no code implementations • 14 Feb 2024 • Malika Sanhinova, Nazim Haouchine, Steve D. Pieper, William M. Wells III, Tracy A. Balboni, Alexander Spektor, Mai Anh Huynh, Jeffrey P. Guenette, Bryan Czajkowski, Sarah Caplan, Patrick Doyle, Heejoo Kang, David B. Hackney, Ron N. Alkalay
Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome.
no code implementations • 3 Oct 2023 • Nazim Haouchine, Reuben Dorent, Parikshit Juvekar, Erickson Torio, William M. Wells III, Tina Kapur, Alexandra J. Golby, Sarah Frisken
In contrast to conventional methods, our approach transfers the processing tasks to the preoperative stage, reducing thereby the impact of low-resolution, distorted, and noisy intraoperative images, that often degrade the registration accuracy.
no code implementations • 8 Mar 2023 • Jian Wang, Jiarui Xing, Jason Druzgal, William M. Wells III, Miaomiao Zhang
This paper presents a novel predictive model, MetaMorph, for metamorphic registration of images with appearance changes (i. e., caused by brain tumors).
1 code implementation • 19 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.
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.
no code implementations • 12 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.
no code implementations • 29 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.
no code implementations • 21 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.
no code implementations • 31 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.
no code implementations • 28 Nov 2018 • Guoxin Fan, Huaqing Liu, Zhenhua Wu, Yu-Feng Li, Chaobo Feng, Dongdong Wang, Jie Luo, Xiaofei Guan, William M. Wells III, Shisheng He
Pixel accuracy, IoU, and Dice score are used to assess the segmentation performance of lumbosacral structures.
no code implementations • 4 Apr 2018 • Alireza Sedghi, Jie Luo, Alireza Mehrtash, Steve Pieper, Clare M. Tempany, Tina Kapur, Parvin Mousavi, William M. Wells III
In this paper, we propose a strategy for learning such metrics from roughly aligned training data.
no code implementations • 20 Mar 2018 • Jie Luo, Matt Toews, Ines Machado, Sarah Frisken, Miaomiao Zhang, Frank Preiswerk, Alireza Sedghi, Hongyi Ding, Steve Pieper, Polina Golland, Alexandra Golby, Masashi Sugiyama, William M. Wells III
Kernels of the GP are estimated by using variograms and a discrete grid search method.
no code implementations • 14 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.
no code implementations • 26 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.
no code implementations • 25 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?
no code implementations • 22 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.
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.