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

Classification of Skeletal Muscle Fibers

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...

Classification of Bones

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...

Bone Markings

Bone Markings

Bones have various surface features that help form joints and attach to other soft tissues. Depending on the function, bone markings are categorized into articulating projections, processes for attachment, depressions, and openings.
Articulating Projections
Articulating projections are found where two bones meet to form a joint. These structures are usually found at the ends of bones. The largest articulation is a rounded projection called the head, supported by a narrow neck at the ends of...

Functional Classification of Joints

Functional Classification of Joints

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 immobile...

Structural Classification of Joints

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...

You might also read

Related Articles

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

Sort by

Same author

Development and assessment of an assisted diagnosis model using machine learning for identifying adult-onset Still's disease in fever of unknown origin: a retrospective study in China.

RMD open·2026

Same author

Dual-substrate-based full-color fluorescent probe TPE-4PA@Tb/Eu-GMP-acac ICPs for alkaline phosphatase detection and its on-site application for algal bloom alert.

Talanta·2026

Same author

Integrating ultrasound-CT-MR for preoperative multi-task prediction in ovarian cancer: achieving diagnostic parity with multidisciplinary team consensus.

NPJ digital medicine·2026

Same author

Enhancing Predictive Healthcare Models through Counterfactual Data Augmentation: A Novel Approach Leveraging Counterfactual Generative Adversarial Networks.

IEEE journal of biomedical and health informatics·2026

Same author

A brief measure of academic procrastination in university students: development and validation of the Aitken Procrastination Inventory-Short Form.

Frontiers in psychology·2026

Same author

Prediction Pipeline Selection for Incomplete Clinical Data via Missingness Fingerprints and Instance Augmentation.

Bioengineering (Basel, Switzerland)·2026

Search research articles

Related Experiment Video

Updated: Jun 20, 2026

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

Dynamically Enhanced Multi-organ Segmentation Base on Boundary-Aware Partial Label.

Yanxia Zhao, Peijun Hu, Yu Tian

IEEE Journal of Biomedical and Health Informatics

|December 1, 2025

Summary

This summary is machine-generated.

This study introduces BAPLDE-MOSNet, a novel network for abdominal CT segmentation using partially labeled data. It achieves state-of-the-art accuracy in multi-organ segmentation, improving clinical applications.

More Related Videos

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Jun 20, 2026

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Accurate abdominal CT segmentation is crucial for clinical applications but hindered by reliance on fully annotated datasets.
Existing datasets are often partially labeled and sourced from diverse medical centers, posing a challenge for model training.

Purpose of the Study:

To develop a robust multi-organ segmentation network capable of handling partially labeled abdominal CT data.
To improve the accuracy and generalization of abdominal multi-organ segmentation.

Main Methods:

Proposed BAPLDE-MOSNet, a boundary-aware multi-organ segmentation network.
Integrated task-guided attention and dynamic feature enhancement modules.
Employed multi-task learning with an edge prediction auxiliary regression network and a boundary correction module.

Main Results:

Achieved state-of-the-art performance on five public datasets with an average DSC of 93.42% and HD95 of 3.635mm.
Demonstrated superior generalization on the external BTCV dataset (average DSC 77.87%, average HD95 26.626 mm).
Outperformed specialized single-organ and existing multi-organ segmentation methods.

Conclusions:

BAPLDE-MOSNet effectively addresses the challenge of partially labeled data in abdominal CT segmentation.
The proposed network offers improved accuracy, localization, and generalization capabilities for multi-organ segmentation.