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

Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

536
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
536
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

1.9K
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.
1.9K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

451
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
451
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.5K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.5K
Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

2.4K
Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
2.4K
Learning Disabilities01:25

Learning Disabilities

580
Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
580

You might also read

Related Articles

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

Sort by
Same author

Spatio-Temporal Representation Decoupling and Enhancement for Federated Instrument Segmentation in Surgical Videos.

IEEE transactions on medical imaging·2026
Same author

Efficient frequency-decomposed transformer via large vision model guidance for surgical image desmoking.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2025
Same author

Taming large vision model for medical image segmentation via Dual Visual Prompt Tuning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2025
Same author

Instrument-Tissue-Guided Surgical Action Triplet Detection via Textual-Temporal Trail Exploration.

IEEE transactions on medical imaging·2025
Same author

Boosting Few-Shot Semantic Segmentation of 3D Medical Images via Collaborative Slice Alignment.

IEEE journal of biomedical and health informatics·2025
Same author

Toward Reliable AR-Guided Surgical Navigation: Interactive Deformation Modeling With Data-Driven Biomechanics and Prompts.

IEEE transactions on medical imaging·2025

Related Experiment Video

Updated: Jan 23, 2026

Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach
13:57

Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach

Published on: May 23, 2025

1.1K

Depth-induced prompt learning for laparoscopic liver landmark detection.

Ruize Cui1, Weixin Si2, Zhixi Li3

  • 1Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, China.

Medical Image Analysis
|January 21, 2026
PubMed
Summary

A new dataset (L3D-2K) and method (D2GPLand+) improve laparoscopic liver surgery by enhancing anatomical landmark detection using depth and RGB data. This aids surgeons in complex procedures.

Keywords:
Landmark detectionLaparoscopic liver surgeryPrompt learningState space models

More Related Videos

Fluorescent Laparoscopic Central Hepatectomy for Liver Cancer Using Indocyanine Green Negative Staining
03:23

Fluorescent Laparoscopic Central Hepatectomy for Liver Cancer Using Indocyanine Green Negative Staining

Published on: March 17, 2023

1.2K
Author Spotlight: Learning Systematic Bronchoscopy in a Simulation-Base Setting
04:47

Author Spotlight: Learning Systematic Bronchoscopy in a Simulation-Base Setting

Published on: June 23, 2023

3.4K

Related Experiment Videos

Last Updated: Jan 23, 2026

Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach
13:57

Laparoscopic Anatomical Liver Segment VII Resection with Liver Parenchymal Transection Following a Priority Approach

Published on: May 23, 2025

1.1K
Fluorescent Laparoscopic Central Hepatectomy for Liver Cancer Using Indocyanine Green Negative Staining
03:23

Fluorescent Laparoscopic Central Hepatectomy for Liver Cancer Using Indocyanine Green Negative Staining

Published on: March 17, 2023

1.2K
Author Spotlight: Learning Systematic Bronchoscopy in a Simulation-Base Setting
04:47

Author Spotlight: Learning Systematic Bronchoscopy in a Simulation-Base Setting

Published on: June 23, 2023

3.4K

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Surgical Technology

Background:

  • Laparoscopic liver surgery is complex due to liver deformation, challenging landmark identification.
  • Accurate anatomical landmarks are crucial for spatial perception and preoperative-to-intraoperative registration.

Purpose of the Study:

  • To develop a new dataset (L3D-2K) for liver landmark detection research.
  • To propose a novel deep learning method (D2GPLand+) to improve landmark detection performance.

Main Methods:

  • Introduced L3D-2K dataset with 2000 annotated keyframes from 47 patients.
  • Developed D2GPLand+ leveraging depth modality with Depth-aware Prompt Embedding (DPE) and Cross-dimension Unified Mamba (CUMamba).
  • Incorporated Anatomical Feature Augmentation (AFA) module for enhanced anatomical cue capture.

Main Results:

  • D2GPLand+ demonstrated superior performance compared to 17 mainstream detection models on L3D, L3D-2K, and P2ILF datasets.
  • The method effectively integrates RGB and depth features for robust landmark detection.
  • Experimental validation confirmed the efficacy of the proposed DPE, CUMamba, and AFA modules.

Conclusions:

  • The D2GPLand+ method significantly advances liver landmark detection in laparoscopic surgery.
  • The L3D-2K dataset provides a valuable resource for future research.
  • This work offers practical guidance for surgeons, improving decision-making in complex laparoscopic procedures.