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Related Experiment Video

Updated: Jun 26, 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

Cloud-based large-scale curation of medical imaging data using AI segmentation.

Vamsi Krishna Thiriveedhi¹, Deepa Krishnaswamy¹, David Clunie²

¹Brigham and Women's Hospital, Boston, MA.

Research Square

|May 15, 2024

Summary

Cloud computing enables efficient, large-scale medical image analysis. This study used cloud resources to analyze over 126,000 lung screening CT scans, completing the task in hours at a low cost.

Keywords:

AI cloud computing computed tomography image segmentation radiomics

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Area of Science:

Medical Imaging
Artificial Intelligence
Cloud Computing

Background:

Medical imaging AI requires significant computational power, often exceeding on-premises capabilities.
Cloud computing offers scalable and economical solutions for demanding AI tasks.
Few studies evaluate cloud price/performance for medical image analysis.

Approach:

Evaluated NCI CRDC Cloud Resources (Terra and Seven Bridges) for AI-based curation of National Lung Screening Trial (NLST) CT images.
Performed automatic image segmentation and radiomics feature extraction on >126,000 CT volumes using TotalSegmentator and pyradiomics.
Utilized over 21,000 Virtual Machines (VMs) for rapid analysis.

Key Points:

Analysis completed in under 9 hours using cloud resources, compared to an estimated 522 days on a single workstation.
Total cost for large-scale analysis was $1,011.05.
Generated 9,565,554 segmentations and radiomics features for the NLST dataset.

Conclusions:

Cloud computing provides a cost-effective and scalable solution for large-scale medical image analysis.
Developed CloudSegmentator, an open-source workflow for reproducible medical image computing.
Offers practical recommendations for optimizing cloud resource utilization in medical imaging research.