Evaluation Tool to Diagnose Faults and Discrepancy in Semi-Automated or Manual Annotations in Ultrasound Cine Loops (Videos).

Good quality (annotated) data is one of the most important aspects of supervised deep learning. Tasks such as semantic segmentation have a huge data requirement in exchange for only satisfactory performance. Large-scale annotations spread across multiple annotators tends to create inconsistencies, as there are various manual and semi-automated techniques involved.

Attention-augmented U-Net (AA-U-Net) for semantic segmentation.

Unlabelled: Deep learning-based image segmentation models rely strongly on capturing sufficient spatial context without requiring complex models that are hard to train with limited labeled data. For COVID-19 infection segmentation on CT images, training data are currently scarce. Attention models, in particular the most recent self-attention methods, have shown to help gather contextual information within deep networks and benefit semantic segmentation tasks.

Dynamic deformable attention network (DDANet) for COVID-19 lesions semantic segmentation.

Deep learning based medical image segmentation is an important step within diagnosis, which relies strongly on capturing sufficient spatial context without requiring too complex models that are hard to train with limited labelled data. Training data is in particular scarce for segmenting infection regions of CT images of COVID-19 patients. Attention models help gather contextual information within deep networks and benefit semantic segmentation tasks.

Statistical deformable bone models for robust 3D surface extrapolation from sparse data.

A majority of pre-operative planning and navigational guidance during computer assisted orthopaedic surgery routinely uses three-dimensional models of patient anatomy. These models enhance the surgeon's capability to decrease the invasiveness of surgical procedures and increase their accuracy and safety. A common approach for this is to use computed tomography (CT) or magnetic resonance imaging (MRI).

Kernel regularized bone surface reconstruction from partial data using statistical shape model.

This paper addresses the problem of surface reconstruction from partial data consisting of digitized landmarks and surface points that are obtained intraoperatively. The surface is derived by deforming a template so that the reconstructed surface matches the digitized points. Two techniques are employed to address such an ill-posed problem.

Evaluation of 3D correspondence methods for model building.

The correspondence problem is of high relevance in the construction and use of statistical models. Statistical models are used for a variety of medical application, e.g.