Top

International Journal of Computer Assisted Radiology and Surgery

Published in:

01-06-2019 | Original Article

Toward an automatic preoperative pipeline for image-guided temporal bone surgery

Authors: Johannes Fauser, Igor Stenin, Markus Bauer, Wei-Hung Hsu, Julia Kristin, Thomas Klenzner, Jörg Schipper, Anirban Mukhopadhyay

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 6/2019

Abstract

Purpose

Minimally invasive surgery is often built upon a time-consuming preoperative step consisting of segmentation and trajectory planning. At the temporal bone, a complete automation of these two tasks might lead to faster interventions and more reproducible results, benefiting clinical workflow and patient health.

Methods

We propose an automatic segmentation and trajectory planning pipeline for image-guided interventions at the temporal bone. For segmentation, we use a shape regularized deep learning approach that is capable of automatically detecting even the cluttered tiny structures specific for this anatomy. We then perform trajectory planning for both linear and nonlinear interventions on these automatically segmented risk structures.

Results

We evaluate the usability of segmentation algorithms for planning access canals to the cochlea and the internal auditory canal on 24 CT data sets of real patients. Our new approach achieves similar results to the existing semiautomatic method in terms of Dice but provides more accurate organ shapes for the subsequent trajectory planning step. The source code of the algorithms is publicly available.

Conclusion

Automatic segmentation and trajectory planning for various clinical procedures at the temporal bone are feasible. The proposed automatic pipeline leads to an efficient and unbiased workflow for preoperative planning.

Available only for authorised users

https://github.com/MECLabTUDA/MUKNO.git, www.gris.tu-darmstadt.de/short/IPCAI2019weights.

Becker M, Kirschner M, Sakas G (2014) Segmentation of risk structures for otologic surgery using the probabilistic active shape model (pasm). Proc SPIE 9036:9036–7

Besl PJ, McKay ND (1992) A method for registration of 3-d shapes. IEEE Trans Pattern Anal Mach Int 14(2):239–256CrossRef

Caversaccio M, Gavaghan K, Wimmer W, Williamson T, Ansò J, Mantokoudis G, Gerber N, Rathgeb C, Feldmann A, Wagner F, Scheidegger O, Kompis M, Weisstanner C, Zoka-Assadi M, Roesler K, Anschuetz L, Huth M, Weber S (2017) Robotic cochlear implantation: surgical procedure and first clinical experience. Acta Oto Laryngol 137(4):447–454CrossRef

Cootes T, Taylor C, Cooper D, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59CrossRef

Dahroug B, Tamadazte B, Weber S, Tavernier L, Andreff N (2018) Review on otological robotic systems: toward microrobot-assisted cholesteatoma surgery. IEEE Rev Biomed Eng 11:125–142CrossRefPubMed

Fauser J, Stenin I, Kristin J, Klenzner T, Schipper J, Sakas G (2016) A software tool for planning and evaluation of non-linear trajectories for minimally invasive lateral skull base surgery. In: Tagungsb. der 15. Jahrestag. der Dtsch. Ges. f. Comput.- und Roboterass. Chirurgie e.V. (CURAC), pp 125–126

Fauser J, Sakas G, Mukhopadhyay A (2018) Planning nonlinear access paths for temporal bone surgery. Int J Comput Assist Radiol Surg 13(5):637–646CrossRefPubMed

Fauser J, Stenin I, Kristin J, Klenzner T, Schipper J, Fellner D, Mukhopadhyay A (2018) Generalized trajectory planning for nonlinear interventions. In: OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis, Springer International Publishing, Cham, pp 46–53

Ferreira A, Tavares JMRS, Gentil F (2012) A review of segmentation algorithms for ear image data. In: 7th Iberian conference on information systems and technologies (CISTI 2012), pp 1–6

10.

Fichera L, Dillon NP, Zhang D, Godage IS, Siebold MA, Hartley BI, Noble JH, Russell PT, Labadie RF, Webster RJ (2017) Through the eustachian tube and beyond: a new miniature robotic endoscope to see into the middle ear. IEEE Robot Autom Lett 2(3):1488–1494CrossRefPubMedCentralPubMed

11.

Gerber N, Bell B, Gavaghan K, Weisstanner C, Caversaccio M, Weber S (2014) Surgical planning tool for robotically assisted hearing aid implantation. Int J Comput Assist Radiol Surg 9(1):11–20CrossRefPubMed

12.

Gerber N, Reyes M, Barazzetti L, Kjer HM, Vera S, Stauber M, Mistrik P, Ceresa M, Mangado N, Wimmer W, Stark T, Paulsen RR, Weber S, Caversaccio M, Ballester MAG (2017) A multiscale imaging and modelling dataset of the human inner ear. Sci Data 4:170132CrossRefPubMedCentralPubMed

13.

Kirschner M (2013) The probabilistic active shape model: from model construction to flexible medical image segmentation. Ph.D. thesis, Technische Universität, Darmstadt

14.

Kjer HM, Fagertun J, Vera S, Gil D, Ángel González Ballester M, Paulsen RR (2016) Free-form image registration of human cochlear mu ct data using skeleton similarity as anatomical prior. Pattern Recogn Lett 76:76–82 (special issue on Skeletonization and its application) CrossRef

15.

