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X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery
X-ray image guidance enables percutaneous alternatives to complex proced...
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Automatic Registration between Cone-Beam CT and Scanned Surface via Deep-Pose Regression Neural Networks and Clustered Similarities
Computerized registration between maxillofacial cone-beam computed tomog...
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Initialize globally before acting locally: Enabling Landmark-free 3D US to MRI Registration
Registration of partial-view 3D US volumes with MRI data is influenced b...
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Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration
Fluoroscopy is the standard imaging modality used to guide hip surgery a...
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Attaining human-level performance for anatomical landmark detection in 3D CT data
We present an efficient neural network approach for locating anatomical ...
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Robust Landmark Detection for Alignment of Mouse Brain Section Images
Brightfield and fluorescent imaging of whole brain sections are funda- m...
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Deep Learning based Cephalometric Landmark Identification using Landmark-dependent Multi-scale Patches
A deep neural network based cephalometric landmark identification model ...
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Pose-dependent weights and Domain Randomization for fully automatic X-ray to CT Registration
Fully automatic X-ray to CT registration requires a solid initialization to provide an initial alignment within the capture range of existing intensity-based registrations. This work adresses that need by providing a novel automatic initialization, which enables end to end registration. First, a neural network is trained once to detect a set of anatomical landmarks on simulated X-rays. A domain randomization scheme is proposed to enable the network to overcome the challenge of being trained purely on simulated data and run inference on real Xrays. Then, for each patient CT, a patient-specific landmark extraction scheme is used. It is based on backprojecting and clustering the previously trained networks predictions on a set of simulated X-rays. Next, the network is retrained to detect the new landmarks. Finally the combination of network and 3D landmark locations is used to compute the initialization using a perspective-n-point algorithm. During the computation of the pose, a weighting scheme is introduced to incorporate the confidence of the network in detecting the landmarks. The algorithm is evaluated on the pelvis using both real and simulated x-rays. The mean (+-standard deviation) target registration error in millimetres is 4.1 +- 4.3 for simulated X-rays with a success rate of 92 86.8
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