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

Search research articles

Related Experiment Video

Updated: May 29, 2026

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

Deep-learning-based multiple organ segmentation for CT scout images: applications to automatic CT planning.

Kaylee W Fang¹, Sen Wang¹, Maria Jose Medrano¹

¹Stanford University, Department of Radiology, Stanford, California, United States.

Journal of Medical Imaging (Bellingham, Wash.)

|May 28, 2026

Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Erratum: The overexpression of lncRNA MEG3 inhibits cell viability and invasion and promotes apoptosis in ovarian cancer by sponging miR-205-5p.

International journal of clinical and experimental pathology·2026

Same author

CaMKIIδ splice variants differentially regulate vascular smooth muscle cell motility.

Vascular pharmacology·2026

Same author

Is It Time to Simplify LI-RADS Ancillary Features?

AJR. American journal of roentgenology·2026

Same author

Identification of a Novel HLA-DPA1 Allele, HLA-DPA1*02:162, Using PolyseqOne and Oxford Nanopore Sequencing.

HLA·2026

Same author

Risk factors of histologic upgrade between colposcopy-directed biopsy and loop electrosurgical excision procedure in cervical squamous intraepithelial lesions: a retrospective study.

Frontiers in medicine·2026

Same author

Site-specific limitations of ctDNA in PDAC surveillance: imaging detects pulmonary and local recurrence missed by liquid biopsy.

Abdominal radiology (New York)·2026

This summary is machine-generated.

This study introduces an AI system that automatically segments organs on CT scout images, enabling precise scan range prediction and reducing patient radiation dose. This advances automated computed tomography (CT) planning.

Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Computed tomography (CT) scout images are crucial for planning scan ranges and optimizing radiation dose.
Manual CT planning is inconsistent and can lead to excessive radiation exposure.
Accurate scan range prediction is vital for efficient and safe CT examinations.

Purpose of the Study:

To develop and evaluate a multi-organ segmentation system for CT scout images.
To enable automatic prediction of scan ranges for CT planning.
To reduce variability and radiation dose in CT procedures.

Main Methods:

Trained U-Net models with ResNet-50 encoders to segment 35 anatomic structures from frontal and lateral scout images.
Utilized organ boundaries from segmentation to automatically determine scan ranges for chest-abdomen-pelvis and abdomen-pelvis CT protocols.

Keywords:

deep learning localizer organ segmentation scan range scout image topogram

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Related Experiment Videos

Last Updated: May 29, 2026

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

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Evaluated model performance using Dice scores and assessed the impact on scan range prediction accuracy and length.

Main Results:

Achieved high mean Dice scores for organ segmentation (0.823±0.014 frontal, 0.784±0.013 lateral).
Predicted scan ranges covered ground-truth anatomy in 87%-100% of cases.
Reduced scan limit variability and scan lengths in multiple prediction tasks.

Conclusions:

The developed models accurately segment diverse organs from CT scout images.
Demonstrated significant potential for improving automatic CT scan range prediction.
Offers a promising approach for enhancing automated CT planning and radiation dose optimization.