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Article Abstract
Manual segmentation of histopathological images is both resource-intensive and prone to human error, particularly when dealing with challenging tumor types like Glioblastoma (GBM), an aggressive and highly heterogeneous brain tumor. The fuzzy borders of GBM make it especially difficult to segment, requiring models with strong generalization capabilities to achieve reliable results. In this study, we leverage the Medical Open Network for Artificial Intelligence (MONAI) framework to segment GBM tissue from hematoxylin and eosin-stained Whole-Slide Images. MONAI performed comparably well to state-of-the-art AutoML tools on our in-house dataset, achieving a Dice score of 79%. These promising results highlight the potential for future research on public datasets.
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| http://dx.doi.org/10.3233/SHTI250279 | DOI Listing |
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