Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Wells III, William M."'
Autor:
Rasheed, Hassan, Dorent, Reuben, Fehrentz, Maximilian, Kapur, Tina, Wells III, William M., Golby, Alexandra, Frisken, Sarah, Schnabel, Julia A., Haouchine, Nazim
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 matching-by-synthesis strategy, where intra
Externí odkaz:
http://arxiv.org/abs/2409.08169
Autor:
Sanhinova, Malika, Haouchine, Nazim, Pieper, Steve D., Wells III, William M., Balboni, Tracy A., Spektor, Alexander, Huynh, Mai Anh, Guenette, Jeffrey P., Czajkowski, Bryan, Caplan, Sarah, Doyle, Patrick, Kang, Heejoo, Hackney, David B., Alkalay, Ron N.
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, however, it is challenging du
Externí odkaz:
http://arxiv.org/abs/2402.09341
Autor:
Haouchine, Nazim, Dorent, Reuben, Juvekar, Parikshit, Torio, Erickson, Wells III, William M., Kapur, Tina, Golby, Alexandra J., Frisken, Sarah
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 range of tra
Externí odkaz:
http://arxiv.org/abs/2310.01735
This paper presents a novel predictive model, MetaMorph, for metamorphic registration of images with appearance changes (i.e., caused by brain tumors). In contrast to previous learning-based registration methods that have little or no control over ap
Externí odkaz:
http://arxiv.org/abs/2303.04849
Autor:
Carluer, Jean-Baptiste, Chauvin, Laurent, Luo, Jie, Wells III, William M., Machado, Ines, Harmouche, Rola, Toews, Matthew
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. The primary operations of the 3D SIFT code are imp
Externí odkaz:
http://arxiv.org/abs/2112.10258
Autor:
Mehrtash, Alireza, Abolmaesumi, Purang, Golland, Polina, Kapur, Tina, Wassermann, Demian, Wells III, William M.
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 an ensemble of parameter values
Externí odkaz:
http://arxiv.org/abs/2010.12721
Autor:
Bayer, Siming, Spiske, Ute, Luo, Jie, Geimer, Tobias, Wells III, William M., Ostermeier, Martin, Fahrig, Rebecca, Nabavi, Arya, Bert, Christoph, Eyupoglo, Ilker, Maier, Andreas
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. FD
Externí odkaz:
http://arxiv.org/abs/2001.05862
Autor:
Mehrtash, Alireza, Wells III, William M., Tempany, Clare M., Abolmaesumi, Purang, Kapur, Tina
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. Moreover, batch normalization and Dice loss have been used successfully t
Externí odkaz:
http://arxiv.org/abs/1911.13273
Autor:
Luo, Jie, Frisken, Sarah, Wang, Duo, Golby, Alexandra, Sugiyama, Masashi, Wells III, William M.
In image-guided neurosurgery, current commercial systems usually provide only rigid registration, partly because it is harder to predict, validate and understand non-rigid registration error. For instance, when surgeons see a discrepancy in aligned i
Externí odkaz:
http://arxiv.org/abs/1908.07709
Autor:
Sedghi, Alireza, Luo, Jie, Mehrtash, Alireza, Pieper, Steve, Tempany, Clare M., Kapur, Tina, Mousavi, Parvin, Wells III, William M.
This paper establishes an information theoretic framework for deep metric based image registration techniques. We show an exact equivalence between maximum profile likelihood and minimization of joint entropy, an important early information theoretic
Externí odkaz:
http://arxiv.org/abs/1901.00040