Interactive segmentation in MRI for orthopedic surgery planning: bone tissue
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Planning orthopedic surgeries is commonly performed in computed tomography (CT) images due to the higher contrast of bony structure. However, soft tissues such as muscles and ligaments that may determine the functional outcome of a procedure are not easy to identify in CT, for which fast and accurate segmentation in MRI would be desirable. To be usable in daily practice, such method should provide convenient means of interaction for modifications and corrections, e.g., during perusal by the surgeon or the planning physician for quality control.
We propose an interactive segmentation framework for MR images and evaluate the outcome for segmentation of bones. We use a random forest classification and a random walker-based spatial regularization. The latter enables the incorporation of user input as well as enforcing a single connected anatomical structures, thanks to which a selective sampling strategy is proposed to substantially improve the supervised learning performance.
We evaluated our segmentation framework on 10 patient humerus MRI as well as 4 high-resolution MRI from volunteers. Interactive humerus segmentations for patients took on average 150 s with over 3.5 times time-gain compared to manual segmentations, with accuracies comparable (converging) to that of much longer interactions. For high-resolution data, a novel multi-resolution random walker strategy further reduced the run time over 20 times of the manual segmentation, allowing for a feasible interactive segmentation framework.
We present a segmentation framework that allows iterative corrections leading to substantial speed gains in bone annotation in MRI. This will allow us to pursue semi-automatic segmentations of other musculoskeletal anatomy first in a user-in-the-loop manner, where later less user interactions or perhaps only few for quality control will be necessary as our annotation suggestions improve.
KeywordsBone segmentation Iterative refinement MR in CAOS
This work was funded by a highly specialized medicine Grant (HSM2) of the Canton of Zurich.
Compliance with ethical standards
Conflict of interest
All authors declare no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the provincial ethics committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- 1.Fürnstahl P, Schweizer A, Graf M, Vlachopoulos L, Fucentese S, Wirth S, Nagy L, Szekely G, Goksel O (2016) Surgical treatment of long-bone deformities: 3D preoperative planning and patient-specific instrumentation. In: Zheng G, Li S (eds) Computational radiology for orthopaedic interventions. Springer, pp 123–149Google Scholar
- 2.Despotović I, Goossens B, Philips W (2015) MRI segmentation of the human brain: challenges, methods, and applications. CMMM 2015:23–45. doi: 10.1155/2015/450341
- 6.Koch M, Schwing AG, Comaniciu D, Pollefeys M (2011) Fully automatic segmentation of wrist bones for arthritis patients. In: 2011 IEEE international symposium on biomedical imaging: from nano to macro. IEEE, pp 636–640Google Scholar
- 8.Criminisi A, Sharp T, Blake A, (2008) Geos: geodesic image segmentation. In: European conference on computer vision (ECCV) (Marseille, France), pp 99–112Google Scholar
- 9.Zhu L, Kolesov I, Gao Y, Kikinis R, Tannenbaum A (2014) An effective interactive medical image segmentation method using fast growcut. In: MICCAI W IMICGoogle Scholar
- 14.Thoma J, Ozdemir F, Goksel O (2016) Automatic segmentation of abdominal MRI using selective sampling and random walker. In: MICCAI W MCV, pp 1–11Google Scholar
- 15.Zheng Y, Barbu A, Georgescu B, Scheuering M, Comaniciu D (2008) Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE TMI 27(11):1668–1681Google Scholar
- 17.Mahapatra D, Schuffler PJ, Tielbeek JAW, Makanyanga JC, Stoker J, Taylor SA, Vos FM, Buhmann JM (2013) Automatic detection and segmentation of crohn’s disease tissues from abdominal MRI. IEEE TMI 32:2332–2347Google Scholar
- 19.Dakua SP, Sahambi JS (2009) LV contour extraction from cardiac MR images using random walks approach. In: Advance computing conference (IACC), p 228Google Scholar
- 20.Maier F, Wimmer A, Soza G, Kaftan JN, Fritz D, Dillmann R (2008) Automatic liver segmentation using the random walker algorithm. In: Tolxdorff T, Braun J, Deserno TM, Horsch A, Handels H, Meinzer HP (eds) Bildverarbeitung für die Medizin 2008. Springer, pp 56–61Google Scholar
- 22.Grady L, Sinop AK (2008) Fast approximate random walker segmentation using eigenvector precomputation. In: CVPR 2008. IEEE, pp 1–8Google Scholar
- 23.Andrews S, Hamarneh G, Saad A (2010) Fast random walker with priors using precomputation for interactive medical image segmentation. In: MICCAI, pp 9–16Google Scholar
- 24.Gueziri H-E, McGuffin MJ, Laporte C (2016) A generalized graph reduction framework for interactive segmentation of large images. CVIU 150:44–57Google Scholar