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Abstract #1709

Deep Learning Based Tumor Segmentation on MRI of Prostate Cancer Patient-Derived Xenografts in Mouse Models

Satvik Nayak1, Henry Salkever1, Ernesto Diaz1, Avantika Sinha1, Nikhil Deveshwar1,2, Madeline Hess1, Matthew Gibbons1, Sule Sahin1, Abhejit Rajagopal3, Peder Larson1,2, and Renuka Sriram1
1University of California, San Francisco, San Francisco, CA, United States, 2UC Berkeley-UCSF Gradaute Program in Bioengineering, San Francisco, CA, United States, 3Allen Institute, Oakland, CA, United States

Synopsis

Keywords: AI/ML Software, AI/ML Software

Motivation: Tumor volume measurement in non-subcutaneous murine xenografts is labor-intensive and prone to variability, necessitating automated approaches to improve efficiency and accuracy.

Goal(s): Develop an automated deep-learning pipeline for accurately measuring xenograft tumor volumes from MRI scans, minimizing human intervention and improving reproducibility.

Approach: We designed a two-step pipeline involving ResNet-50 for tumor classification and D-R2UNet for segmentation. Models were trained on MRI datasets with 5-fold cross-validation.

Results: The classifier achieved 89.9% accuracy. The D-R2UNet demonstrated superior segmentation performance across anatomical sites, significantly reducing segmentation time while maintaining high accuracy.

Impact: This automated segmentation pipeline enhances efficiency in preclinical tumor studies, reducing manual effort and interuser variability. It provides a robust tool for evaluating treatment efficacy, potentially enabling broader use in diverse xenograft studies and informing translational research.

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Keywords