Automatic Hemorrhage Segmentation in Brain CT Scans Using Curriculum-Based Semi-Supervised Learning

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2024
Subtitle of host publicationImage Processing
EditorsOlivier Colliot, Jhimli Mitra
ISBN (Electronic)9781510671560
StatePublished - 2024
EventMedical Imaging 2024: Image Processing - San Diego, United States
Duration: Feb 19 2024Feb 22 2024

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2024: Image Processing
Country/TerritoryUnited States
CitySan Diego


  • Brain CT Scans
  • Curriculum Learning
  • Hemorrhage Segmentation
  • Intracranial Hemorrhage
  • Semi-Supervised Learning
  • Traumatic Brain Injury

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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