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Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network
- A. Prasoon, K. Petersen, C. Igel, F. Lauze, E. Dam, M. Nielsen
- Computer ScienceMICCAI
- 22 September 2013
A novel system for voxel classification integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D image, respectively, which performs better than a state-of-the-art method using 3D multi-scale features.
Scale-Space Theories in Computer Vision
A formal framework for locally disorderly images is discussed, which boils down to a number of intricately intertwined scale spaces, one of which is the ordinary linear scale space for the image.
Toward a Theory of Statistical Tree-Shape Analysis
- A. Feragen, P. Lo, Marleen de Bruijne, M. Nielsen, F. Lauze
- Mathematics, Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 23 July 2012
A shape space framework for tree-shapes and study metrics on the shape space, TED and QED is constructed and it is shown that the new metric QED has nice geometric properties that are needed for statistical analysis.
Sparse Multi-Scale Diffeomorphic Registration: The Kernel Bundle Framework
- Stefan Sommer, F. Lauze, M. Nielsen, X. Pennec
- Computer ScienceJournal of Mathematical Imaging and Vision
- 1 July 2013
The kernel bundle extension of the LDDMM framework that allows multiple kernels at multiple scales to be incorporated in the registration is presented and the mathematical foundation of the framework is presented with derivation of the KB-EPDiff evolution equations.
Temporal Super Resolution Using Variational Methods
A novel motion compensated (MC) TSR algorithm using variational methods for both optic flow calculation and the actual new frame interpolation is presented, and a frame doubling version of the algorithm is implemented and in testing it, it focuses on making the motion of high contrast edges to seem smooth and thus reestablish the illusion of motion pictures.
Early detection of Alzheimer's disease using MRI hippocampal texture
The presence of hippocampal texture abnormalities in MCI is highlighted, and the possibility that texture may serve as a prognostic neuroimaging biomarker of early cognitive impairment is highlighted.
Training recurrent neural networks robust to incomplete data: Application to Alzheimer’s disease progression modeling
Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring
- Michiel Kallenberg, K. Petersen, M. Lillholm
- Computer ScienceIEEE Transactions on Medical Imaging
- 18 February 2016
The state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learning texture scores are predictive of breast cancer.
Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative
A segmentation framework for fully automatic segmentation of knee MRI that combines multiatlas rigid registration with voxel classification and was trained on manual segmentations with varying configurations of bones, cartilages, and menisci demonstrated precision and accuracy comparable to manual segmentation.
Identification of progressors in osteoarthritis by combining biochemical and MRI-based markers
Combination of biochemical and MRI-based biomarkers improved diagnosis and prognosis of knee osteoarthritis and may be useful to select high-risk patients for inclusion in DMOAD clinical trials.