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Pixelwise tissue segmentation for precise local in-vivo dose response assessment in patient-derived xenografts
Author(s): Lucas Ewing; Sebastian W. Ahn; Oliver H. Jonas; Nobuhiko Hata
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Paper 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.

Paper Details

Date Published: 8 March 2019
PDF: 3 pages
Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 1095133 (8 March 2019); doi: 10.1117/12.2513080
Show Author Affiliations
Lucas Ewing, Concord Academy (United States)
Brigham and Women's Hospital (United States)
Sebastian W. Ahn, Brigham and Women's Hospital (United States)
Oliver H. Jonas, Brigham and Women's Hospital (United States)
Nobuhiko Hata, Brigham and Women's Hospital (United States)

Published in SPIE Proceedings Vol. 10951:
Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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