Han et al., 2022 - Google Patents
Joint synthesis and registration network for deformable MR-CBCT image registration for neurosurgical guidanceHan et al., 2022
View PDF- Document ID
- 7118481353076978099
- Author
- Han R
- Jones C
- Lee J
- Zhang X
- Wu P
- Vagdargi P
- Uneri A
- Helm P
- Luciano M
- Anderson W
- Siewerdsen J
- Publication year
- Publication venue
- Physics in Medicine & Biology
External Links
Snippet
Objective. The accuracy of navigation in minimally invasive neurosurgery is often challenged by deep brain deformations (up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach). We propose a deep learning-based deformable …
- 238000007408 cone-beam computed tomography 0 title abstract description 165
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20128—Atlas-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/0068—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for image registration, e.g. elastic snapping
- G06T3/0081—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for image registration, e.g. elastic snapping by elastic snapping
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Fu et al. | Deep learning in medical image registration: a review | |
Lei et al. | 4D-CT deformable image registration using multiscale unsupervised deep learning | |
Jiang et al. | A multi-scale framework with unsupervised joint training of convolutional neural networks for pulmonary deformable image registration | |
De Bruijne et al. | Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part III | |
Chandra et al. | Focused shape models for hip joint segmentation in 3D magnetic resonance images | |
Han et al. | Joint synthesis and registration network for deformable MR-CBCT image registration for neurosurgical guidance | |
Zeng et al. | Label-driven magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) registration using weakly supervised learning for MRI-guided prostate radiotherapy | |
Zhang et al. | GroupRegNet: a groupwise one-shot deep learning-based 4D image registration method | |
US12175621B2 (en) | Unsupervised deformable image registration method using cycle-consistent neural network and apparatus therefor | |
Sun et al. | Synthesis of pseudo-CT images from pelvic MRI images based on an MD-CycleGAN model for radiotherapy | |
Fu et al. | Synthetic CT-aided MRI-CT image registration for head and neck radiotherapy | |
Xu et al. | A review on AI-based medical image computing in head and neck surgery | |
Castro-Mateos et al. | 3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images | |
Forsberg et al. | Model-based registration for assessment of spinal deformities in idiopathic scoliosis | |
Touati et al. | A feature invariant generative adversarial network for head and neck MRI/CT image synthesis | |
Liu et al. | A female pelvic bone shape model for air/bone separation in support of synthetic CT generation for radiation therapy | |
Salehi et al. | Deep learning-based non-rigid image registration for high-dose rate brachytherapy in inter-fraction cervical cancer | |
Lei et al. | Male pelvic CT multi-organ segmentation using synthetic MRI-aided dual pyramid networks | |
Cao et al. | CDFRegNet: a cross-domain fusion registration network for CT-to-CBCT image registration | |
Chang et al. | A generative adversarial network (GAN)-based technique for synthesizing realistic respiratory motion in the extended cardiac-torso (XCAT) phantoms | |
Yu et al. | Accelerated gradient-based free form deformable registration for online adaptive radiotherapy | |
Chandra et al. | Fast automated segmentation of multiple objects via spatially weighted shape learning | |
Huang et al. | 3D vertebrae labeling in spine CT: an accurate, memory-efficient (Ortho2D) framework | |
Kim et al. | Multi-domain CT translation by a routable translation network | |
Fourcade et al. | Deformable image registration with deep network priors: a study on longitudinal PET images |