William M Wells 3rd
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Explore the profile of William M Wells 3rd including associated specialties, affiliations and a list of published articles.
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1247
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Recent Articles
1.
Rasheed H, Dorent R, Fehrentz M, Morozov D, Kapur T, Wells 3rd W, et al.
Unknown
. 2024 Dec;
15186:78-87.
PMID: 39736888
We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (US) images. We introduce a strategy, where intraoperative...
2.
Sanhinova M, Haouchine N, Pieper S, Wells 3rd W, Balboni T, Spektor A, et al.
Proc SPIE Int Soc Opt Eng
. 2024 Sep;
12926.
PMID: 39310216
Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome. Implementing a fully automatic and robust registration is crucial for clinical use,...
3.
Dorent R, Haouchine N, Kogl F, Joutard S, Juvekar P, Torio E, et al.
Med Image Comput Comput Assist Interv
. 2024 Apr;
2023:448-458.
PMID: 38655383
We introduce MHVAE, a deep hierarchical variational autoencoder (VAE) that synthesizes missing images from various modalities. Extending multi-modal VAEs with a hierarchical latent structure, we introduce a probabilistic formulation for...
4.
Haouchine N, Dorent R, Juvekar P, Torio E, Wells 3rd W, Kapur T, et al.
Med Image Comput Comput Assist Interv
. 2024 Feb;
14228:227-237.
PMID: 38371724
We present a novel method for intraoperative patient-to-image registration by learning Expected Appearances. Our method uses preoperative imaging to synthesize patient-specific expected views through a surgical microscope for a predicted...
5.
Xu Z, Luo J, Lu D, Yan J, Frisken S, Jagadeesan J, et al.
Med Image Comput Comput Assist Interv
. 2023 May;
2022:14-24.
PMID: 37250854
In order to tackle the difficulty associated with the ill-posed nature of the image registration problem, regularization is often used to constrain the solution space. For most learning-based registration approaches,...
6.
Mehrtash A, Abolmaesumi P, Golland P, Kapur T, Wassermann D, Wells 3rd W
Adv Neural Inf Process Syst
. 2022 Nov;
33:8895-8906.
PMID: 36415583
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 by Perturbation (PEP), that constructs...
7.
Mehrtash A, Kapur T, Tempany C, Abolmaesumi P, Wells 3rd W
Proc IEEE Int Symp Biomed Imaging
. 2022 Oct;
2021:443-447.
PMID: 36225596
Prostate cancer is the second most prevalent cancer in men worldwide. Deep neural networks have been successfully applied for prostate cancer diagnosis in magnetic resonance images (MRI). Pathology results from...
8.
Haouchine N, Juvekar P, Golby A, Wells 3rd W, Cotin S, Frisken S
Proc SPIE Int Soc Opt Eng
. 2021 Apr;
11315.
PMID: 33840881
Brain shift is a non-rigid deformation of brain tissue that is affected by loss of cerebrospinal fluid, tissue manipulation and gravity among other phenomena. This deformation can negatively influence the...
9.
Haouchine N, Juvekar P, Wells 3rd W, Cotin S, Golby A, Frisken S
Med Image Comput Comput Assist Interv
. 2021 Mar;
12264:735-744.
PMID: 33778818
Intra-operative brain shift is a well-known phenomenon that describes non-rigid deformation of brain tissues due to gravity and loss of cerebrospinal fluid among other phenomena. This has a negative influence...
10.
Sedghi A, ODonnell L, Kapur T, Learned-Miller E, Mousavi P, Wells 3rd W
Med Image Anal
. 2021 Jan;
69:101939.
PMID: 33388458
In this work, we propose a theoretical framework based on maximum profile likelihood for pairwise and groupwise registration. By an asymptotic analysis, we demonstrate that maximum profile likelihood registration minimizes...