Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

You might also read

Related Articles

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

Sort by

Same author

Immunity-and-matrix-regulatory cells promote hyaline-like cartilage repair in osteoarthritis.

Bioactive materials·2026

Same author

Optimization of Thulium Fiber Laser Lithotripsy Efficacy Based on Multidimensional Parameter Combination Strategies.

European urology open science·2026

Same author

Revelations of pancreatic cancer treated by high-intensity focused ultrasound.

Discover oncology·2026

Same author

Differentiating benign from malignant pulmonary nodules in the context of bronchiectasis: a retrospective study.

Annals of medicine·2026

Same author

Stiff-yet-tough glassy hydrogels for tendon rupture repair.

Bioactive materials·2026

Same author

Consistent Brain Entropy Changes During Rumination Across Three Magnetic Resonance Imaging Scanners.

Neural plasticity·2026

Same journal

ESD-VesNet: uncertainty-aware vessel segmentation network for endoscopic submucosal dissection with hard negative mining.

International journal of computer assisted radiology and surgery·2026

Same journal

Lean Unet: a compact model for image segmentation.

International journal of computer assisted radiology and surgery·2026

Same journal

Strain alignment: toward assessing mechanical plausibility of predicted displacement fields.

International journal of computer assisted radiology and surgery·2026

Same journal

Vascular geometry characterization for AI-based endovascular navigation.

International journal of computer assisted radiology and surgery·2026

Same journal

Nail It! A learning framework for autonomous surgical suturing and teleoperation on the dVRK.

International journal of computer assisted radiology and surgery·2026

Same journal

Correspondence-free local-to-global liver deformation correction via implicit neural representation and biomechanical model.

International journal of computer assisted radiology and surgery·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 26, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

SfMDiffusion: self-supervised monocular depth estimation in endoscopy based on diffusion models.

Yu Li¹, Da Chang², Die Luo³

¹The Institute of Technological Sciences, Wuhan University, Wuhan, China.

International Journal of Computer Assisted Radiology and Surgery

|February 24, 2025

Summary

This summary is machine-generated.

SfMDiffusion enhances 3D reconstruction in laparoscopic surgery using a novel self-supervised monocular depth estimation (MDE) framework. This method achieves superior accuracy without ground-truth data, improving image-guided surgical techniques.

Keywords:

Diffusion Discriminative prior Monocular depth estimation Self-supervised learning

More Related Videos

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

Related Experiment Videos

Last Updated: May 26, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

Area of Science:

Medical Imaging
Computer Vision
Surgical Technology

Background:

Accurate 3D reconstruction from endoscopic video is vital for image-guided laparoscopic surgery.
Existing monocular depth estimation (MDE) methods struggle with surgical scene complexities like reflections and poor lighting.

Purpose of the Study:

To develop a robust self-supervised framework for monocular depth estimation (MDE) in laparoscopic surgery.
To overcome limitations of current MDE techniques in complex surgical environments.

Main Methods:

Introduced SfMDiffusion, a novel diffusion-based self-supervised framework for MDE.
Integrated a denoising diffusion process with pseudo-ground-truth depth maps, knowledge distillation, and discriminative priors.
Enabled accurate depth estimation without requiring ground-truth depth data during training.

Main Results:

SfMDiffusion demonstrated superior performance on SCARED and Hamlyn datasets.
Achieved low error metrics: Abs Rel of 0.049, Sq Rel of 0.366, RMSE of 4.305 on SCARED.
Reported Abs Rel of 0.067, Sq Rel of 0.800, RMSE of 7.465 on Hamlyn.

Conclusions:

SfMDiffusion offers an innovative solution for 3D reconstruction in image-guided surgery.
Future research will focus on computational optimization and validation in varied surgical settings.
The code is publicly available for further research and development.