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 develop a large number of software tools and hosting infrastructures to support the research developed at the Department. We will be detailing in this section the different tools available. You can take a look for the moment at the offer available within the UPF Knowledge Portal, the innovations created in the context of EU projects in the Innovation Radar and the software sections of some of our research groups:
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Artificial Intelligence |
Nonlinear Time Series Analysis |
Web Research |
Music Technology |
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Interactive Technologies |
Barcelona MedTech |
Natural Language Processing |
Nonlinear Time Series Analysis |
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UbicaLab |
Wireless Networking |
Educational Technologies |
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.