways and vessels in 3–5 s on a modern GPU and is less sensitive to noise than other centerline extraction methods.

Conclusions

   Tubular structures such as blood vessels and airways can be extracted from various organs imaged by different modalities in a matter of seconds, even for large datasets.
Footnotes
Literature
1.
2.
Aylward SR, Bullitt E (2002) Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction. IEEE Trans Med Imaging 21(2):61–75PubMedCrossRef
3.
Bauer C (2010) Segmentation of 3D tubular tree structures in medical images. PhD thesis, Graz University of Technology
4.
Bauer C, Bischof V (2008) A novel approach for detection of tubular objects and its application to medical image analysis. In: Proceedings of the 30th DAGM symposium on pattern recognition. Springer, pp 163–172
5.
6.
Bauer C, Bischof H (2008) Extracting curve skeletons from gray value images for virtual endoscopy. In: Proceedings of the 4th international workshop on medical imaging and augmented reality. Springer, pp 393–402
7.
Bauer C, Bischof H, Beichel R (2009) Segmentation of airways based on gradient vector flow. In: Proceedings of the 2nd international workshop on pulmonary image analysis. MICCAI, Citeseer, pp 191–201
8.
Bauer C, Pock T, Bischof H, Beichel R (2009) Airway tree reconstruction based on tube detection. In: Proceedings of the 2nd international workshop on pulmonary image analysis. MICCAI, Citeseer, pp 203–214
9.
Behrens T, Rohr K, Stiehl HS (2003) Robust segmentation of tubular structures in 3-D medical images by parametric object detection and tracking. IEEE Trans Syst Man Cybern Part B Cybern Publ IEEE Syst Man Cybern Soc 33(4):554–61
10.
Benmansour F, Cohen LD (2010) Tubular structure segmentation based on minimal path method and anisotropic enhancement. Int J Comput Vis 92(2):192–210CrossRef
11.
Billeter M, Olsson O, Assarsson U (2009) Efficient stream compaction on wide SIMD many-core architectures. In: Proceedings of the conference on high performance graphics, pp 159–166
12.
Cohen LD, Deschamps T (2007) Segmentation of 3D tubular objects with adaptive front propagation and minimal tree extraction for 3D medical imaging. Comput Method Biomech Biomed Eng 10(4):289–305CrossRef
13.
Eidheim O, Skjermo J, Aurdal L (2005) Real-time analysis of ultrasound images using GPU. Int Congr Ser 1281:284–289CrossRef
14.
Erdt M, Raspe M, Suehling M (2008) Automatic hepatic vessel segmentation using graphics hardware. In: Proceedings of the 4th international workshop on medical imaging and augmented reality, pp 403–412
15.
Frangi A, Niessen W, Vincken K, Viergever M (1998) Multiscale vessel enhancement filtering. Med Image Comput Comput Assist Interv 1496:130–137
16.
Graham MW, Gibbs JD, Cornish DC (2010) Robust 3-D airway tree segmentation for image-guided peripheral bronchoscopy. IEEE Trans Med Imaging 29(4):982–997PubMedCrossRef
17.
Hamarneh G, Jassi P (2010) VascuSynth: simulating vascular trees for generating volumetric image data with ground-truth segmentation and tree analysis. Comput Med Imaging Graph 34(8):605–616PubMedCrossRef
18.
Hassouna M., Farag A. (2007) On the extraction of curve skeletons using gradient vector flow. In: IEEE 11th international conference on computer vision. IEEE, pp 1–8
19.
Hawick K, Leist A, Playne D (2010) Parallel graph component labelling with GPUs and CUDA. Parallel Comput 36(12):655–678CrossRef
20.
He Z, Kuester F (2006) GPU-based active contour segmentation using gradient vector flow. In: Advances in visual, computing, pp 191–201
21.
Helmberger M, Urschler M, Pienn M, Bálint Z, Olschewski A, Bischof H (2013) Pulmonary vascular tree segmentation from contrast-enhanced CT images. In: Proceedings of the 37th annual workshop of the austrian association for, pattern recognition, pp 1–10
22.
23.
24.
Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331CrossRef
25.
Kirbas C, Quek F (2004) A review of vessel extraction techniques and algorithms. ACM Comput Surv 36(2):81–121CrossRef
26.
Krissian K, Malandain G, Ayache N (2000) Model-based detection of tubular structures in 3D images. Comput Vis Image Underst 80(2):130–171CrossRef
27.
Law T-Y, Heng PA (2000) Automated extraction of bronchus from 3D CT images of lung based on genetic algorithm and 3D region growing. Proc SPIE 3979:906–916CrossRef
28.
