nach oben

International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

11.12.2019 | Original Article

Kidney edge detection in laparoscopic image data for computer-assisted surgery

Kidney edge detection

verfasst von: Georges Hattab, Marvin Arnold, Leon Strenger, Max Allan, Darja Arsentjeva, Oliver Gold, Tobias Simpfendörfer, Lena Maier-Hein, Stefanie Speidel

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 3/2020

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Purpose

In robotic-assisted kidney surgery, computational methods make it possible to augment the surgical scene and potentially improve patient outcome. Most often, soft-tissue registration is a prerequisite for the visualization of tumors and vascular structures hidden beneath the surface. State-of-the-art volume-to-surface registration methods, however, are computationally demanding and require a sufficiently large target surface. To overcome this limitation, the first step toward registration is the extraction of the outer edge of the kidney.

Methods

To tackle this task, we propose a deep learning-based solution. Rather than working only on the raw laparoscopic images, the network is given depth information and distance fields to predict whether a pixel of the image belongs to an edge. We evaluate our method on expert-labeled in vivo data from the EndoVis sub-challenge 2017 Kidney Boundary Detection and define the current state of the art.

Results

By using a leave-one-out cross-validation, we report results for the most suitable network with a median precision-like, recall-like, and intersection over union (IOU) of 39.5 px, 143.3 px, and 0.3, respectively.

Conclusion

We conclude that our approach succeeds in predicting the edges of the kidney, except in instances where high occlusion occurs, which explains the average decrease in the IOU score. All source code, reference data, models, and evaluation results are openly available for download: https://github.com/ghattab/kidney-edge-detection/.

Nächster Artikel Automated measurement of bone scan index from a whole-body bone scintigram

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

Nur mit Berechtigung zugänglich

http://endovissub2017-kidneyboundarydetection.grand-challenge.org/.

Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916CrossRef

Bernhardt S, Nicolau SA, Soler L, Doignon C (2017) The status of augmented reality in laparoscopic surgery as of 2016. Med Image Anal 37:66–90CrossRef

Collins T, Bartoli A, Bourdel N, Canis M (2016) Robust, real-time, dense and deformable 3D organ tracking in laparoscopic videos. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 404–412

Cui N, Kharel P, Gruev V (2017) Augmented reality with Microsoft HoloLens holograms for near infrared fluorescence based image guided surgery. In: Molecular-guided surgery: molecules, devices, and applications III. International Society for Optics and Photonics, vol 10049, p 100490I

Dawkins M, Sherrill L, Fieldhouse K, Hoogs A, Richards B, Zhang D, Prasad L, Williams K, Lauffenburger N, Wang G (2017) An open-source platform for underwater image and video analytics. In: 2017 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 898–906

Eitel A, Springenberg JT, Spinello L, Riedmiller M, Burgard W (2015) Multimodal deep learning for robust RGB-D object recognition. In: 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 681–687

Haouchine N, Dequidt J, Peterlik I, Kerrien E, Berger MO, Cotin S (2014) Towards an accurate tracking of liver tumors for augmented reality in robotic assisted surgery. In: International conference on robotics and automation (ICRA)

Hirschmuller H (2008) Stereo processing by semiglobal matching and mutual information. IEEE Trans Pattern Anal Mach Intell 30(2):328–341CrossRef

Hughes-Hallett A, Mayer EK, Marcus HJ, Cundy TP, Pratt PJ, Darzi AW, Vale JA (2014) Augmented reality partial nephrectomy: examining the current status and future perspectives. Urology 83(2):266–273CrossRef

10.

Koo B, Özgür E, Le Roy B, Buc E, Bartoli A (2017) Deformable registration of a preoperative 3D liver volume to a laparoscopy image using contour and shading cues. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 326–334

11.

Lee TC, Kashyap RL, Chu CN (1994) Building skeleton models via 3-D medial surface axis thinning algorithms. CVGIP Graph Models Image Process 56(6):462–478CrossRef

12.

Liu Y, Cheng MM, Hu X, Wang K, Bai X (2017) Richer convolutional features for edge detection. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 5872–5881

13.

Maier-Hein L, Speidel S, Stenau E, Chen EC, Ma B (2018) Registration. In: Peters TM, Linte CA, Yaniv Z, William J (eds) Mixed and augmented reality in medicine. CRC Press, Boca Raton, pp 29–45CrossRef

14.

Pratt P, Mayer E, Vale J, Cohen D, Edwards E, Darzi A, Yang GZ (2012) An effective visualisation and registration system for image-guided robotic partial nephrectomy. J Robot Surg 6(1):23–31CrossRef

15.

Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241

16.

Sadykova D, James AP (2018) Quality assessment metrics for edge detection and edge-aware filtering: a tutorial review. CoRR arXiv:1801.00454

17.

Simpfendörfer T, Gasch C, Hatiboglu G, Muller M, Maier-Hein L, Hohenfellner M, Teber D (2016) Intraoperative computed tomography imaging for navigated laparoscopic renal surgery: first clinical experience. J Endourol 30(10):1105–1111CrossRef

18.

Su LM, Vagvolgyi BP, Agarwal R, Reiley CE, Taylor RH, Hager GD (2009) Augmented reality during robot-assisted laparoscopic partial nephrectomy: toward real-time 3D-CT to stereoscopic video registration. Urology 73(4):896–900CrossRef

19.

Yang J, Price B, Cohen S, Lee H, Yang MH (2016) Object contour detection with a fully convolutional encoder-decoder network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 193–202

20.

Zhang T, Suen CY (1984) A fast parallel algorithm for thinning digital patterns. Commun ACM 27(3):236–239CrossRef

Titel: Kidney edge detection in laparoscopic image data for computer-assisted surgery
Kidney edge detection
verfasst von: Georges Hattab
Marvin Arnold
Leon Strenger
Max Allan
Darja Arsentjeva
Oliver Gold
Tobias Simpfendörfer
Lena Maier-Hein
Stefanie Speidel
Publikationsdatum: 11.12.2019
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 3/2020
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-019-02102-0

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 3/2020

In vivo static and dynamic lengthening measurements of the posterior cruciate ligament at high knee flexion angles

Ultrasound needle segmentation and trajectory prediction using excitation network

Modified radial-search algorithm for segmentation of tibiofemoral cartilage in MR images of patients with subchondral lesion

Augmentation of haptic feedback for teleoperated robotic surgery

Correction to: Automated measurement of bone scan index from a whole-body bone scintigram

Transrectal ultrasound image-based real-time augmented reality guidance in robot-assisted laparoscopic rectal surgery: a proof-of-concept study