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 labelmaps, 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.
|Number of pages||4|
|Publication status||Published - 2019|
|Event||Medical Imaging with Deep Learning 2019 - Imperial College’s South Kensington Campus, London, United Kingdom|
Duration: 8 Jul 2019 → 10 Jul 2019
|Conference||Medical Imaging with Deep Learning 2019|
|Location||Imperial College’s South Kensington Campus|
|Period||08/07/2019 → 10/07/2019|
- Signed distance fields
- Pixel-wise regression
- Left atrial appendage
Juhl, K. A., Paulsen, R. R., Dahl, A. B., Dahl, V. A., De Backer, O., Kofoed, K. F., & Camara, O. (2019). Guiding 3D U-nets with signed distance fields for creating 3D models from images. Paper presented at Medical Imaging with Deep Learning 2019, London, United Kingdom.