nach oben

International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

21.09.2016 | Original Article

A framework for automatic creation of gold-standard rigid 3D–2D registration datasets

verfasst von: Hennadii Madan, Franjo Pernuš, Boštjan Likar, Žiga Špiclin

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 2/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Purpose

Advanced image-guided medical procedures incorporate 2D intra-interventional information into pre-interventional 3D image and plan of the procedure through 3D/2D image registration (32R). To enter clinical use, and even for publication purposes, novel and existing 32R methods have to be rigorously validated. The performance of a 32R method can be estimated by comparing it to an accurate reference or gold standard method (usually based on fiducial markers) on the same set of images (gold standard dataset). Objective validation and comparison of methods are possible only if evaluation methodology is standardized, and the gold standard dataset is made publicly available. Currently, very few such datasets exist and only one contains images of multiple patients acquired during a procedure. To encourage the creation of gold standard 32R datasets, we propose an automatic framework.

Methods

The framework is based on rigid registration of fiducial markers. The main novelty is spatial grouping of fiducial markers on the carrier device, which enables automatic marker localization and identification across the 3D and 2D images.

Results

The proposed framework was demonstrated on clinical angiograms of 20 patients. Rigid 32R computed by the framework was more accurate than that obtained manually, with the respective target registration error below 0.027 mm compared to 0.040 mm.

Conclusion

The framework is applicable for gold standard setup on any rigid anatomy, provided that the acquired images contain spatially grouped fiducial markers. The gold standard datasets and software will be made publicly available.

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

Nächster Artikel Fiducial-based registration with a touchable region model

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

The distance between the closest elements of the clusters.

We assumed that the projection of spherical marker is circular, and the same circular template, sampled at the resolution of the X-ray image, is used to detect markers across the entire 2D gradient direction image. In general, the projections of ball-shaped objects are not circular but rather elliptic, the more eccentric the further away they lie from the projection axis. Under our C-arm projection geometry, the ratio of major and minor marker semi-axes was always less than 1.037, which is negligible and did not have an impact on marker localization.

In all 3D and 2D the images, the fiducial markers were masked and cubic spline interpolation was used to impaint new intensity values. Hence, markers are not visible and thus cannot bias the registration.

Al-Sharadqah A, Chernov N (2009) Error analysis for circle fitting algorithms. Electron J Stat 3:886–911. doi:10.1214/09-EJS419 CrossRef

Atherton TJ, Kerbyson DJ (1999) Size invariant circle detection. Image Vis Comput 17(11):795–803CrossRef

Baka N, Metz CT, Schultz CJ, van Geuns RJ, Niessen WJ, van Walsum T (2014) Oriented Gaussian mixture models for nonrigid 2D/3D coronary artery registration. IEEE Trans Med Imaging 33(5):1023–1034. doi:10.1109/TMI.2014.2300117 PubMedCrossRef

Bose CB, Amir I (1990) Design of fiducials for accurate registration using machine vision. IEEE Trans Pattern Anal 12(12):1196–1200. doi:10.1109/34.62609 CrossRef

Brandenberger D, Birkfellner W, Baumann B, Messmer P, Huegli RW, Regazzoni P, Jacob AL (2007) Positioning accuracy in a registration-free CT-based navigation system. Phys Med Biol 52(23):7073PubMedCrossRef

Chauris H, Karoui I, Garreau P, Wackernagel H, Craneguy P, Bertino L (2011) The circlet transform: a robust tool for detecting features with circular shapes. Comput Geosci 37(3):331–342. doi:10.1016/j.cageo.2010.05.009 CrossRef

Chen K, Wu J (2014) One-dimensional voting scheme for circle and arc detection. J Opt Soc Am A 31(12):2593. doi:10.1364/JOSAA.31.002593 CrossRef

Dang H, Otake Y, Schafer S, Stayman JW, Kleinszig G, Siewerdsen JH (2012) Robust methods for automatic image-to-world registration in cone-beam CT interventional guidance. Med Phys 39(10):6484. doi:10.1118/1.4754589 PubMedPubMedCentralCrossRef

Demirci S, Baust M, Kutter O, Manstad-Hulaas F, Eckstein HH, Navab N (2013) Disocclusion-based 2D–3D registration for aortic interventions. Comput Biol Med 43(4):312–322PubMedCrossRef

10.

