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Forsíða

Lotta María Ellingsen - Professor

Lotta María Ellingsen

–

School of Engineering and Natural Sciences

Lotta María Ellingsen

Professor

Medical imaging

Location
VR-2 / V02-357
Phone
525 - 4670
Email
lotta [at] hi.is

Unit
Faculty of Electrical and Computer Engineering
Website
https://lotta.hi.is/

Published works

2024

Segmentation methods for quantifying X-ray Computed Tomography based biomarkers to assess hip fracture riskFrontiers in Bioengineering and Biotechnology
Region-Based U-Net for Accelerated Training and Enhanced Precision in Deep Brain Segmentation
Automated fingerprint analysis as a diagnostic tool for the genetic disorder Kabuki syndromeGenetics in Medicine Open

2022

Fast and robust femur segmentation from computed tomography images for patient-specific hip fracture risk screeningComputer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
A joint ventricle and WMH segmentation from MRI for evaluation of healthy and pathological changes in the aging brainPLoS ONE
Introducing a gatekeeping system for amyloid status assessment in mild cognitive impairmentEuropean Journal of Nuclear Medicine and Molecular Imaging

2021

Automated brainstem parcellation using multi-atlas segmentation and deep neural network
Automated femur segmentation from computed tomography images using a deep neural network

2019

Applications of a deep learning method for anti-aliasing and super-resolution in MRIMagnetic Resonance Imaging
Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder
Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegalyNeuroImage: Clinical
Brain age prediction using deep learning uncovers associated sequence variantsNature Communications
Large-scale parcellation of the ventricular system using convolutional neural networks
SegAE: Unsupervised white matter lesion segmentation from brain MRIs using a CNN autoencoderNeuroImage: Clinical

2018

Deformable model reconstruction of the subarachnoid space
Shortcomings of ventricle segmentation using deep convolutional networks
Multi-atlas segmentation of the hydrocephalus brain using an adaptive ventricle atlas

2017

15q11.2 CNV affects cognitive, structural and functional correlates of dyslexia and dyscalculiaTranslational Psychiatry
Whole brain parcellation with pathology
Longitudinal multiple sclerosis lesion segmentation data resourceData in Brief
Longitudinal multiple sclerosis lesion segmentationNeuroImage

2016

Segmentation and labeling of the ventricular system in normal pressure hydrocephalus using patch-based tissue classification and multi-atlas labeling

2011

Digital Topology in Brain Image Segmentation and Registration

2010

Robust deformable image registration using prior shape information for atlas to patient registrationComputerized Medical Imaging and Graphics

2009

MjolnirInternational Journal of Biomedical Imaging

2007

Digital homeomorphisms in deformable registration
Statistical atlases of bone anatomy

2006

Deformable registration of CT pelvis images using Mjolnir
Transposon-derived repeats in the human genome and 5-methylcytosine- associated mutations in adjacent genesGene
Mjolnir
Atlas-assisted tomography

2001

Automated genotyping

Keywords for Specialisation:

Medical Image Processing

Computer Vision

Artificial Intelligence

Computed Tomography

Machine Learning