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8 March 2019 Pixelwise tissue segmentation for precise local in-vivo dose response assessment in patient-derived xenografts
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Abstract
Patient-specific dose response against chemotherapeutics can be assessed through local in situ release of drugs at sub-therapeutic concentrations. Such controlled release can be performed in patient-derived xenografts (PDXs), which offer pre-clinical methods for mimicking the tumor microenvironment. However, the prolonged co-existence of intermingled human and mouse tissues poses a number of challenges for histological image analysis. Manual annotation of regions of human tissue is labor-intensive and lacks reproducibility and scalability, complicating the investigation of multiplexed local drug effects near drug-dispensing microdevices. To this end, we apply a random forest algorithm for segmenting histological images to obtain binary masks for refined image analysis. Region-of-interest masks obtained using this supervised learning approach allow for a spatially refined assessment of the dose response in heterogeneous tissue8. We achieved a Dice similarity coefficient score (DSC) of 0.56 with the random forest classifier.
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Lucas Ewing, Sebastian W. Ahn, Oliver H. Jonas, and Nobuhiko Hata "Pixelwise tissue segmentation for precise local in-vivo dose response assessment in patient-derived xenografts", Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 1095133 (8 March 2019); https://doi.org/10.1117/12.2513080
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