Abstract
We present a method for the automatic detection of the uterus and the Fallopian tube/Uterus junctions (FU-junctions) in a monocular laparoscopic image. The main application is to perform automatic registration and fusion between preoperative radiological images of the uterus and laparoscopic images for image-guided surgery. In the broader context of computer assisted intervention, our method is the first that detects an organ and registration landmarks from laparoscopic images without manual input. Our detection problem is challenging because of the large inter-patient anatomical variability and pathologies such as uterine fibroids. We solve the problem using learned contextual geometric constraints that statistically model the positions and orientations of the FU-junctions relative to the uterus’ body. We train the uterus detector using a modern part-based approach and the FU-junction detector using junction-specific context-sensitive features. We have trained and tested on a database of 95 uterus images with cross validation, and successfully detected the uterus with Recall = 0.95 and average Number of False Positives per Image (NFPI) = 0.21, and FU-junctions with Recall = 0.80 and NFPI = 0.50. Our experimental results show that the contextual constraints are fundamental to achieve high quality detection.
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References
Allan, M., Ourselin, S., Thompson, S., Hawkes, D.J., Kelly, J., Stoyanov, D.: Toward detection and localization of instruments in minimally invasive surgery. IEEE Trans. Biomed. Eng. 60(4), 1050–1058 (2013)
Awrangjeb, M., Lu, G.: Robust image corner detection based on the chord-to-point distance accumulation technique. IEEE Trans. Multimedia 10(6), 1059–1072 (2008)
Bouchard, G., Triggs, B.: Hierarchical part-based visual object categorization. In: CVPR (2005)
Collins, T., Pizarro, D., Bartoli, A., Canis, M., Bourdel, N.: Realtime wide-baseline registration of the uterus in laparoscopic videos using multiple texture maps. In: Liao, H., Linte, C.A., Masamune, K., Peters, T.M., Zheng, G. (eds.) MIAR 2013 and AE-CAI 2013. LNCS, vol. 8090, pp. 162–171. Springer, Heidelberg (2013)
Collins, T., Pizarro, D., Bartoli, A., Canis, M., Bourdel, N.: Computer-assisted laparoscopic myomectomy by augmenting the uterus with pre-operative MRI data. In: ISMAR (2014)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
Deng, Y., Wang, Y., Chen, P.: Automated detection of fetal nuchal translucency based on hierarchical structural model. In: ICBMS (2010)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2010 (VOC2010) Results
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Girshick, R.B.: From rigid templates to grammars: object detection with structured models. Ph.D. thesis, University of Chicago (2012)
Hamarneh, G., Amir-Khalili, A., Nosrati, M., Figueroa, I., Kawahara, J., Al-Alao, O., Peyrat, J.-M., Abi-Nahed, J., Al-Ansari, A., Abugharbieh, R.: Towards multi-modal image-guided tumour identification in robot-assisted partial nephrectomy. In: MECBME (2014)
Haouchine, N., Dequidt, J., Peterlik, I., Kerrien, E., Berger, M.-O., Cotin, S.: Image-guided simulation of heterogeneous tissue deformation for augmented reality during hepatic surgery. In: ISMAR (2013)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Fourth Alvey Vision Conference (1988)
Howe, N.R.: Kontour-Pruned Skeletonization (2006). http://cs.smith.edu/~nhowe/research/code/Skeleton.zip. Accessed 23 September 2014
Plantefève, R., Haouchine, N., Radoux, J.-P., Cotin, S.: Automatic alignment of pre and intraoperative data using anatomical landmarks for augmented laparoscopic liver surgery. In: Bello, F., Cotin, S. (eds.) ISBMS 2014. LNCS, vol. 8789, pp. 58–66. Springer, Heidelberg (2014)
Rangarajan, A., Chui, H., Bookstein, F.L.: The softassign procrustes matching algorithm. In: Information Processing in Medical Imaging (1997)
Simpfendorfer, T., Baumhauer, M., Muller, M., Gutt, C.N., Meinzer, H.-P., Rassweiler, J.J., Guven, S., Teber, D.: Augmented reality visualization during laparoscopic radical prostatectomy. J. Endourol. 25, 1841–1845 (2011)
Su, L.-M., Vagvolgyi, B.P., Agarwal, R., Reiley, C.E., Taylor, R.H., Hager, G.D.: Augmented reality during robot-assisted laparoscopic partial nephrectomy: toward real-time 3D-CT to stereoscopic video registration. Urology 73(4), 896–900 (2009)
Xia, G.-S., Delon, J., Gousseau, Y.: Accurate junction detection and characterization in natural images. Int. J. Comput. Vision 106(1), 31–56 (2014)
Zhang, F., Song, Y., Cai, W., Lee, M.-Z., Zhou, Y., Huang, H., Shan, S., Fulham, M., Feng, D.: Lung nodule classification with multilevel patch-based context analysis. IEEE Trans. Biomed. Eng. 61(4), 1155–1166 (2014)
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Prokopetc, K., Collins, T., Bartoli, A. (2015). Automatic Detection of the Uterus and Fallopian Tube Junctions in Laparoscopic Images. In: Ourselin, S., Alexander, D., Westin, CF., Cardoso, M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science(), vol 9123. Springer, Cham. https://doi.org/10.1007/978-3-319-19992-4_43
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DOI: https://doi.org/10.1007/978-3-319-19992-4_43
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