Skip to main content
Hauptrubriken
CME Facharzt-Training Webinare Zeitschriften Bücher e.Medpedia Podcast
Service
Jobbörse Info & Hilfe
Fachgebiete
Anästhesiologie Allgemeinmedizin Arbeitsmedizin Augenheilkunde Chirurgie Dermatologie Gynäkologie und Geburtshilfe HNO Innere Medizin Kardiologie Neurologie Onkologie und Hämatologie Orthopädie und Unfallchirurgie Pädiatrie Pathologie Psychiatrie Radiologie Rechtsmedizin Urologie Zahnmedizin
Themen
Klimawandel und Gesundheit Neues aus dem Markt COVID-19 Praxis und Beruf Seltene Erkrankungen GOÄ & EBM Panorama
Sammlungen
Kasuistiken Algorithmen & Infografiken Blickdiagnosen Arthropedia FlapFinder (für Dermatochirurgen) Videos Kongressberichterstattung Medizin für Apothekerinnen und Apotheker Für Ärztinnen und Ärzte in Weiterbildung Für Medizinstudierende
Erweiterte Suche
Anmelden
nach oben
International Journal of Computer Assisted Radiology and Surgery
Erschienen 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

verfasst von: Erik Smistad, Anne C. Elster, Frank Lindseth

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 4/2014

Einloggen, um Zugang zu erhalten

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.
Fußnoten
Literatur
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
Metadaten
Titel
GPU accelerated segmentation and centerline extraction of tubular structures from medical images
verfasst von
Erik Smistad
Anne C. Elster
Frank Lindseth
Publikationsdatum
01.07.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Computer Assisted Radiology and Surgery / Ausgabe 4/2014
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-013-0956-x

Weitere Artikel der Ausgabe 4/2014

International Journal of Computer Assisted Radiology and Surgery 4/2014 Zur Ausgabe

Original Article

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

Original Article

MRI-compatible positioning device for guiding a focused ultrasound system for transrectal treatment of prostate cancer

Original Article

Using a shape prior for robust modeling of the mitral annulus on 4D ultrasound data

Original Article

A fast deformable registration method for 4D lung CT in hybrid framework

Original Article

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

Original Article

A framework for correcting brain retraction based on an eXtended Finite Element Method using a laser range scanner

Neu im Fachgebiet Radiologie

Akuter Schwindel: Wann lohnt sich eine MRT?

28.04.2024 Schwindel Nachrichten

Akuter Schwindel stellt oft eine diagnostische Herausforderung dar. Wie nützlich dabei eine MRT ist, hat eine Studie aus Finnland untersucht. Immerhin einer von sechs Patienten wurde mit akutem ischämischem Schlaganfall diagnostiziert.

Screening-Mammografie offenbart erhöhtes Herz-Kreislauf-Risiko

26.04.2024 Mammografie Nachrichten

Routinemäßige Mammografien helfen, Brustkrebs frühzeitig zu erkennen. Anhand der Röntgenuntersuchung lassen sich aber auch kardiovaskuläre Risikopatientinnen identifizieren. Als zuverlässiger Anhaltspunkt gilt die Verkalkung der Brustarterien.

S3-Leitlinie zu Pankreaskrebs aktualisiert

23.04.2024 Pankreaskarzinom Nachrichten

Die Empfehlungen zur Therapie des Pankreaskarzinoms wurden um zwei Off-Label-Anwendungen erweitert. Und auch im Bereich der Früherkennung gibt es Aktualisierungen.

Fünf Dinge, die im Kindernotfall besser zu unterlassen sind

18.04.2024 Pädiatrische Notfallmedizin Nachrichten

Im Choosing-Wisely-Programm, das für die deutsche Initiative „Klug entscheiden“ Pate gestanden hat, sind erstmals Empfehlungen zum Umgang mit Notfällen von Kindern erschienen. Fünf Dinge gilt es demnach zu vermeiden.

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen
Bildnachweise