Search Results for author: William M. Wells III

Found 14 papers, 1 papers with code

GPU optimization of the 3D Scale-invariant Feature Transform Algorithm and a Novel BRIEF-inspired 3D Fast Descriptor

1 code implementation19 Dec 2021 Jean-Baptiste Carluer, Laurent Chauvin, Jie Luo, William M. Wells III, Ines Machado, Rola Harmouche, Matthew Toews

This work details a highly efficient implementation of the 3D scale-invariant feature transform (SIFT) algorithm, for the purpose of machine learning from large sets of volumetric medical image data.

Keypoint Detection

PEP: Parameter Ensembling by Perturbation

no code implementations NeurIPS 2020 Alireza Mehrtash, Purang Abolmaesumi, Polina Golland, Tina Kapur, Demian Wassermann, William M. Wells III

In most experiments, PEP provides a small improvement in performance, and, in some cases, a substantial improvement in empirical calibration.

An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process

no code implementations12 Jan 2020 Siming Bayer, Ute Spiske, Jie Luo, Tobias Geimer, William M. Wells III, Martin Ostermeier, Rebecca Fahrig, Arya Nabavi, Christoph Bert, Ilker Eyupoglo, Andreas Maier

For a wide range of clinical applications, such as adaptive treatment planning or intraoperative image update, feature-based deformable registration (FDR) approaches are widely employed because of their simplicity and low computational complexity.

Image Registration

Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation

no code implementations29 Nov 2019 Alireza Mehrtash, William M. Wells III, Clare M. Tempany, Purang Abolmaesumi, Tina Kapur

We make the following contributions: 1) We systematically compare cross entropy loss with Dice loss in terms of segmentation quality and uncertainty estimation of FCNs; 2) We propose model ensembling for confidence calibration of the FCNs trained with batch normalization and Dice loss; 3) We assess the ability of calibrated FCNs to predict segmentation quality of structures and detect out-of-distribution test examples.

Medical Image Segmentation Out-of-Distribution Detection +1

Are Registration Uncertainty and Error Monotonically Associated

no code implementations21 Aug 2019 Jie Luo, Sarah Frisken, Duo Wang, Alexandra Golby, Masashi Sugiyama, William M. Wells III

Probabilistic image registration (PIR) methods provide measures of registration uncertainty, which could be a surrogate for assessing the registration error.

Image Registration

Deep Information Theoretic Registration

no code implementations31 Dec 2018 Alireza Sedghi, Jie Luo, Alireza Mehrtash, Steve Pieper, Clare M. Tempany, Tina Kapur, Parvin Mousavi, William M. Wells III

This paper establishes an information theoretic framework for deep metric based image registration techniques.

Image Registration

On the Applicability of Registration Uncertainty

no code implementations14 Mar 2018 Jie Luo, Alireza Sedghi, Karteek Popuri, Dana Cobzas, Miaomiao Zhang, Frank Preiswerk, Matthew Toews, Alexandra Golby, Masashi Sugiyama, William M. Wells III, Sarah Frisken

For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters.

Image Registration

Misdirected Registration Uncertainty

no code implementations26 Apr 2017 Jie Luo, Karteek Popuri, Dana Cobzas, Hongyi Ding, William M. Wells III, Masashi Sugiyama

Since the transformation is such an essential component of registration, most existing researches conventionally quantify the registration uncertainty, which is the confidence in the estimated spatial correspondences, by the transformation uncertainty.

Image Registration Medical Image Registration

Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

no code implementations25 Feb 2017 Mohsen Ghafoorian, Alireza Mehrtash, Tina Kapur, Nico Karssemeijer, Elena Marchiori, Mehran Pesteie, Charles R. G. Guttmann, Frank-Erik de Leeuw, Clare M. Tempany, Bram van Ginneken, Andriy Fedorov, Purang Abolmaesumi, Bram Platel, William M. Wells III

In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?

Domain Adaptation Lesion Segmentation

Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models

no code implementations22 Jan 2015 Marc Niethammer, Kilian M. Pohl, Firdaus Janoos, William M. Wells III

A specific implementation of that model is the Chan-Vese segmentation model (CV), in which the binary segmentation task is defined by a Gaussian likelihood and a prior regularizing the length of the segmentation boundary.

Image Denoising Semantic Segmentation

Deformable Registration of Feature-Endowed Point Sets Based on Tensor Fields

no code implementations CVPR 2014 Demian Wassermann, James Ross, George Washko, William M. Wells III, Raul San Jose-Estepar

Our framework relies on a dense tensor field representation which we implement sparsely as a kernel mixture of tensor fields.

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