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Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks
Sonography synthesis has a wide range of applications, including medical...
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Prostate motion modelling using biomechanically-trained deep neural networks on unstructured nodes
In this paper, we propose to train deep neural networks with biomechanic...
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Adversarial Deformation Regularization for Training Image Registration Neural Networks
We describe an adversarial learning approach to constrain convolutional ...
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Assisted Probe Positioning for Ultrasound Guided Radiotherapy Using Image Sequence Classification
Effective transperineal ultrasound image guidance in prostate external b...
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Image Quality Assessment for Rigid Motion Compensation
Diagnostic stroke imaging with C-arm cone-beam computed tomography (CBCT...
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Geometric Uncertainty in Patient-Specific Cardiovascular Modeling with Convolutional Dropout Networks
We propose a novel approach to generate samples from the conditional dis...
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Registration by tracking for sequential 2D MRI
Our anatomy is in constant motion. With modern MR imaging it is possible...
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Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks
In this paper, we describe how a patient-specific, ultrasound-probe-induced prostate motion model can be directly generated from a single preoperative MR image. Our motion model allows for sampling from the conditional distribution of dense displacement fields, is encoded by a generative neural network conditioned on a medical image, and accepts random noise as additional input. The generative network is trained by a minimax optimisation with a second discriminative neural network, tasked to distinguish generated samples from training motion data. In this work, we propose that 1) jointly optimising a third conditioning neural network that pre-processes the input image, can effectively extract patient-specific features for conditioning; and 2) combining multiple generative models trained separately with heuristically pre-disjointed training data sets can adequately mitigate the problem of mode collapse. Trained with diagnostic T2-weighted MR images from 143 real patients and 73,216 3D dense displacement fields from finite element simulations of intraoperative prostate motion due to transrectal ultrasound probe pressure, the proposed models produced physically-plausible patient-specific motion of prostate glands. The ability to capture biomechanically simulated motion was evaluated using two errors representing generalisability and specificity of the model. The median values, calculated from a 10-fold cross-validation, were 2.8+/-0.3 mm and 1.7+/-0.1 mm, respectively. We conclude that the introduced approach demonstrates the feasibility of applying state-of-the-art machine learning algorithms to generate organ motion models from patient images, and shows significant promise for future research.
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