Lee T, Kashyap R, Chu C (1994) Building skeleton models via 3-D medial surface/axis thinning algorithms. CVGIP Graph Model Image Processing 56(6):462–478CrossRef
29.
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
30.
Li H, Yezzi A (2007) Vessels as 4-D curves: global minimal 4-D paths to extract 3-D tubular surfaces and centerlines. IEEE Trans Med Imaging 29(9):1213–1223CrossRef
31.
Lo P, Ginneken BV, Reinhardt JM, de Bruijne M (2009) Extraction of airways from CT (EXACT’09) . In: Second international workshop on pulmonary image, analysis, pp 175–189
32.
Lo P, Sporring J, Ashraf H, Pedersen JJH, de Bruijne M (2010) Vessel-guided airway tree segmentation: a voxel classification approach. Med Image Anal 14(4):527–538PubMedCrossRef
33.
Lorigo L, Faugeras O (2000) Codimension-two geodesic active contours for the segmentation of tubular structures. Comput Vis Pattern Recognit, 444–451
34.
Maintz JBA, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2(1):1–36
35.
Malladi R, Sethian J, Vemuri B (1995) Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Machine Intell 17(2):158–175CrossRef
36.
Narayanaswamy A, Dwarakapuram S, Bjornsson CS, Cutler BM, Shain W, Roysam B (2010) Robust adaptive 3-D segmentation of vessel laminae from fluorescence confocal microscope images and parallel GPU implementation. IEEE Trans Med Imaging 29(3):583–597PubMedCentralPubMedCrossRef
37.
38.
Reinertsen I, Lindseth F, Unsgaard G, Collins DL (2007) Clinical validation of vessel-based registration for correction of brain-shift. Med Image Anal 11(6):673–684PubMedCrossRef
39.
Shi L, Liu W, Zhang H, Xie Y, Wang D (2012) A survey of GPU-based medical image computing techniques. Quant Imaging Med Surg 2(3):188–206PubMedCentralPubMed
40.
Sluimer I, Schilham A, Prokop M, van Ginneken B (2006) Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans Med Imaging 25(4):385–405PubMedCrossRef
41.
Smistad E, Elster AC, Lindseth F (2012) GPU-based airway segmentation and centerline extraction for image guided bronchoscopy. In Norsk informatikkonferanse. Akademika forlag, pp 129–140
42.
43.
Smistad E, Elster AC, Lindseth F (2012) Real-time surface extraction and visualization of medical images using OpenCL and GPUs. In: Norsk informatikkonferanse. Akademika forlag, pp 141–152
44.
Spuhler C, Harders M, Székely G (2006) Fast and robust extraction of centerlines in 3D tubular structures using a scattered–snakelet approach. Proc SPIE 6144, March 2006
45.
van Ginneken B, Baggerman W, van Rikxoort EM (2008) Robust segmentation and anatomical labeling of the airway tree from thoracic CT scans. Int Conf Med Image Comput Comput Assist Interv 11:219–226
46.
Vasilevskiy A, Siddiqi K (2002) Flux maximizing geometric flows. IEEE Trans Pattern Anal Mach Intell 24(12):1565–1578CrossRef
47.
Xu C, Prince J (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Processing 7(3):359–369CrossRef
48.
Zheng Z, Zhang R (2012) A fast GVF snake algorithm on the GPU. Res J Appl Sci Eng Technol 4(24):5565–5571
49.
Ziegler G, Tevs A, Theobalt C, Seidel H (2006) On-the-fly point clouds through histogram pyramids. In Vision, modeling, and visualization 2006: proceedings, Nov 22–24, 2006. IOS Press, Aachen, Germany, pp 137
Metadata
Title
GPU accelerated segmentation and centerline extraction of tubular structures from medical images
Authors
Erik Smistad
Anne C. Elster
Frank Lindseth
Publication date
01-07-2014
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 4/2014
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-013-0956-x

Other articles of this Issue 4/2014

International Journal of Computer Assisted Radiology and Surgery 4/2014 Go to the issue

Original Article

Automated implant segmentation in cone-beam CT using edge detection and particle counting

Original Article

A low-cost tracked C-arm (TC-arm) upgrade system for versatile quantitative intraoperative imaging

Original Article

Deformable image registration for temporal subtraction of chest radiographs

Original Article

Automated liver lesion detection in CT images based on multi-level geometric features

Original Article

Evaluation of novice reader diagnostic performance in coronary CT angiography using an advanced cardiac software package

Original Article

A morphological study of anatomical plates for acetabular posterior column