Spatiotemporal Free-Form Registration Method Assisted by a Minimum Spanning Tree During Discontinuous Transformations.

The sliding motion along the boundaries of discontinuous regions has been actively studied in B-spline free-form deformation framework. This study focusses on the sliding motion for a velocity field-based 3D+t registration. The discontinuity of the tangent direction guides the deformation of the object region, and a separate control of two regions provides a better registration accuracy.

Lumbar Vertebrae Synthetic Segmentation in Computed Tomography Images Using Hybrid Deep Generative Adversarial Networks.

The lumbar vertebrae segmentation in Computed tomography (CT) is challenging due to the scarcity of the labeled training data that we define as paired training data for the deep learning technique. Much of the available data is limited to the raw CT scans, unlabeled by radiologists. To handle the scarcity of labeled data, we utilized a hybrid training system by combining paired and unpaired training data and construct a hybrid deep segmentation generative adversarial network (Hybrid-SegGAN).

Semi-Supervised Learning for Low-Dose CT Image Restoration with Hierarchical Deep Generative Adversarial Network (HD-GAN).

In the absence of duplicate high-dose CT data, it is challenging to restore high-quality images based on deep learning with only low-dose CT (LDCT) data. When different reconstruction algorithms and settings are adopted to prepare high-quality images, LDCT datasets for deep learning can be unpaired. To address this problem, we propose hierarchical deep generative adversarial networks (HD-GANs) for semi-supervised learning with the unpaired datasets.