Integrating deep learning algorithms for the lung segmentation and body-part-specific anatomical classification with Medical Imaging and Data Resource Center (MIDRC)

Nadya Shusharina; Deepa Krishnaswamy; Paul Kinahan; Andrey Fedorov

doi:10.1117/12.2653606

10 April 2023 Integrating deep learning algorithms for the lung segmentation and body-part-specific anatomical classification with Medical Imaging and Data Resource Center (MIDRC)

Nadya Shusharina, Deepa Krishnaswamy, Paul Kinahan, Andrey Fedorov

Author Affiliations +

Proceedings Volume 12469, Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications; 124690S (2023) https://doi.org/10.1117/12.2653606
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

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.

Citation Download Citation

Nadya Shusharina, Deepa Krishnaswamy, Paul Kinahan, and Andrey Fedorov "Integrating deep learning algorithms for the lung segmentation and body-part-specific anatomical classification with Medical Imaging and Data Resource Center (MIDRC)", Proc. SPIE 12469, Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications, 124690S (10 April 2023); https://doi.org/10.1117/12.2653606

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