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- W4296193522 abstract "Careful surgical planning facilitates the precise and safe placement of implants and grafts in reconstructive orthopedics. Current attempts to (semi-)automatic planning separate the extraction of relevant anatomical structures on X-ray images and perform the actual positioning step using geometric post-processing. Such separation requires optimization of a proxy objective different from the actual planning target, limiting generalization to complex image impressions and the positioning accuracy that can be achieved. We address this problem by translating the geometric steps to a continuously differentiable function, enabling end-to-end gradient flow. Combining this companion objective function with the original proxy formulation improves target positioning directly while preserving the geometric relation of the underlying anatomical structures. We name this concept Deep Geometric Supervision. The developed method is evaluated for graft fixation site identification in medial patellofemoral ligament (MPFL) reconstruction surgery on (1) 221 diagnostic and (2) 89 intra-operative knee radiographs. Using the companion objective reduces the median Euclidean Distance error for MPFL insertion site localization from (1) $$2.29,textrm{mm}$$ to $$1.58,textrm{mm}$$ and (2) $$8.70,textrm{px}$$ to $$3.44,textrm{px}$$ , respectively. Furthermore, we empirically show that our method improves spatial generalization for strongly truncated anatomy." @default.
- W4296193522 created "2022-09-18" @default.
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- W4296193522 date "2022-01-01" @default.
- W4296193522 modified "2023-10-06" @default.
- W4296193522 title "Deep Geometric Supervision Improves Spatial Generalization in Orthopedic Surgery Planning" @default.
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- W4296193522 doi "https://doi.org/10.1007/978-3-031-16449-1_59" @default.
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