Search research articles

Related Concept Videos

Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

You might also read

Related Articles

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

Sort by

Same author

Canapes in the canopy: Canopy macroalgae are dining hotspots for small and juvenile fishes at Orpheus Island, Great Barrier Reef.

Marine environmental research·2025

Same author

The effect of neuter status on longevity in the Rottweiler dog.

Scientific reports·2023

Same author

Field-based adipose tissue quantification in sea turtles using bioelectrical impedance spectroscopy validated with CT scans and deep learning.

Ecology and evolution·2022

Same author

Environmental conditions constrain nursery habitat value in Australian sub-tropical estuaries.

Marine environmental research·2022

Same author

Age- and sex-dependent variation in relatedness corresponds to reproductive skew, territory inheritance, and workload in cooperatively breeding cichlids.

Evolution; international journal of organic evolution·2021

Same author

A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis.

Scientific reports·2020

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: Jun 13, 2025

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

Accelerating segmentation of fossil CT scans through Deep Learning.

Espen M Knutsen^1,2, Dmitry A Konovalov³

¹College of Science and Engineering, James Cook University, Townsville, QLD, 4811, Australia. espen.knutsen@jcu.edu.au.

Scientific Reports

|September 9, 2024

Summary

This summary is machine-generated.

Deep learning now automates fossil segmentation from CT scans with minimal data. This new method significantly reduces processing time for 3D fossil models.

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Hybrid µCT-FMT imaging and image analysis

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

Related Experiment Videos

Last Updated: Jun 13, 2025

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 Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Hybrid µCT-FMT imaging and image analysis

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

Area of Science:

Paleontology
Computer Science
Artificial Intelligence

Background:

Automated segmentation of fossil CT scans is crucial for creating 3D models.
Previous methods required extensive training data, limiting their application.
Deep learning offers potential for efficient fossil data processing.

Purpose of the Study:

To develop an automated Deep Learning segmentation method for fossil CT data.
To train a model using a small fraction of the CT dataset (1-2%).
To enable high-fidelity 3D model generation of fossils extracted from surrounding rock.

Main Methods:

Implemented an automated Deep Learning segmentation workflow.
Trained a Unet segmentation model with limited CT scan data.
Validated the model's performance using Dice similarity.

Main Results:

Achieved high-fidelity 3D models of fossil material.
Successfully trained the Deep Learning model with less than 1-2% of the CT dataset.
The final Unet model reached a validation Dice similarity score of 0.96.

Conclusions:

This novel workflow significantly reduces processing time for CT-scanned fossil data.
The method enhances the availability of segmented fossil material for research.
It has the potential to revolutionize the use of Deep Learning in paleontology.