7 citations · 11 across the 6 of their papers we have counts for
7 papers
ANISE: Assembly-based Neural Implicit Surface rEconstruction
Dmitry Petrov, Matheus Gadelha, Radomir Mech +1
We present ANISE, a method that reconstructs a 3D~shape from partial observations (images or sparse point clouds) using a part-aware neural implicit shape representation. The shape…
Cross-Shape Attention for Part Segmentation of 3D Point Clouds
Marios Loizou, Siddhant Garg, Dmitry Petrov +2
We present a deep learning method that propagates point-wise feature representations across shapes within a collection for the purpose of 3D shape segmentation. We propose a cross-…
Deep Learning for Quality Control of Subcortical Brain 3D Shape Models
Dmitry Petrov, Boris A. Gutman Egor Kuznetsov, Theo G. M. van Erp +70
We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional ne…
Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
Dmitry Petrov, Boris A. Gutman, Shih-Hua +72
As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts ha…
Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification
Dmitry Petrov, Alexander Ivanov, Joshua Faskowitz +5
There is no consensus on how to construct structural brain networks from diffusion MRI. How variations in pre-processing steps affect network reliability and its ability to disting…
Structural Connectome Validation Using Pairwise Classification
Dmitry Petrov, Boris Gutman, Alexander Ivanov +4
In this work, we study the extent to which structural connectomes and topological derivative measures are unique to individual changes within human brains. To do so, we classify st…