nach oben

International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

09.04.2019 | Original Article

Face detection in the operating room: comparison of state-of-the-art methods and a self-supervised approach

verfasst von: Thibaut Issenhuth, Vinkle Srivastav, Afshin Gangi, Nicolas Padoy

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 6/2019

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Purpose

Face detection is a needed component for the automatic analysis and assistance of human activities during surgical procedures. Efficient face detection algorithms can indeed help to detect and identify the persons present in the room and also be used to automatically anonymize the data. However, current algorithms trained on natural images do not generalize well to the operating room (OR) images. In this work, we provide a comparison of state-of-the-art face detectors on OR data and also present an approach to train a face detector for the OR by exploiting non-annotated OR images.

Methods

We propose a comparison of six state-of-the-art face detectors on clinical data using multi-view OR faces, a dataset of OR images capturing real surgical activities. We then propose to use self-supervision, a domain adaptation method, for the task of face detection in the OR. The approach makes use of non-annotated images to fine-tune a state-of-the-art detector for the OR without using any human supervision.

Results

The results show that the best model, namely the tiny face detector, yields an average precision of 0.556 at intersection over union of 0.5. Our self-supervised model using non-annotated clinical data outperforms this result by 9.2%.

Conclusion

We present the first comparison of state-of-the-art face detectors on OR images and show that results can be significantly improved by using self-supervision on non-annotated data.

Vorheriger Artikel Flexible needle and patient tracking using fractional scanning in interventional CT procedures

Nächster Artikel Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos

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

Chen K, Gabriel P, Alasfour A, Gong C, Doyle WK, Devinsky O, Friedman D, Dugan P, Melloni L, Thesen T, Gonda D, Sattar S, Wang S, Gilja V (2018) Patient-specific pose estimation in clinical environments. IEEE J Transl Eng Health Med 6:1–11

Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: CVPR, pp I–I

Najibi M, Samangouei P, Chellappa R, Davis LS (2017) SSH: single stage headless face detector. In: ICCV, pp 4885–4894

Zhang S, Zhu X, Lei Z, Shi H, Wang X, Li SZ (2017) S\(^3\)FD: single shot scale-invariant face detector. In: International conference on computer vision (ICCV) at Venice, Italy

Jiang H, Learned-Miller E (2017) Face detection with the faster R-CNN. In: 12th IEEE international conference on automatic face & gesture recognition (FG 2017)

Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, pp 91–99

Yang S, Luo P, Loy CC, Tang X (2016) Wider face: a face detection benchmark. In: CVPR

Cao Z, Simon T, Wei S-E, Sheikh Y (2017) Realtime multi-person 2D pose estimation using part affinity fields. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 1302–1310

Insafutdinov E, Pishchulin L, Andres B, Andriluka M, Schiele B (2016) Deepercut: a deeper, stronger, and faster multi-person pose estimation model. In: ECCV, pp 34–50

10.

Fang H-S, Xie S, Tai Y-W, Lu C (2017) RMPE: regional multi-person pose estimation. In: ICCV

11.

Xiao B, Wu H, Wei Y (2018) Simple baselines for human pose estimation and tracking. In: ECCV

12.

Chen Y, Wang Z, Peng Y, Zhang Z, Yu G, Sun J (2018) Cascaded pyramid network for multi-person pose estimation. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 7103–7112

13.

Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft COCO: common objects in context. In: ECCV, pp 740–755

14.

Andriluka M, Pishchulin L, Gehler P, Schiele B (2014) 2D human pose estimation: new benchmark and state of the art analysis. In: CVPR, June 2014

15.

Twinanda AP, Shehata S, Mutter D, Marescaux J, de Mathelin M, Padoy N (2016) Multi-stream deep architecture for surgical phase recognition on multi-view RGBD videos. In: MICCAI workshop on modeling and monitoring of computer assisted interventions (M2CAI)

16.

Maier-Hein L, Vedula SS, Speidel S, Navab N, Kikinis R, Park A, Eisenmann M, Feussner H, Forestier G, Giannarou S, Hashizume M, Katic D, Kenngott H, Kranzfelder M, Malpani A, März K, Neumuth T, Padoy N, Pugh C, Schoch N, Stoyanov D, Taylor R, Wagner M, Hager GD, Jannin P (2017) Surgical data science for next-generation interventions. Nat Biomed Eng 1(9):691CrossRefPubMed

17.

Yeung S, Downing NL, Fei-Fei L, Milstein A (2018) Bedside computer vision-moving artificial intelligence from driver assistance to patient safety. NEJM 378(14):1271CrossRefPubMed

18.

Kadkhodamohammadi A, Gangi A, de Mathelin M, Padoy N (2017) Articulated clinician detection using 3D pictorial structures on RGB-D data. Med Image Anal 35:215–224CrossRefPubMed

19.

Kadkhodamohammadi A, Gangi A, de Mathelin M, Padoy N (2017) A multi-view RGB-D approach for human pose estimation in operating rooms. In: WACV, pp 363–372

20.

Belagiannis V, Wang X, Shitrit HB, Hashimoto K, Stauder R, Aoki Y, Kranzfelder M, Schneider A, Fua P, Ilic S, Feussner H, Navab N (2016) Parsing human skeletons in an operating room. Mach Vis Appl 27(7):1035–1046CrossRef

21.

Nieto-Rodríguez A, Mucientes M, Brea VM (2015) System for medical mask detection in the operating room through facial attributes. In: Iberian conference on pattern recognition and image analysis. Springer, pp 138–145

22.

Flouty E, Zisimopoulos O, Stoyanov D (2018) Faceoff: anonymizing videos in the operating rooms. In: OR 2.0 context-aware operating theaters, computer assisted robotic endoscopy, clinical image-based procedures, and skin image analysis. Springer, pp 30–38

23.

Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 28(2):337–407CrossRef

24.

Srivastav V, Issenhuth T, Kadkhodamohammadi A, de Mathelin M, Gangi A, Padoy N (2018) MVOR: a multi-view rgb-d operating room dataset for 2D and 3D human pose estimation. In: MICCAI-LABELS-2018

25.

Laine S, Aila T (2016) Temporal ensembling for semi-supervised learning. In: ICLR. arXiv preprint arXiv:1610.02242

26.

Radosavovic I, Dollár P, Girshick RB, Gkioxari G, He K (2018) Data distillation: towards omni-supervised learning. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 4119–4128

27.

Hu P, Ramanan D (2017) Finding tiny faces. In: CVPR

28.

Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: single shot multibox detector. In: ECCV. Springer, pp 21–37

Titel: Face detection in the operating room: comparison of state-of-the-art methods and a self-supervised approach
verfasst von: Thibaut Issenhuth
Vinkle Srivastav
Afshin Gangi
Nicolas Padoy
Publikationsdatum: 09.04.2019
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 6/2019
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-019-01944-y

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 6/2019

Prediction of laparoscopic procedure duration using unlabeled, multimodal sensor data

Interactive Flying Frustums (IFFs): spatially aware surgical data visualization

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

Estimation of tissue oxygen saturation from RGB images and sparse hyperspectral signals based on conditional generative adversarial network

IJCARS—IPCAI 2019 special issue: conference information processing for computer-assisted interventions, 10th international conference 2019—part 1