Publications by Pamuditha Udaranga Wickramasinghe

A method and system for enforcing smoothness constraints on surface meshes from a graph convolutional neural network

Pascal Fua, Pamuditha Udaranga Wickramasinghe

A method for enforcing smoothness constraints on surface meshes produced by a Graph Convolutional Neural Network (GCNN) including the steps of reading image data from a memory, the image data including two-dimensional image data representing a three-dimens ...

2023

Object Priors for Volumetric Image Segmentation

Pamuditha Udaranga Wickramasinghe

Large training datasets have played a vital role in the success of modern deep learning methods in computer vision. But, obtaining sufficient amount of training data is challenging, specially when annotating volumetric images. This is because fully annotat ...

EPFL2022

Weakly Supervised Volumetric Image Segmentation with Deformed Templates

Pascal Fua, Pamuditha Udaranga Wickramasinghe, Patrick Moller Jensen, Mian Akbar Shah

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 of a subset of 2D slices of the 3D volume. We propose an approach to volume ...

Springer2022

Neural Annotation Refinement: Development of a New 3D Dataset for Adrenal Gland Analysis

Pascal Fua, Pamuditha Udaranga Wickramasinghe, Jiancheng Yang, Rui Shi

The human annotations are imperfect, especially when produced by junior practitioners. Multi-expert consensus is usually regarded as golden standard, while this annotation protocol is too expensive to implement in many real-world projects. In this study, w ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging

Pascal Fua, Pamuditha Udaranga Wickramasinghe, Jiancheng Yang

2022

Deep Active Surface Models

Pascal Fua, Graham Knott, Pamuditha Udaranga Wickramasinghe

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 produce the data term as well as meta-parameter maps controlling them. In th ...

2021

Introducing reinforcement learning to the energy system design process

Jean-Louis Scartezzini, Pamuditha Udaranga Wickramasinghe, Amarasinghage Tharindu Dasun Perera, Vahid Moussavi Nik

Design optimization of distributed energy systems has become an interest of a wider group of researchers due the capability of these systems to integrate non-dispatchable renewable energy technologies such as solar PV and wind. White box models, using line ...

2020

Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data

Pascal Fua, Graham Knott, Pamuditha Udaranga Wickramasinghe, Edoardo Remelli

CNN-based volumetric methods that label individual voxels now dominate the field of biomedical segmentation. However, 3D surface representations are often required for proper analysis. They can be obtained by post-processing the labeled volumes which typic ...

2020

Probabilistic Atlases to Enforce Topological Constraints

Pascal Fua, Graham Knott, Pamuditha Udaranga Wickramasinghe

Probabilistic atlases (PAs) have long been used in standard segmentation approaches and, more recently, in conjunction with Convolutional Neural Networks (CNNs). However, their use has been restricted to relatively standardized structures such as the brain ...

2019

Optimum design of distributed energy hubs using hybrid surrogate models (HSM)

Jean-Louis Scartezzini, Pamuditha Udaranga Wickramasinghe, Amarasinghage Tharindu Dasun Perera, Vahid Moussavi Nik

The energy hub concept is gradually getting popular due to its capability to integrate renewable energy technologies into the energy grid with a minimum impact. Designing distributed energy hubs is a challenging task due to the coupling of optimum dispatch ...

Elsevier2017