Search Results for author:
Found 20 papers, 8 papers with code
Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning
Next, the method was externally validated on a dataset including five unseen STT phenotypes in extremities, achieving 0. 81$\pm$0. 08 for CT, 0. 84$\pm$0. 09 for T1-weighted MRI, and 0. 88\pm0. 08 for previously unseen T2-weighted fat-saturated (FS) MRI.
An automated framework for brain vessel centerline extraction from CTA images
In this paper, we consider automatic lumen segmentation generation without additional annotation effort by physicians and more effective use of the generated lumen segmentation for improved centerline extraction performance.
Computer-aided diagnosis and prediction in brain disorders
Regarding prediction, i. e. estimation of the future 'condition' of the patient, we will zoom in on use cases such as predicting the disease course in multiple sclerosis and predicting patient outcomes after treatment in brain cancer.
GenNet framework: interpretable deep learning for predicting phenotypes from genetic data
Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants.
Reproducible radiomics through automated machine learning validated on twelve clinical applications
In this study we propose a framework for automatically optimizing the construction of radiomics workflows per application.
Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net.
Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease
For prediction in MCI, performances decreased for both SVM (0. 665) and CNN (0. 702).
Learning unbiased group-wise registration (LUGR) and joint segmentation: evaluation on longitudinal diffusion MRI
We here propose an analytical framework based on an unbiased learning strategy for group-wise registration that simultaneously registers images to the mean space of a group to obtain consistent segmentations.
Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach
The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA, BRAF mutational status and mitotic index (MI).
WHO 2016 subtyping and automated segmentation of glioma using multi-task deep learning
Accurate characterization of glioma is crucial for clinical decision making.
autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients
Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT.
Analyzing the effect of APOE on Alzheimer's disease progression using an event-based model for stratified populations
In this work, we determined the effect of APOE alleles on the disease progression timeline of AD using a discriminative event-based model (DEBM).
Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging
This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N=9752, 1. 5T MRI).
Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration
We propose an unsupervised deep learning method for atlas based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework.
When Weak Becomes Strong: Robust Quantification of White Matter Hyperintensities in Brain MRI scans
We showed that networks optimized using only weak labels reflecting WMH volume generalized better for WMH volume prediction than networks optimized with voxel-wise segmentations of WMH.
Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network
We show the potential of this approach to detect enlarged perivascular spaces in white matter on a large brain MRI dataset with an independent test set of 1000 scans.
Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia
In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers' Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions.
End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging
In this paper, we propose a learning method to train diagnosis models, where our approach is designed to work with relatively small datasets.
Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling
We evaluated the proposed method on Alzheimer's Disease Neuroimaging Initiative (ADNI) data and compared the results with existing state-of-the-art EBM methods.
Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy
We trained the method on images from 182 X-ray sequences from 23 different interventions.