Labadie RF, Balachandran R, Noble JH, Blachon GS, Mitchell JE, Reda FA, Dawant BM, Fitzpatrick JM (2014) Minimally invasive image-guided cochlear implantation surgery: first report of clinical implementation. The Laryngoscope 124(8):1915–1922CrossRefPubMedCentralPubMed

16.

Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3d surface construction algorithm. In: Proceedings of the 14th annual conference on computer graphics and interactive techniques, ACM, New York, NY, USA, SIGGRAPH ’87, pp 163–169

17.

Lu P, Barazzetti L, Chandran V, Gavaghan K, Weber S, Gerber N, Reyes M (2018) Highly accurate facial nerve segmentation refinement from CBCT/CT imaging using a super-resolution classification approach. IEEE Trans Biomed Eng 65(1):178–188CrossRefPubMed

18.

Moghaddam B, Pentland A (1997) Probabilistic visual learning for object representation. IEEE Trans Pattern Anal Mach Intell 19(7):696–710CrossRef

19.

Noble JH, Dawant BM (2011) An atlas-navigated optimal medial axis and deformable model algorithm (nomad) for the segmentation of the optic nerves and chiasm in mr and ct images. Med Image Anal 15(6):877–884CrossRefPubMedCentralPubMed

20.

Noble JH, Warren FM, Labadie RF, Dawant BM (2008) Automatic segmentation of the facial nerve and chorda tympani in ct images using spatially dependent feature values. Med Phys 35(12):5375–5384CrossRefPubMedCentralPubMed

21.

Noble JH, Labadie RF, Majdani O, Dawant BM (2011) Automatic segmentation of intracochlear anatomy in conventional ct. IEEE Trans Biomed Eng 58(9):2625–2632CrossRefPubMedCentralPubMed

22.

Powell KA, Liang T, Hittle B, Stredney D, Kerwin T, Wiet GJ (2017) Atlas-based segmentation of temporal bone anatomy. Int J Comput Assist Radiol Surg 12(11):1937–1944CrossRefPubMedCentralPubMed

23.

Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. Springer, Cham, pp 234–241

24.

Ruiz Pujadas E, Piella G, Kjer HM, González Ballester MA (2018) Random walks with statistical shape prior for cochlea and inner ear segmentation in micro-ct images. Mach Vis Appl 29(3):405–414CrossRef

25.

Stenin I, Hansen S, Becker M, Sakas G, Fellner D, Klenzner T, Schipper J (2014) Minimally invasive multi-port surgery of the lateral skull base. BioMed Res Int 2014:7CrossRef

26.

Tack A, Mukhopadhyay A, Zachow S (2018) Knee menisci segmentation using convolutional neural networks: data from the osteoarthritis initiative. Osteoarthr Cartil 26(5):680–688CrossRefPubMed

27.

Torres R, Kazmitcheff G, De Seta D, Ferrary E, Sterkers O, Nguyen Y (2017) Improvement of the insertion axis for cochlear implantation with a robot-based system. Eur Arch Oto-Rhino-Laryngol 274(2):715–721CrossRef

28.

Voormolen E, van Stralen M, Woerdeman PA, Pluim JJ, Noordman H, Viergever MM, Regli L, van der Sprenkel JB (2012) Determination of a facial nerve safety zone for navigated temporal bone surgery. Neurosurgery 70(1):50PubMed

29.

Weber S, Gerber N, Gavaghan KA, Williamson T, Wimmer W, Anso J, Brogna-Salas L, Chen D, Weisstanner C, Caversaccio M, Bell B (2013) Image guided and robotic assisted minimally invasive cochlear implantation. In: The Hamlyn symposium on medical robotics, pp 17–18

30.

Xianfen D, Siping C, Changhong L, Yuanmei W (2005) 3d semi-automatic segmentation of the cochlea and inner ear. In: 2005 IEEE engineering in medicine and biology 27th annual conference, pp 6285–6288

31.

Zhu S, Gao W, Zhang Y, Zheng J, Liu Z, Yuan G (2017) 3D automatic mri level set segmentation of inner ear based on statistical shape models prior. In: 2017 10th International congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), pp 1–6

Title: Toward an automatic preoperative pipeline for image-guided temporal bone surgery
Authors: Johannes Fauser
Igor Stenin
Markus Bauer
Wei-Hung Hsu
Julia Kristin
Thomas Klenzner
Jörg Schipper
Anirban Mukhopadhyay
Publication date: 01-06-2019
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 6/2019
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-019-01937-x

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Toward an automatic preoperative pipeline for image-guided temporal bone surgery

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 6/2019

Active learning using deep Bayesian networks for surgical workflow analysis

Preliminary study of an RNN-based active interventional robotic system (AIRS) in retinal microsurgery

Objective assessment of intraoperative technical skill in capsulorhexis using videos of cataract surgery

Deep neural maps for unsupervised visualization of high-grade cancer in prostate biopsies

Catheter localization in 3D ultrasound using voxel-of-interest-based ConvNets for cardiac intervention

On the feasibility of transperineal 3D ultrasound image guidance for robotic radical prostatectomy