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Abstract #0256

Multimodal, Multispecies and Pathology Invariant Skullstripping

Jong Sung Park1, Juhyung Ha2, Siddhesh Pravin Tahkur3, Spyridon Bakas3, and Eleftherios Garyfallidis4
1Intelligent Systems Engineering / Neuroscience, Indiana University, Bloomington, Bloomington, IN, United States, 2Computer Science, Indiana University, Bloomington, Bloomington, IN, United States, 3Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, United States, 4Intelligent Systems Engineering, Indiana University, Bloomington, Bloomington, IN, United States

Synopsis

Keywords: Analysis/Processing, Segmentation, Skullstripping

Motivation: Recent skullstripping models aim towards better generalizability, but still suffer from innate assumptions from the training data.

Goal(s): Our goal is to minimize assumptions and priors, so that a single model would be generalizable between modalities, pathology, and species.

Approach: We train a skullstripping model on synthetic data that uses two assumptions: 1. The brain would be inside the skull/head 2. The brain is the center main object in the image. Post processing step of removing false positives is done after inference.

Results: Our model succeeds in removing the skull various multimodal, multispecies and pathology tasks.

Impact: This is the first demonstration of a multimodal, multispecies and pathology invariant skullstripping model, only trained on synthetic data with minimal assumptions. Results suggest that with correct assumptions, a single model could be all we need for any skullstripping task.

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Keywords