Integrating deep learning algorithms for the lung segmentation and body-part-specific anatomical classification with Medical Imaging and Data Resource Center (MIDRC)
Progress in Biomedical Optics and Imaging - Proceedings of SPIE
; 12469, 2023.
Artículo
en Inglés
| Scopus | ID: covidwho-20242921
ABSTRACT
Medical Imaging and Data Resource Center (MIDRC) has been built to support AI-based research in response to the COVID-19 pandemic. One of the main goals of MIDRC is to make data collected in the repository ready for AI analysis. Due to data heterogeneity, there is a need to standardize data and make data-mining easier. Our study aims to stratify imaging data according to underlying anatomy using open-source image processing tools. The experiments were performed using Google Colaboratory on computed tomography (CT) imaging data available from the MIDRC. We adopted the existing open-source tools to process CT series (N=389) to define the image sub-volumes according to body part classification, and additionally identified series slices containing specific anatomic landmarks. Cases with automatically identified chest regions (N=369) were then processed to automatically segment the lungs. In order to assess the accuracy of segmentation, we performed outlier analysis using 3D shape radiomics features extracted from the left and right lungs. Standardized DICOM objects were created to store the resulting segmentations, regions, landmarks and radiomics features. We demonstrated that the MIDRC chest CT collections can be enriched using open-source analysis tools and that data available in MIDRC can be further used to evaluate the robustness of publicly available tools. © 2023 SPIE.
computed tomography; DICOM structured report; image processing; open-source tools; radiomics; visualization; Classification (of information); Computer aided diagnosis; Computerized tomography; Data mining; Data visualization; Deep learning; Image classification; Image segmentation; Body parts; Data resources; Images processing; Imaging resources; Open source tools; Radiomic; Resource center; Structured reports; Medical imaging
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
Scopus
Tipo de estudio:
Estudio experimental
/
Revisiones
Idioma:
Inglés
Revista:
Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Año:
2023
Tipo del documento:
Artículo
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