20 Result(s)
within
Computer Science
Health Informatics
Image Processing and Computer Vision
Kilian M. Pohl
Chapter
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Chapter and Conference Paper
Self-supervised Longitudinal Neighbourhood Embedding
Longitudinal MRIs are often used to capture the gradual deterioration of brain structure and function caused by aging or neurological diseases. Analyzing this data via machine learning generally requires a lar...
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Chapter and Conference Paper
Longitudinal Correlation Analysis for Decoding Multi-modal Brain Development
Starting from childhood, the human brain restructures and rewires throughout life. Characterizing such complex brain development requires effective analysis of longitudinal and multi-modal neuroimaging data. H...
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Chapter and Conference Paper
Variational AutoEncoder for Regression: Application to Brain Aging Analysis
While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored. We aim to close this gap by proposing a unified...
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Chapter and Conference Paper
Data Augmentation Based on Substituting Regional MRIs Volume Scores
Due to difficulties in collecting sufficient training data, recent advances in neural-network-based methods have not been fully explored in the analysis of brain Magnetic Resonance Imaging (MRI). A possible so...
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Chapter and Conference Paper
Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis
Resting-state functional connectivity states are often identified as clusters of dynamic connectivity patterns. However, existing clustering approaches do not distinguish major states from rarely occurring min...
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Chapter and Conference Paper
End-To-End Alzheimer’s Disease Diagnosis and Biomarker Identification
As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, d...
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Chapter and Conference Paper
Joint Data Harmonization and Group Cardinality Constrained Classification
To boost the power of classifiers, studies often increase the size of existing samples through the addition of independently collected data sets. Doing so requires harmonizing the data for demographic and acqu...
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Chapter and Conference Paper
Solving Logistic Regression with Group Cardinality Constraints for Time Series Analysis
We propose an algorithm to distinguish 3D+t images of healthy from diseased subjects by solving logistic regression based on cardinality constrained, group sparsity. This method reduces the risk of overfitting...
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Chapter and Conference Paper
FLOOR: Fusing Locally Optimal Registrations
Most registration algorithms, such as Demons [1], align two scans by iteratively finding the deformation minimizing the image dissimilarity at each location and smoothing this minimum across the image domain. ...
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Chapter and Conference Paper
Collaborative Multi Organ Segmentation by Integrating Deformable and Graphical Models
Organ segmentation is a challenging problem on which significant progress has been made. Deformable models (DM) and graphical models (GM) are two important categories of optimization based image segmentation m...
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Chapter and Conference Paper
Extracting Evolving Pathologies via Spectral Clustering
A bottleneck in the analysis of longitudinal MR scans with white matter brain lesions is the temporally consistent segmentation of the pathology. We identify pathologies in 3D+t(ime) within a spectral graph cl...
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Chapter and Conference Paper
Multinomial Probabilistic Fiber Representation for Connectivity Driven Clustering
The clustering of fibers into bundles is an important task in studying the structure and function of white matter. Existing technology mostly relies on geometrical features, such as the shape of fibers, and th...
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Chapter and Conference Paper
Regional Manifold Learning for Deformable Registration of Brain MR Images
We propose a method for deformable registration based on learning the manifolds of individual brain regions. Recent publications on registration of medical images advocate the use of manifold learning in order...
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Chapter and Conference Paper
Temporal Shape Analysis via the Spectral Signature
In this paper, we adapt spectral signatures for capturing morphological changes over time. Advanced techniques for capturing temporal shape changes frequently rely on first registering the sequence of shapes a...
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Chapter and Conference Paper
Joint Segmentation and Deformable Registration of Brain Scans Guided by a Tumor Growth Model
This paper presents an approach for joint segmentation and deformable registration of brain scans of glioma patients to a normal atlas. The proposed method is based on the Expectation Maximization (EM) algorit...
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Chapter and Conference Paper
Active Mean Fields: Solving the Mean Field Approximation in the Level Set Framework
We describe a new approach for estimating the posterior probability of tissue labels. Conventional likelihood models are combined with a curve length prior on boundaries, and an approximate posterior distribut...
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Chapter and Conference Paper
Logarithm Odds Maps for Shape Representation
The concept of the Logarithm of the Odds (LogOdds) is frequently used in areas such as artificial neural networks, economics, and biology. Here, we utilize LogOdds for a shape representation that demonstrates ...
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Chapter and Conference Paper
Shape Based Segmentation of Anatomical Structures in Magnetic Resonance Images
Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases, segmentation is largely performed manually using prior know...
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Chapter and Conference Paper
A Unifying Approach to Registration, Segmentation, and Intensity Correction
We present a statistical framework that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within th...
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Chapter and Conference Paper
Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images
The paper introduces an algorithm which allows the automatic segmentation of multi channel magnetic resonance images. We extended the Expectation Maximization-Mean Field Approximation Segmenter, to include Loc...
