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

Hormones and Bone Tissue

Hormones and Bone Tissue

The endocrine system produces and secretes hormones, which interact with the skeletal system. These hormones control bone growth, maintain bone once it is formed, and remodel it.
Hormones That Influence Osteoblasts and/or Maintain the Matrix
Several hormones are necessary for controlling bone growth and maintaining the bone matrix. The pituitary gland secretes growth hormone (GH), which, as its name implies, controls bone growth. This happens in several ways: first, it triggers chondrocyte...

You might also read

Related Articles

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

Sort by

Same author

Vision, Medical Cost, and Risk of Falls and Fractures in Geographic Atrophy: An American Academy of Ophthalmology IRIS Registry Analysis.

Journal of vitreoretinal diseases·2026

Same author

Standalone Canaloplasty and Trabeculotomy Using the OMNI Surgical System: A Subgroup Analysis from the American Academy of Ophthalmology IRIS<sup>®</sup> Registry (Intelligent Research in Sight).

Clinical ophthalmology (Auckland, N.Z.)·2025

Same author

Changes in cardiovascular disease risk, lung function and other clinical health outcomes when people who smoke use e-cigarettes to reduce cigarette smoking: an exploratory analysis from a randomised placebo-controlled trial.

BMJ open·2025

Same author

Epidemiology of idiopathic pulmonary fibrosis in central and Western Pennsylvania.

Respiratory research·2025

Same author

Two-phase radial endobronchial ultrasound bronchoscopy registration.

Journal of medical imaging (Bellingham, Wash.)·2025

Same author

Automatic lesion detection for narrow-band imaging bronchoscopy.

Journal of medical imaging (Bellingham, Wash.)·2024

Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026

Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026

Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026

Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026

Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026

Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 7, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

ESFPNet: Efficient Stage-Wise Feature Pyramid on Mix Transformer for Deep Learning-Based Cancer Analysis in

Qi Chang¹, Danish Ahmad², Jennifer Toth²

¹School of Electrical Engineering and Computer Science, Penn State University, University Park, PA 16802, USA.

Journal of Imaging

|August 28, 2024

Summary

This summary is machine-generated.

A new deep learning model, ESFPNet, accurately detects and segments lesions in endoscopic videos for lung and colorectal cancer screening. This efficient architecture enables real-time analysis, improving diagnostic capabilities.

Keywords:

autofluorescence bronchoscopy colonoscopy colorectal cancer deep learning efficient stage-wise feature pyramid endoscopic video analysis lesion analysis lung cancer mix transformer semantic image segmentation

More Related Videos

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

Published on: June 2, 2023

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

Related Experiment Videos

Last Updated: May 7, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

Published on: June 2, 2023

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Early detection of lung and colorectal cancer relies on identifying suspect lesions during endoscopic examinations.
Current visual inspection of endoscopic video is time-consuming and prone to errors.
Automated lesion detection can significantly improve diagnostic accuracy and efficiency.

Purpose of the Study:

To develop and evaluate a deep learning architecture for real-time lesion detection and segmentation in endoscopic videos.
To assess the performance of the proposed model in autofluorescence bronchoscopy (AFB) and colonoscopy procedures.
To demonstrate the architectural efficiency and generalizability of the model for endoscopic video analysis.

Main Methods:

Proposed ESFPNet architecture utilizing a pretrained Mix Transformer (MiT) encoder and an Efficient Stage-Wise Feature Pyramid (ESFP) decoder.
Experiments conducted on autofluorescence bronchoscopy (AFB) datasets for lung cancer screening.
Evaluation on multiple public colonoscopy databases for colorectal cancer screening.

Main Results:

ESFPNet demonstrated superior lesion segmentation performance on the AFB dataset compared to existing deep learning models.
The model achieved superior segmentation results on three public colonoscopy databases and near-best results on two others.
ESFPNet requires fewer parameters and less computation, enabling real-time video frame analysis.

Conclusions:

ESFPNet offers superior analysis performance and architectural efficiency for endoscopic video analysis.
The model's learning ability and generalizability suggest potential applications in other image segmentation domains.
This deep learning approach can enhance the early detection of cancers through improved endoscopic video analysis.