7 papers
PrIntMesh: Precise Intersection Surfaces for 3D Organ Mesh Reconstruction
Deniz Sayin Mercadier, Hieu Le, Yihong Chen +3
Human organs are composed of interconnected substructures whose geometry and spatial relationships constrain one another. Yet, most deep-learning approaches treat these parts indep…
Neural Annotation Refinement: Development of a New 3D Dataset for Adrenal Gland Analysis
Jiancheng Yang, Rui Shi, Udaranga Wickramasinghe +3
The human annotations are imperfect, especially when produced by junior practitioners. Multi-expert consensus is usually regarded as golden standard, while this annotation protocol…
Deep Active Latent Surfaces for Medical Geometries
Patrick M. Jensen, Udaranga Wickramasinghe, Anders B. Dahl +2
Shape priors have long been known to be effective when reconstructing 3D shapes from noisy or incomplete data. When using a deep-learning based shape representation, this often inv…
Weakly Supervised Volumetric Image Segmentation with Deformed Templates
Udaranga Wickramasinghe, Patrick M. Jensen, Mian Shah +2
There are many approaches to weakly-supervised training of networks to segment 2D images. By contrast, existing approaches to segmenting volumetric images rely on full-supervision…
Deep Active Surface Models
Udaranga Wickramasinghe, Graham Knott, Pascal Fua
Active Surface Models have a long history of being useful to model complex 3D surfaces but only Active Contours have been used in conjunction with deep networks, and then only to p…
Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data
Udaranga Wickramasinghe, Edoardo Remelli, Graham Knott +1
CNN-based volumetric methods that label individual voxels now dominate the field of biomedical segmentation. However, 3D surface representations are often required for proper analy…