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EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
TLDR
This work proposes EL-GAN: a GAN framework to mitigate the discussed problem using an embedding loss, and uses the TuSimple lane marking challenge to demonstrate that with this proposed framework it is viable to overcome the inherent anomalies of posing it as a semantic segmentation problem.
Oriented Gaussian Mixture Models for Nonrigid 2D/3D Coronary Artery Registration
TLDR
A novel feature based 2D/3D registration framework, that is based on probabilistic point correspondences, is presented, and its usefulness on aligning 3D coronary artery centerlines derived from CTA images with their 2D projection derived from interventional X-ray angiography is shown.
Statistical Shape Model-Based Femur Kinematics From Biplane Fluoroscopy
TLDR
A dedicated dynamic reconstruction and tracking algorithm was developed estimating the shape based on all frames, and pose per frame, and employs a dynamic prior, image features, and prior knowledge about bone edge appearances to increase robustness.
Confidence of model based shape reconstruction from sparse data
TLDR
It is found that the proposed constrained shape model outperforms the other models, is robust against the selection and amount of sparse information, and indicates the shape confidence well.
Ultrasound Aided Vertebral Level Localization for Lumbar Surgery
TLDR
A deep convolutional neural network-based bone segmentation algorithm from US imaging that outperforms state of the art methods in both performance and speed is proposed.
Three dimensional measurement of minimum joint space width in the knee from stereo radiographs using statistical shape models
TLDR
The proposed measurement method is not a substitute for the conventional 2D measurement due to limitations in the SSM model accuracy, but combined with the promising options for applications using quantitative information on bone morphology, SSM based 3D reconstructions of natural knees are attractive for further development.
Comparison of Shape Regression Methods under Landmark Position Uncertainty
TLDR
This work compares linear regression methods with a special focus on shapes with landmark position uncertainties by comparing different regression methods for shape estimation by investigating two scenarios: in the first, the uncertainty of the landmark positions was similar in the training and test dataset, whereas in the second the uncertainty in theTraining and test data were different.
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