Automated segmentation of lungs and lung tumors in mouse micro-CT scans

Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT) scans of mouse models of non-small cell lung cancer and lung fibrosis. Over 3000 scans acquired across multiple studies were used to train/v...

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Published in:	iScience Vol. 25; no. 12; p. 105712
Main Authors:	Ferl, Gregory Z., Barck, Kai H., Patil, Jasmine, Jemaa, Skander, Malamut, Evelyn J., Lima, Anthony, Long, Jason E., Cheng, Jason H., Junttila, Melissa R., Carano, Richard A.D.
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 22.12.2022 Elsevier
Subjects:	Artificial intelligence Cancer Machine learning Artificial intelligence Machine learning Cancer
ISSN:	2589-0042, 2589-0042
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Abstract	Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT) scans of mouse models of non-small cell lung cancer and lung fibrosis. Over 3000 scans acquired across multiple studies were used to train/validate a 3D U-net lung segmentation model and a Support Vector Machine (SVM) classifier to segment individual lung tumors. The U-net lung segmentation algorithm can be used to estimate changes in soft tissue volume within lungs (primarily tumors and blood vessels), whereas the trained SVM is able to discriminate between tumors and blood vessels and identify individual tumors. The trained segmentation algorithms (1) significantly reduce time required for lung and tumor segmentation, (2) reduce bias and error associated with manual image segmentation, and (3) facilitate identification of individual lung tumors and objective assessment of changes in lung and individual tumor volumes under different experimental conditions. [Display omitted] •Manually segmenting lungs/tumors in murine CT images is subjective and time consuming•Automated algorithm segments lungs and identifies individual lung tumors•Automated algorithm reduces bias and image processing time•Facilitates translational investigation of intra-subject tumor heterogeneity Cancer; Artificial intelligence; Machine learning
AbstractList	Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT) scans of mouse models of non-small cell lung cancer and lung fibrosis. Over 3000 scans acquired across multiple studies were used to train/validate a 3D U-net lung segmentation model and a Support Vector Machine (SVM) classifier to segment individual lung tumors. The U-net lung segmentation algorithm can be used to estimate changes in soft tissue volume within lungs (primarily tumors and blood vessels), whereas the trained SVM is able to discriminate between tumors and blood vessels and identify individual tumors. The trained segmentation algorithms (1) significantly reduce time required for lung and tumor segmentation, (2) reduce bias and error associated with manual image segmentation, and (3) facilitate identification of individual lung tumors and objective assessment of changes in lung and individual tumor volumes under different experimental conditions.Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT) scans of mouse models of non-small cell lung cancer and lung fibrosis. Over 3000 scans acquired across multiple studies were used to train/validate a 3D U-net lung segmentation model and a Support Vector Machine (SVM) classifier to segment individual lung tumors. The U-net lung segmentation algorithm can be used to estimate changes in soft tissue volume within lungs (primarily tumors and blood vessels), whereas the trained SVM is able to discriminate between tumors and blood vessels and identify individual tumors. The trained segmentation algorithms (1) significantly reduce time required for lung and tumor segmentation, (2) reduce bias and error associated with manual image segmentation, and (3) facilitate identification of individual lung tumors and objective assessment of changes in lung and individual tumor volumes under different experimental conditions. Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in micro-computed tomography (micro-CT) scans of mouse models of non-small cell lung cancer and lung fibrosis. Over 3000 scans acquired across multiple studies were used to train/validate a 3D U-net lung segmentation model and a Support Vector Machine (SVM) classifier to segment individual lung tumors. The U-net lung segmentation algorithm can be used to estimate changes in soft tissue volume within lungs (primarily tumors and blood vessels), whereas the trained SVM is able to discriminate between tumors and blood vessels and identify individual tumors. The trained segmentation algorithms (1) significantly reduce time required for lung and tumor segmentation, (2) reduce bias and error associated with manual image segmentation, and (3) facilitate identification of individual lung tumors and objective assessment of changes in lung and individual tumor volumes under different experimental conditions. Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT) scans of mouse models of non-small cell lung cancer and lung fibrosis. Over 3000 scans acquired across multiple studies were used to train/validate a 3D U-net lung segmentation model and a Support Vector Machine (SVM) classifier to segment individual lung tumors. The U-net lung segmentation algorithm can be used to estimate changes in soft tissue volume within lungs (primarily tumors and blood vessels), whereas the trained SVM is able to discriminate between tumors and blood vessels and identify individual tumors. The trained segmentation algorithms (1) significantly reduce time required for lung and tumor segmentation, (2) reduce bias and error associated with manual image segmentation, and (3) facilitate identification of individual lung tumors and objective assessment of changes in lung and individual tumor volumes under different experimental conditions. [Display omitted] •Manually segmenting lungs/tumors in murine CT images is subjective and time consuming•Automated algorithm segments lungs and identifies individual lung tumors•Automated algorithm reduces bias and image processing time•Facilitates translational investigation of intra-subject tumor heterogeneity Cancer; Artificial intelligence; Machine learning Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT) scans of mouse models of non-small cell lung cancer and lung fibrosis. Over 3000 scans acquired across multiple studies were used to train/validate a 3D U-net lung segmentation model and a Support Vector Machine (SVM) classifier to segment individual lung tumors. The U-net lung segmentation algorithm can be used to estimate changes in soft tissue volume within lungs (primarily tumors and blood vessels), whereas the trained SVM is able to discriminate between tumors and blood vessels and identify individual tumors. The trained segmentation algorithms (1) significantly reduce time required for lung and tumor segmentation, (2) reduce bias and error associated with manual image segmentation, and (3) facilitate identification of individual lung tumors and objective assessment of changes in lung and individual tumor volumes under different experimental conditions. Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT) scans of mouse models of non-small cell lung cancer and lung fibrosis. Over 3000 scans acquired across multiple studies were used to train/validate a 3D U-net lung segmentation model and a Support Vector Machine (SVM) classifier to segment individual lung tumors. The U-net lung segmentation algorithm can be used to estimate changes in soft tissue volume within lungs (primarily tumors and blood vessels), whereas the trained SVM is able to discriminate between tumors and blood vessels and identify individual tumors. The trained segmentation algorithms (1) significantly reduce time required for lung and tumor segmentation, (2) reduce bias and error associated with manual image segmentation, and (3) facilitate identification of individual lung tumors and objective assessment of changes in lung and individual tumor volumes under different experimental conditions. • Manually segmenting lungs/tumors in murine CT images is subjective and time consuming • Automated algorithm segments lungs and identifies individual lung tumors • Automated algorithm reduces bias and image processing time • Facilitates translational investigation of intra-subject tumor heterogeneity Cancer; Artificial intelligence; Machine learning
ArticleNumber	105712
Author	Malamut, Evelyn J. Junttila, Melissa R. Long, Jason E. Patil, Jasmine Cheng, Jason H. Carano, Richard A.D. Lima, Anthony Ferl, Gregory Z. Jemaa, Skander Barck, Kai H.
Author_xml	– sequence: 1 givenname: Gregory Z. orcidid: 0000-0002-9943-1569 surname: Ferl fullname: Ferl, Gregory Z. email: ferlg@gene.com organization: Preclinical & Translational PKPD, Genentech, South San Francisco, CA 94080, USA – sequence: 2 givenname: Kai H. orcidid: 0000-0003-1052-5719 surname: Barck fullname: Barck, Kai H. email: kbarck@gene.com organization: Department of Translational Imaging, Genentech, South San Francisco, CA 94080, USA – sequence: 3 givenname: Jasmine surname: Patil fullname: Patil, Jasmine organization: Genetic Science Group, Thermo Fisher Scientific, South San Francisco, CA 94080, USA – sequence: 4 givenname: Skander surname: Jemaa fullname: Jemaa, Skander organization: Data, Analytics and Imaging, Product Development, Genentech, South San Francisco, CA 94080, USA – sequence: 5 givenname: Evelyn J. surname: Malamut fullname: Malamut, Evelyn J. organization: Preclinical & Translational PKPD, Genentech, South San Francisco, CA 94080, USA – sequence: 6 givenname: Anthony surname: Lima fullname: Lima, Anthony organization: Department of Translational Oncology, Genentech, South San Francisco, CA 94080, USA – sequence: 7 givenname: Jason E. orcidid: 0000-0002-8229-0341 surname: Long fullname: Long, Jason E. organization: ORIC Pharmaceuticals, South San Francisco, CA 94080, USA – sequence: 8 givenname: Jason H. surname: Cheng fullname: Cheng, Jason H. organization: Department of Translational Oncology, Genentech, South San Francisco, CA 94080, USA – sequence: 9 givenname: Melissa R. orcidid: 0000-0003-3538-1192 surname: Junttila fullname: Junttila, Melissa R. organization: ORIC Pharmaceuticals, South San Francisco, CA 94080, USA – sequence: 10 givenname: Richard A.D. orcidid: 0000-0002-6799-4367 surname: Carano fullname: Carano, Richard A.D. organization: Data, Analytics and Imaging, Product Development, Genentech, South San Francisco, CA 94080, USA
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Snippet	Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT)... Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in micro-computed tomography (micro-CT) scans... Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT)...
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Title	Automated segmentation of lungs and lung tumors in mouse micro-CT scans
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