381 citations · 1.2k across the 12 of their papers we have counts for
12 papers
PEP: Parameter Ensembling by Perturbation
Alireza Mehrtash, Purang Abolmaesumi, Polina Golland +3
Ensembling is now recognized as an effective approach for increasing the predictive performance and calibration of deep networks. We introduce a new approach, Parameter Ensembling…
Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation
Alireza Mehrtash, William M. Wells, Clare M. Tempany +2
Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-the-art results in semantic segmentation for numerous medical imaging applications. Mor…
Deep Information Theoretic Registration
Alireza Sedghi, Jie Luo, Alireza Mehrtash +5
This paper establishes an information theoretic framework for deep metric based image registration techniques. We show an exact equivalence between maximum profile likelihood and m…
Semi-Supervised Deep Metrics for Image Registration
Alireza Sedghi, Jie Luo, Alireza Mehrtash +5
Deep metrics have been shown effective as similarity measures in multi-modal image registration; however, the metrics are currently constructed from aligned image pairs in the trai…
Model-based Catheter Segmentation in MRI-images
Andre Mastmeyer, Guillaume Pernelle, Lauren Barber +5
Accurate and reliable segmentation of catheters in MR-guided interventions remains a challenge, and a step of critical importance in clinical workflows. In this work, under reasona…
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
Mohsen Ghafoorian, Alireza Mehrtash, Tina Kapur +11
Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of nor…
Integration of the OpenIGTLink Network Protocol for Image-Guided Therapy with the Medical Platform MeVisLab
Jan Egger, Junichi Tokuda, Laurent Chauvin +4
We present the integration of the OpenIGTLink network protocol for image-guided therapy (IGT) with the medical prototyping platform MeVisLab. OpenIGTLink is a new, open, simple and…
GBM Volumetry using the 3D Slicer Medical Image Computing Platform
Jan Egger, Tina Kapur, Andriy Fedorov +7
Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions. Typically, the tumor volume is computed on a slice-by-slice basis using MR…
3T MR-Guided Brachytherapy for Gynecologic Malignancies
Tina Kapur, Jan Egger, Antonio Damato +2
Gynecologic malignancies are a leading cause of death in women worldwide. Standard treatment for many primary and recurrent gynecologic cancer cases includes a combination of exter…
Pituitary Adenoma Volumetry with 3D Slicer
Jan Egger, Tina Kapur, Christopher Nimsky +1
In this study, we present pituitary adenoma volumetry using the free and open source medical image computing platform for biomedical research: (3D) Slicer. Volumetric changes in ce…
Template-Cut: A Pattern-Based Segmentation Paradigm
Jan Egger, Bernd Freisleben, Christopher Nimsky +1
We present a scale-invariant, template-based segmentation paradigm that sets up a graph and performs a graph cut to separate an object from the background. Typically graph-based sc…
Square-Cut: A Segmentation Algorithm on the Basis of a Rectangle Shape
Jan Egger, Tina Kapur, Thomas Dukatz +4
We present a rectangle-based segmentation algorithm that sets up a graph and performs a graph cut to separate an object from the background. However, graph-based algorithms distrib…