Automatic hemorrhage segmentation in brain CT scans using curriculum-based semi-supervised learning.

Solayman Hossain Emon; Tzu-Liang (Bill) Tseng; Michael Pokojovy; Peter McCaffrey; Scott Moen; Md Fashiar Rahman

doi:10.1117/12.3006596

2 April 2024 Automatic hemorrhage segmentation in brain CT scans using curriculum-based semi-supervised learning.

Solayman Hossain Emon, Tzu-Liang (Bill) Tseng, Michael Pokojovy, Peter McCaffrey, Scott Moen, Md Fashiar Rahman

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129262M (2024) https://doi.org/10.1117/12.3006596
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

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.

Citation Download Citation

Solayman Hossain Emon, Tzu-Liang (Bill) Tseng, Michael Pokojovy, Peter McCaffrey, Scott Moen, and Md Fashiar Rahman "Automatic hemorrhage segmentation in brain CT scans using curriculum-based semi-supervised learning.", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129262M (2 April 2024); https://doi.org/10.1117/12.3006596

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