List of results published directly linked with the projects co-funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Program (MDM-2015-0502).

List of publications acknowledging the funding in Scopus.

The record for each publication will include access to postprints (following the Open Access policy of the program), as well as datasets and software used. Ongoing work with UPF Library and Informatics will improve the interface and automation of the retrieval of this information soon.

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   

 

 

Back 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.

 

https://openreview.net/forum?id=rJgzz3Y4qV