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

Imaging Studies for Cardiovascular System V: CT

Imaging Studies for Cardiovascular System V: CT

Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...

You might also read

Related Articles

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

Sort by

Same author

Innovative Techniques and Challenges in Securing Endotracheal Tubes Among Patients With Facial Hair: A Scoping Review.

Health science reports·2026

Same author

AI-Powered MRI Radiomics and Deep Learning for Preoperative Prediction of Cavernous Sinus Invasion in Pituitary Adenomas: A Clinically Oriented Review of Current Evidence.

Neuroimage. Reports·2026

Same author

Supervised Volumetric Segmentation of White and Gray Matter from Brain Positron Emission Tomography Images Using Magnetic Resonance Labels.

Journal of medical signals and sensors·2026

Same author

Swin UNETR-based prediction of [<sup>68</sup>Ga]Ga-FAPI-46 PET/CT dose rate maps in cancer patients: quantitative comparison with [<sup>18</sup>F]FDG PET/CT.

BMC medical imaging·2026

Same author

Revisiting multi-nodal radiomics with advanced feature learning for lymphoma classification: a multi-center study.

Physics in medicine and biology·2026

Same author

Clinically reliable and stable automated segmentation of DLBCL lesions on PET/CT using self-configuring nnU-Net for robust TMTV quantification.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026

Same journal

Kolmogorov-Arnold Guided Local-Global Attention for Medical Image Classification.

Journal of imaging informatics in medicine·2026

Same journal

Artificial Intelligence-Assisted Inner Ear Computed Tomography Analysis: Radiomics-Based Comparison of Affected and Unaffected Ears in Idiopathic Sudden Sensorineural Hearing Loss.

Journal of imaging informatics in medicine·2026

Same journal

High Adoption, Higher Expectations: A Cross-Sectional Survey of Radiologist Engagement with Artificial Intelligence in the United Arab Emirates.

Journal of imaging informatics in medicine·2026

Same journal

Complex-valued Multi-scale Hybrid Attention Network for Fast MRI via Sparsified Data Learning.

Journal of imaging informatics in medicine·2026

Same journal

Automatic Phase and Sequence Identification in Gd-EOB-DTPA-Enhanced Liver MRI Using Deep Convolutional and Sequential Learning.

Journal of imaging informatics in medicine·2026

Same journal

Ultrasound-Based AI in Predicting Hormone Receptor Status in Breast Cancer: Is "Digital Biopsy" Possible.

Journal of imaging informatics in medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 3, 2026

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

Enhanced Pelvic CT Segmentation via Deep Learning: A Study on Loss Function Effects.

Elnaz Ghaedi¹, Ali Asadi¹, Seyed Abolfazl Hosseini²

¹Department of Energy Engineering, Sharif University of Technology, Tehran, 8639-11365, Iran.

Journal of Imaging Informatics in Medicine

|May 28, 2025

Summary

This summary is machine-generated.

Convolutional neural networks (CNNs) automate organ at risk (OAR) segmentation in pelvic CT scans, improving radiotherapy planning. SegResNet models achieved high accuracy, offering an efficient alternative to manual delineation.

Keywords:

CT Deep learning Organ at risk Prostate Segmentation

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 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

Related Experiment Videos

Last Updated: May 3, 2026

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

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 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

Area of Science:

Medical Imaging
Artificial Intelligence in Healthcare
Radiotherapy Planning

Background:

Manual segmentation of organs at risk (OARs) in pelvic CT images for radiotherapy planning is time-consuming and prone to inter-observer variability.
Accurate OAR delineation is critical for effective radiotherapy planning, minimizing dose to healthy tissues while maximizing tumor coverage.

Purpose of the Study:

To evaluate the efficacy of various convolutional neural network (CNN) architectures for automated OAR segmentation in pelvic CT images.
To compare the performance of U-Net, ResU-Net, SegResNet, and Attention U-Net models against expert manual segmentations.
To investigate the impact of different loss functions on segmentation accuracy for OARs including the bladder, prostate, rectum, and femoral heads.

Main Methods:

Implementation and comparison of U-Net, ResU-Net, SegResNet, and Attention U-Net models using the MONAI framework.
Training and validation on pelvic CT datasets comprising 240 patients for prostate segmentation and 220 patients for other OARs.
Performance evaluation using Dice Similarity Coefficient (DSC), Jaccard Index (JI), and 95th percentile Hausdorff Distance (95thHD).

Main Results:

SegResNet demonstrated superior performance across all OARs, achieving high DSC values (e.g., 0.951 for bladder, 0.829 for prostate, 0.979 for left FH).
Attention U-Net also showed strong results, particularly for bladder and rectum segmentation.
Dice loss function provided optimal or equivalent performance for SegResNet across OARs, with DiceCE slightly improving prostate segmentation (DSC=0.845).

Conclusions:

Advanced CNNs, particularly SegResNet, offer a reliable and efficient automated solution for OAR segmentation in pelvic CT.
Optimized CNN models and loss functions can significantly enhance the precision and efficiency of radiotherapy planning.
Automated segmentation holds promise for reducing manual workload and improving consistency in clinical radiotherapy workflows.