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X-ray image based surgical tool navigation is fast and supplies accurateimages of deep seated structures. Typically, recovering the 6 DOF rigid poseand deformation of tools with respect to the X-ray camera can be accuratelyachieved through intensity-based 2D/3D registration of 3D images or models to2D X-rays. However, the capture range of image-based 2D/3D registration isinconveniently small suggesting that automatic and robust initializationstrategies are of critical importance. This manuscript describes a first steptowards leveraging semantic information of the imaged object to initialize2D/3D registration within the capture range of image-based registration byperforming concurrent segmentation and localization of dexterous surgical toolsin X-ray images.  We presented a learning-based strategy to simultaneously localize and segmentdexterous surgical tools in X-ray images and demonstrate promising performanceon synthetic and ex vivo data. We currently investigate methods to use semanticinformation extracted by the proposed network to reliably and robustlyinitialize image-based 2D/3D registration.  While image-based 2D/3D registration has been an obvious focus of the CAIcommunity, robust initialization thereof (albeit critical) has largely beenneglected. This manuscript discusses learning-based retrieval of semanticinformation on imaged-objects as a stepping stone for such initialization andmay therefore be of interest to the IPCAI community. Since results are stillpreliminary and only focus on localization, we target the Long Abstractcategory.

Localizing dexterous surgical tools in X-ray for image-based navigation