Dumay A, Reiber J, Gerbrands J (1994) Determination of optimal angiographic viewing angles: basic principles and evaluation study. IEEE Trans Med Imaging 13(1):13–24. doi:10.1109/42.276141 PubMedCrossRef

11.

Fattori G, Riboldi M, Desplanques M, Tagaste B, Pella A, Orecchia R, Baroni G (2012) Automated fiducial localization in CT images based on surface processing and geometrical prior knowledge for radiotherapy applications. IEEE Trans Bio-Med Eng 59(8):2191–2199. doi:10.1109/TBME.2012.2198822 CrossRef

12.

Fitzpatrick JM, West JB, Maurer CR Jr (1998) Predicting error in rigid-body point-based registration. IEEE Trans Med Imaging 17(5):694–702PubMedCrossRef

13.

Fledelius W, Worm E, Elstrøm UV, Petersen JB, Grau C, Høyer M, Poulsen PR (2011) Robust automatic segmentation of multiple implanted cylindrical gold fiducial markers in cone-beam CT projections. Med Phys 38(12):6351–6361PubMedCrossRef

14.

Granger S, Pennec X (2006) Multi-scale EM-ICP: a fast and robust approach for surface registration. In: Computer vision—ECCV 2002, pp 69–73

15.

Hamming NM, Daly MJ, Irish JC, Siewerdsen JH (2009) Automatic image-to-world registration based on X-ray projections in cone-beam CT-guided interventions. Med Phys 36(5):1800–1812PubMedPubMedCentralCrossRef

16.

Jacob AL, Regazzoni P, Bilecen D, Rasmus M, Huegli RW, Messmer P (2007) Medical technology integration: CT, angiography, imaging-capable OR-table, navigation and robotics in a multifunctional sterile suite. Minim Invasive Ther Allied Technol 16(4):205–211. doi:10.1080/13645700701520628 PubMedCrossRef

17.

Jannin P, Fitzpatrick J, Hawkes D, Pennec X, Shahidl R, Vannier M (2002) Validation of medical image processing in image-guided therapy. IEEE Trans Med Imaging 21(12):1445–1449. doi:10.1109/TMI.2002.806568 PubMedCrossRef

18.

Jennings AL, Black J, Allen C (2013) Empirically bounding of space booms with tape spring hinges. Shock Vib 20(3):503–517CrossRef

19.

Madan H, Likar B, Pernuš F, Špiclin Ž (2015) Device and methods for ”gold standard” registration of clinical 3D and 2D cerebral angiograms. In: SPIE medical imaging, p 94151G. doi:10.1117/12.2081908

20.

Maintz JBA, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2(1):1–36. doi:10.1016/S1361-8415(01)80026-8 PubMedCrossRef

21.

Mao W, Wiersma RD, Xing L (2008) Fast internal marker tracking algorithm for onboard MV and kV imaging systems. Med Phys 35(5):1942. doi:10.1118/1.2905225 PubMedPubMedCentralCrossRef

22.

Markelj P, Likar B, Pernuš F (2010) Standardized evaluation methodology for 3D/2D registration based on the Visible Human data set. Med Phys 37(9):4643. doi:10.1118/1.3476414 PubMedCrossRef

23.

Markelj P, Tomaževič D, Likar B, Pernuš F (2012) A review of 3D/2D registration methods for image-guided interventions. Med Image Anal 16(3):642–661. doi:10.1016/j.media.2010.03.005 PubMedCrossRef

24.

Mertzanidou T, Hipwell J, Johnsen S, Han L, Eiben B, Taylor Z, Ourselin S, Huisman H, Mann R, Bick U et al (2014) MRI to X-ray mammography intensity-based registration with simultaneous optimisation of pose and biomechanical transformation parameters. Med Image Anal 18(4):674–683PubMedCrossRef

25.

Miquel ME, Rhode KS, Acher PL, MacDougall ND, Blackall J, Gaston RP, Hegde S, Morris SL, Beaney R, Deehan C et al (2006) Using combined X-ray and MR imaging for prostate I-125 post-implant dosimetry: phantom validation and preliminary patient work. Phys Med Biol 51(5):1129PubMedCrossRef

26.

Mitrovic U, Spiclin Z, Likar B, Pernus F (2013) 3D–2D Registration of cerebral angiograms: a method and evaluation on clinical images. IEEE Trans Med Imaging 32(8):1550–1563. doi:10.1109/TMI.2013.2259844 PubMedCrossRef

27.

