Bone fracture reduction: A combined Deep-Learning/Statistical Shape Model approach for the estimation of the optimal reduction and fixation strategy during planning

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Bone fracture reduction: A combined Deep-Learning/Statistical Shape Model approach for the estimation of the optimal reduction and fixation strategy during planning

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Offer DescriptionBone trauma is a global scourge and ranks as the third leading cause of overall morbidity burden according to the World Health Organization (WHO). Contrary to the scheduled orthopedic surgery that benefits from recent efficient edge-cutting digital technologies, trauma constraints are higher with a shorter period of management, a high complexity and multiplicity of treated cases and a greater variability of the surgeon experience. To this end, a funded research project “ReBone” (ANR RHU, 24M€) gathering industrial, academic, and medical partners have been recently launched. The aim is to optimize the surgery and minimize complications in complex bone trauma by developing and validating personalized, automated, and collaborative pre-operative planning solutions, and by providing intraoperative solutions allowing the accurate execution of the planning. “ReBone” will provide thus a fundamental, deep, and continuous improvement in clinical and surgical management of severe trauma and complex fractures, to (1) reduce treatment delays, (2) have access to more precise and reproducible guided surgeries, (3) decrease post-operative complications. The Laboratory of Medical Information Processing ( ) will be in charge of the fracture reduction modelling and the simulation of the hardware fixation (plate and screws). Still today, most of the available commercial solutions to plan bone fracture reduction rely only on manual approaches which is hardly acceptable for the surgeon especially when the number of fractures to be treated becomes too large [1]. Our aim is therefore to provide a complete easy-to-use planning software solution allowing the automatic and fast simulation of both the fracture reduction and the hardware fixation based on geometrical criteria.Most of the existing methods for the simulation of the fracture reduction are based on the incorporation of the mirrored healthy contralateral bone [2-5]. But these methods cannot be automatically used in all patients, especially in cases of natural shape differences or bilateral trauma [6]. Usual registration algorithms have been also proposed to estimate the reduction process from the alignment of the fracture lines, but their performances were very dependent on the quality of the initialization [7-9]. Statistical Shape Models (SSMs) have been more recently introduced [6, 10]. Although they do not require contralateral bones and can better drive the estimation of the fracture alignment, the accuracy of the estimation is very dependent on the class and type of the fractures. We want therefore to propose a generic approach allowing the accurate, robust, and fast simulation of both the optimal fracture reduction and hardware fixation whatever the type and class of the fracture. Combined approaches exploiting both Deep-Learning and shape priors will be investigated to overcome current limitations [11]. Validation will be carried out by comparing the proposed results with manual reductions and fixation strategies proposed by surgeons on real data. An integration of the proposed approach will be finally integrated, in collaboration with engineers, inside our planning software to be used by surgeons.

[1] Moolenaar, J., Tümer, N., & Checa, S. (2022). Computer-assisted preoperative planning of bone fracture fixation surgery: A state-of-the-art review.[2] Casari, F. A., Roner, S., Fürnstahl, P., Nagy, L., & Schweizer, A. (2021). Computer-assisted open reduction internal fixation of intraarticular radius fractures navigated with patient-specific instrumentation, a prospective case series. Archives of Orthopaedic and Trauma Surgery, 141, 1425-1432.[3] Zhao, C., Guan, M., Shi, C., Zhu, G., Gao, X., Zhao, X., … & Wu, X. (2022). Automatic reduction planning of pelvic fracture based on symmetry. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 10(6), 577-584.[4] Okada, T., Iwasaki, Y., Koyama, T., Sugano, N., Chen, Y. W., Yonenobu, K., & Sato, Y. (2008). Computer-assisted preoperative planning for reduction of proximal femoral fracture using 3-D-CT data. IEEE Transactions on Biomedical Engineering, 56(3), 749-759.[5] Fürnstahl, P., Székely, G., Gerber, C., Hodler, J., Snedeker, J. G., & Harders, M. (2012). Computer assisted reconstruction of complex proximal humerus fractures for preoperative planning. Medical image analysis, 16(3), 704-720.[6] Han, R., Uneri, A., Vijayan, R. C., Wu, P., Vagdargi, P., Sheth, N., … & Siewerdsen, J. H. (2021). Fracture reduction planning and guidance in orthopaedic trauma surgery via multi-body image registration. Medical image analysis, 68, 101917.[7] Thomas, T. P. (2010). Virtual pre-operative reconstruction planning for comminuted articular fractures (Doctoral dissertation, University of Iowa).[8] Liu, B., Zhang, S., Zhang, J., Xu, Z., Chen, Y., Liu, S., … & Yang, L. (2019). A personalized preoperative modeling system for internal fixation plates in long bone fracture surgery-A straightforward way from CT images to plate model. The International Journal of Medical Robotics and Computer Assisted Surgery, 15(5), e2029.[9] Paulano-Godino, F. & Jiménez-Delgado, J.J. (2017). Identification of fracture zones and its application in automatic bone fracture reduction. Computer Methods and Programs in Biomedicine, 141, 93-104.[10] Fouefack J.-R., Borotikar B., Lüthi M., Douglas T. S., Burdin V., Mutsvangwa T. E.M. (2023). Dynamic multi feature-class Gaussian process models. Medical Image Analysis, 85 ( ).[11] Boutillon A., Borotikar B., Burdin V., Conze P.H. (2022). Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network. Artificial Intelligence in Medicine, 132 ( ).Where to apply WebsiteRequirementsSpecific RequirementsPhD in computer vision, AI, applied mathematics.Good programming skills is an important requisite, especially in python and C++. Autonomy, open-mindedness, and motivationGood English skills are also expected.Additional InformationWork Location(s)Number of offers available 1 Company/Institute Université de Bretagne Occidentale Country France City Brest GeofieldContact WebsiteSTATUS: EXPIREDShare this page

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Location

Brest, Finistère

Job date

Wed, 11 Sep 2024 01:41:32 GMT

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