Interactive segmentation in MRI for orthopedic surgery planning: bone tissue

  • Firat Ozdemir
  • Neerav Karani
  • Philipp Fürnstahl
  • Orcun Goksel
Original Article



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.


Bone 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.

Ethical standards

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

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© CARS 2017

Authors and Affiliations

  1. 1.Computer-Assisted Applications in Medicine (CAiM)ETH ZurichZurichSwitzerland
  2. 2.Computer Assisted Research and Development (CARD) GroupUniversity Hospital Balgrist, University of ZurichZurichSwitzerland

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