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Found 14 papers, 5 papers with code
Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling
Shape reconstruction from imaging volumes is a recurring need in medical image analysis.
Anatomy Completor: A Multi-class Completion Framework for 3D Anatomy Reconstruction
We propose two paradigms based on a 3D denoising auto-encoder (DAE) to solve the anatomy reconstruction problem: (i) the DAE learns a many-to-one mapping between incomplete and complete instances; (ii) the DAE learns directly a one-to-one residual mapping between the incomplete instances and the target anatomies.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
The HoloLens in Medicine: A systematic Review and Taxonomy
The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality display, is the main player in the recent boost in medical augmented reality research.
Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model
We evaluate the SSM on several cranial implant design tasks, and the results show that, while the SSM performs suboptimally on synthetic defects compared to CNN-based approaches, it is capable of reconstructing large and complex defects with only minor manual corrections.
Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate
Then to assess the uncertainty in the output stress distribution due to this stochastic constitutive model, a convolutional neural network, specifically a Bayesian encoder-decoder, was used as a surrogate model that maps the random input fields to the output stress distribution obtained from the FE analysis.
Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations
Conclusion: This suggests that pre-existing annotations can be an inexpensive resource in clinics to ease ill-posed and repetitive tasks like cross-section extraction for surveillance of aortic dissections.
Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation
However, downsampling can lead to a loss of image quality, which is undesirable especially in reconstruction tasks, where the fine geometric details need to be preserved.
AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo
The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if completed with a contrast agent, called CT angiography (CTA).
Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact
These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years.
Medical Deep Learning -- A systematic Meta-Review
With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information.
A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge
The approach generates high-quality implants in two steps: First, an encoder-decoder network learns a coarse representation of the implant from down-sampled, defective skulls; The coarse implant is only used to generate the bounding box of the defected region in the original high-resolution skull.
An Online Platform for Automatic Skull Defect Restoration and Cranial Implant Design
Such an automatic cranial implant design system can be integrated into the clinical practice to improve the current routine for surgeries related to skull defect repair (e. g., cranioplasty).
Design and Development of a Web-based Tool for Inpainting of Dissected Aortae in Angiography Images
Medical imaging is an important tool for the diagnosis and the evaluation of an aortic dissection (AD); a serious condition of the aorta, which could lead to a life-threatening aortic rupture.
