Document Type

Conference Paper

Publication Date

2024

DOI

10.1117/12.3006596

Publication Title

Medical Imaging 2024: Image Processing, Proc. of SPIE 12926

Volume

12926

Pages

129262M

Conference Name

Medical Imaging 2024: Image Processing, 19-22 February 2024, San Diego, California

Abstract

One of the major neuropathological consequences of traumatic brain injury (TBI) is intracranial hemorrhage (ICH), which requires swift diagnosis to avert perilous outcomes. We present a new automatic hemorrhage segmentation technique via curriculum-based semi-supervised learning. It employs a pre-trained lightweight encoder-decoder framework (MobileNetV2) on labeled and unlabeled data. The model integrates consistency regularization for improved generalization, offering steady predictions from original and augmented versions of unlabeled data. The training procedure employs curriculum learning to progressively train the model at diverse complexity levels. We utilize the PhysioNet dataset to train and evaluate the proposed approach. The performance results surpass those of supervised model with an average Dice coefficient and Jaccard index of 0.573 and 0.428, respectively. Additionally, the method achieves 87.86% accuracy in hemorrhage classification and Cohen's Kappa value of 0.81, indicating substantial agreement with ground truth.

Rights

Copyright 2024 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.

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Original Publication Citation

Emon, S. H., Tseng, T.-L., Pokojovy, M., McCaffrey, P., Moen, S., & Rahman, M. F. (2024). Automatic hemorrhage segmentation in brain CT scans using curriculum-based semi-supervised learning. In O. Colliot & J. Mitra (Eds.), Medical Imaging 2024: Image Processing, Proc. of SPIE 12926 (129262M). SPIE. https://doi.org/10.1117/12.3006596

ORCID

0000-0002-2122-2572 (Pokojovy)

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