8 citations · 12 across the 5 of their papers we have counts for
3 papers · 1 filter
RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation
Marc Gantenbein, Ertunc Erdil, Ender Konukoglu
Quantifying segmentation uncertainty has become an important issue in medical image analysis due to the inherent ambiguity of anatomical structures and its pathologies. Recently, n…
Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE
Anna Volokitin, Ertunc Erdil, Neerav Karani +4
Probabilistic modelling has been an essential tool in medical image analysis, especially for analyzing brain Magnetic Resonance Images (MRI). Recent deep learning techniques for es…
Combining nonparametric spatial context priors with nonparametric shape priors for dendritic spine segmentation in 2-photon microscopy images
Ertunc Erdil, Ali Ozgur Argunsah, Tolga Tasdizen +2
Data driven segmentation is an important initial step of shape prior-based segmentation methods since it is assumed that the data term brings a curve to a plausible level so that s…