Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts
Background/Objective: Longitudinal in vivo studies of murine xenograft models are widely utilized in oncology to study cancer biology and develop therapies. Magnetic resonance imaging (MRI) of these tumors is an invaluable tool for monitoring tumor growth and characterizing the tumors as well. Metho...
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2025-02-01
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| Series: | Tomography |
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| Online Access: | https://www.mdpi.com/2379-139X/11/3/21 |
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| author | Satvik Nayak Henry Salkever Ernesto Diaz Avantika Sinha Nikhil Deveshwar Madeline Hess Matthew Gibbons Sule Sahin Abhejit Rajagopal Peder E. Z. Larson Renuka Sriram |
| author_facet | Satvik Nayak Henry Salkever Ernesto Diaz Avantika Sinha Nikhil Deveshwar Madeline Hess Matthew Gibbons Sule Sahin Abhejit Rajagopal Peder E. Z. Larson Renuka Sriram |
| author_sort | Satvik Nayak |
| collection | DOAJ |
| description | Background/Objective: Longitudinal in vivo studies of murine xenograft models are widely utilized in oncology to study cancer biology and develop therapies. Magnetic resonance imaging (MRI) of these tumors is an invaluable tool for monitoring tumor growth and characterizing the tumors as well. Methods: In this work, a pipeline for automating the segmentation of xenografts in mouse models was developed. T<sub>2</sub>-weighted (T2-wt) MRI images from mice implanted with six different prostate cancer patient-derived xenografts (PDX) in the kidneys, liver, and tibia were used. The segmentation pipeline included a slice classifier to identify the slices that had tumors and subsequent training and validation using several U-Net-based segmentation architectures. Multiple combinations of the algorithm and training images for different sites were evaluated for inference quality. Results and Conclusions: The slice classifier network achieved 90% accuracy in identifying slices containing tumors. Among the various segmentation architectures tested, the dense residual recurrent U-Net achieved the highest performance in kidney tumors. When evaluated across the kidneys, tibia, and liver, this architecture performed the best when trained on all data as compared to training on only data from a single site (and inferring on a multi-site tumor images), achieving a Dice score of 0.924 across the test set. |
| format | Article |
| id | doaj-art-59e15776a31d43a3afd3fc5d9b9ab787 |
| institution | DOAJ |
| issn | 2379-1381 2379-139X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Tomography |
| spelling | doaj-art-59e15776a31d43a3afd3fc5d9b9ab7872025-08-20T02:43:04ZengMDPI AGTomography2379-13812379-139X2025-02-011132110.3390/tomography11030021Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived XenograftsSatvik Nayak0Henry Salkever1Ernesto Diaz2Avantika Sinha3Nikhil Deveshwar4Madeline Hess5Matthew Gibbons6Sule Sahin7Abhejit Rajagopal8Peder E. Z. Larson9Renuka Sriram10Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USADepartment of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USADepartment of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USADepartment of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USADepartment of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USADepartment of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USADepartment of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USADepartment of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USADepartment of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USADepartment of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USADepartment of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, CA 94158, USABackground/Objective: Longitudinal in vivo studies of murine xenograft models are widely utilized in oncology to study cancer biology and develop therapies. Magnetic resonance imaging (MRI) of these tumors is an invaluable tool for monitoring tumor growth and characterizing the tumors as well. Methods: In this work, a pipeline for automating the segmentation of xenografts in mouse models was developed. T<sub>2</sub>-weighted (T2-wt) MRI images from mice implanted with six different prostate cancer patient-derived xenografts (PDX) in the kidneys, liver, and tibia were used. The segmentation pipeline included a slice classifier to identify the slices that had tumors and subsequent training and validation using several U-Net-based segmentation architectures. Multiple combinations of the algorithm and training images for different sites were evaluated for inference quality. Results and Conclusions: The slice classifier network achieved 90% accuracy in identifying slices containing tumors. Among the various segmentation architectures tested, the dense residual recurrent U-Net achieved the highest performance in kidney tumors. When evaluated across the kidneys, tibia, and liver, this architecture performed the best when trained on all data as compared to training on only data from a single site (and inferring on a multi-site tumor images), achieving a Dice score of 0.924 across the test set.https://www.mdpi.com/2379-139X/11/3/21prostate cancermagnetic resonance imagingpatient-derived xenograftsdeep learningtumor segmentation |
| spellingShingle | Satvik Nayak Henry Salkever Ernesto Diaz Avantika Sinha Nikhil Deveshwar Madeline Hess Matthew Gibbons Sule Sahin Abhejit Rajagopal Peder E. Z. Larson Renuka Sriram Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts Tomography prostate cancer magnetic resonance imaging patient-derived xenografts deep learning tumor segmentation |
| title | Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts |
| title_full | Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts |
| title_fullStr | Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts |
| title_full_unstemmed | Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts |
| title_short | Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts |
| title_sort | deep learning based tumor segmentation of murine magnetic resonance images of prostate cancer patient derived xenografts |
| topic | prostate cancer magnetic resonance imaging patient-derived xenografts deep learning tumor segmentation |
| url | https://www.mdpi.com/2379-139X/11/3/21 |
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