Juhl KA, Paulsen RR, Dahl AB, Dahl VA, De Backer O, Kofoed K, Camara O. Guiding 3D U-nets with signed distance fields for creating 3D models from images. Medical Imaging with Deep Learning (MIDL2019)
We have relevant datasets, repositories, frameworks and tools of relevance for research and technology transfer initiatives related to knowledge extraction. This section provides an overview on a selection of them and links to download or contact details.
The MdM Strategic Research Program has its own community in Zenodo for material available in this repository
as well as at the UPF e-repository 
. Below a non-exhaustive list of datasets representative of the research in the Department.
As part of the promotion of the availability of resources, the creation of specific communities in Zenodo has also been promoted, at level of research communities (for instance, MIR and Educational Data Analytics) or MSc programs (for instance, the Master in Sound and Music Computing)
Juhl KA, Paulsen RR, Dahl AB, Dahl VA, De Backer O, Kofoed K, Camara O. Guiding 3D U-nets with signed distance fields for creating 3D models from images. Medical Imaging with Deep Learning (MIDL2019)
Juhl KA, Paulsen RR, Dahl AB, Dahl VA, De Backer O, Kofoed K, Camara O. Guiding 3D U-nets with signed distance fields for creating 3D models from images. Medical Imaging with Deep Learning (MIDL2019)
Morphological analysis of the left atrial appendage is an important tool to assess risk of ischemic stroke. Most deep learning approaches for 3D segmentation is guided by binary label maps, which results in voxelized segmentations unsuitable for morphological analysis. We propose to use signed distance fields to guide a deep network towards morphologically consistent 3D models. The proposed strategy is evaluated on a synthetic dataset of simple geometries, as well as a set of cardiac computed tomography images containing the left atrial appendage. The proposed method produces smooth surfaces with a closer resemblance to the true surface in terms of segmentation overlap and surface distance.