Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.3K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Associative Learning01:27

Associative Learning

362
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
362
Functional Classification of Joints01:09

Functional Classification of Joints

4.1K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
4.1K
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

454
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
454
Singularity Functions for Shear01:26

Singularity Functions for Shear

132
In structural analysis, singularity functions are crucial in simplifying the representation of shear forces in beams under discontinuous loading. These functions describe discontinuous  variations in shear force across a beam with varying loads by using a single mathematical expression, regardless of the complexity of the loading conditions. The singularity functions are derived from creating a free-body diagram of the beam and then making conceptual cuts at specific points to examine the...
132

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Callus formation during healing is guided by local strain: a retrospective clinical observation.

BMC musculoskeletal disorders·2026
Same author

Uncertainty estimation and probabilistic skull shape reconstruction using bayesian neural networks.

Scientific reports·2026
Same author

A deep-learning framework reveals whole-body perturbations at cell level.

Nature·2026
Same author

Whole-body vibration decreases pain and cartilage degeneration in male and female mice with osteoarthritis.

Pain reports·2026
Same author

Clinical Application of Deep Learning for Spine MRI Interpretation: A Multicenter Evaluation of Artificial-Intelligence-Assisted versus Manual Reading on Diagnostic Agreement with the Reference Standard.

Research (Washington, D.C.)·2026
Same author

Generalist foundation models from a multimodal dataset for 3D computed tomography.

Nature biomedical engineering·2026
Same journal

Generative morphodynamic forecasting enables robust zero-shot volumetric medical segmentation.

Medical image analysis·2026
Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026
Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026
Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026
Same journal

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026
Same journal

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026
See all related articles

Related Experiment Video

Updated: Jul 2, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Learning continuous shape priors from sparse data with neural implicit functions.

Tamaz Amiranashvili1, David Lüdke2, Hongwei Bran Li1

  • 1Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Department of Computer Science, Technical University of Munich, Munich, Germany.

Medical Image Analysis
|February 23, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical shape model using neural implicit functions to reconstruct high-resolution 3D shapes from sparse medical scans. The model effectively learns shape variations and differentiates between healthy and pathological anatomies.

Keywords:
Continuous shape representationsOsteoarthritis classificationRepresentation learningShape modelingShape reconstruction

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

537
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

Related Experiment Videos

Last Updated: Jul 2, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

537
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

Area of Science:

  • Medical Image Analysis
  • Computational Anatomy
  • Machine Learning

Background:

  • Statistical shape models (SSMs) are vital for medical image analysis tasks like reconstruction and classification.
  • Current SSMs are limited by the resolution of training data, hindering high-resolution shape prior learning from sparse scans.
  • Anisotropic scans with large slice distances are common in clinical practice (e.g., CT, MRI), posing challenges for existing methods.

Purpose of the Study:

  • To develop a novel shape modeling approach capable of training on sparse, low-resolution medical image data.
  • To enable the reconstruction of high-resolution 3D shapes from limited input, overcoming limitations of current methods.
  • To create a robust latent space representation for sparse shapes, invariant to acquisition parameters and capable of discriminating between healthy and pathological cases.

Main Methods:

  • Utilized neural implicit functions for continuous shape representation.
  • Trained the model on sparse, binary segmentation masks with large inter-slice distances.
  • Developed a method to embed diverse sparse segmentation masks into a unified, low-dimensional latent space.

Main Results:

  • Successfully reconstructed high-resolution shapes from as few as three orthogonal slices.
  • Demonstrated the model's ability to create a latent space invariant to acquisition direction, resolution, and spacing.
  • Showcased the latent representation's effectiveness in discriminating between healthy and pathological shapes from sparse data.
  • Validated the model on lumbar vertebra and distal femur datasets, confirming smooth latent space and characteristic shape variation capture.

Conclusions:

  • The proposed neural implicit function-based shape modeling approach effectively addresses the challenge of sparse medical imaging data.
  • This method enables high-resolution shape reconstruction and robust feature representation from limited, clinically relevant scans.
  • The developed model holds significant potential for improving shape analysis and diagnosis in medical imaging applications.