Mitschke M, Navab N (2003) Recovering the X-ray projection geometry for three-dimensional tomographic reconstruction with additional sensors: attached camera versus external navigation system. Med Image Anal 7(1):65–78PubMedCrossRef

28.

Muenzing SEA, van Ginneken B, Pluim JPW (2010) Knowledge driven regularization of the deformation field for PDE based non-rigid registration algorithms. Medical image analysis for the clinic-a grand challenge, pp 127–136

29.

Myronenko A, Song X (2010) Point set registration: Coherent point drift. IEEE Trans Pattern Anal 32(12):2262–2275. doi:10.1109/TPAMI.2010.46 CrossRef

30.

Navab N, Heining SM, Traub J (2010) Camera augmented mobile C-arm (CAMC): calibration, accuracy study, and clinical applications. IEEE Trans Med Imaging 29(7):1412–1423PubMedCrossRef

31.

Ni J, Singh M, Bahlmann C (2012) Fast radial symmetry detection under affine transformations. In: Proceedings of the CVPR IEEE, pp 932–939. doi:10.1109/CVPR.2012.6247768

32.

Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11(285–296):23–27

33.

Pawiro SA, Markelj P, Pernuš F, Gendrin C, Figl M, Weber C, Kainberger F, Nöbauer-Huhmann I, Bergmeister H, Stock M, Georg D, Bergmann H, Birkfellner W (2011) Validation for 2D/3D registration I: a new gold standard data set. Med Phys 38(3):1481. doi:10.1118/1.3553402 PubMedPubMedCentralCrossRef

34.

Peters T, Cleary K (2008) Image-guided interventions: technology and applications. Springer, BerlinCrossRef

35.

Poulsen PR, Fledelius W, Keall PJ, Weiss E, Lu J, Brackbill E, Hugo GD (2011) A method for robust segmentation of arbitrarily shaped radiopaque structures in cone-beam CT projections. Med Phys 38(4):2151. doi:10.1118/1.3555295 PubMedPubMedCentralCrossRef

36.

Powell MJ (1964) An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput J 7(2):155–162CrossRef

37.

Shechter G, Shechter B, Resar J, Beyar R (2005) Prospective motion correction of X-ray images for coronary interventions. IEEE Trans Med Imaging 24(4):441–450. doi:10.1109/TMI.2004.839679 PubMedCrossRef

38.

Siebold MA, Dillon NP, Webster RJ, Fitzpatrick JM (2015) Incorporating target registration error into robotic bone milling. In: SPIE medical imaging, international society for optics and photonics, pp 94,150R–94,150R

39.

Tomaževič D, Likar B, Pernuš F (2004) “Gold standard” data for evaluation and comparison of 3D/2D registration methods. Comput Aided Surg 9(4):137–144PubMedCrossRef

40.

van de Kraats E, Penney G, Tomazevic D, Van Walsum T, Niessen W (2005) Standardized evaluation methodology for 2-D–3-D registration. IEEE Trans Med Imaging 24(9):1177–1189. doi:10.1109/TMI.2005.853240 PubMedCrossRef

41.

Varnavas A, Carrell T, Penney G (2013) Increasing the automation of a 2D–3D registration system. IEEE Trans Med Imaging 32(2):387–399PubMedCrossRef

42.

Vermandel M, Betrouni N, Gauvrit JY, Pasquier D, Vasseur C, Rousseau J (2006) Intrinsic 2D/3D registration based on a hybrid approach: use in the radiosurgical imaging process. Cell Mol Biol 52(6):44–53

43.

Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58(301):236–244. doi:10.1080/01621459.1963.10500845 CrossRef

44.

Yaniv Z (2009) Localizing spherical fiducials in C-arm based cone-beam CT. Med Phys 36(11):4957–4966PubMedCrossRef

Titel: A framework for automatic creation of gold-standard rigid 3D–2D registration datasets
verfasst von: Hennadii Madan
Franjo Pernuš
Boštjan Likar
Žiga Špiclin
Publikationsdatum: 21.09.2016
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 2/2017
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-016-1482-4

Springer Professional

Abstract

Purpose

Methods

Results

Conclusion

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 2/2017

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

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

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

Principal components of wrist circumduction from electromagnetic surgical tracking

Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans

Premium Partner