Identification of Hemorrhage and Infarct Lesions on Brain CT Images
  using Deep Learning
Arunkumar Govindarajan Arjun Agarwal Subhankar Chattoraj Dennis Robert Satish Golla Ujjwal Upadhyay Swetha Tanamala Aarthi Govindarajan
Head Non-contrast computed tomography (NCCT) scan remain the preferred
primary imaging modality due to their widespread availability and speed.
However, the current standard for manual annotations of abnormal brain tissue
on head NCCT scans involves significant disadvantages like lack of cutoff
standardization and degeneration identification. The recent advancement of deep
learning-based computer-aided diagnostic (CAD) models in the multidisciplinary
domain has created vast opportunities in neurological medical imaging.
Significant literature has been published earlier in the automated
identification of brain tissue on different imaging modalities. However,
determining Intracranial hemorrhage (ICH) and infarct can be challenging due to
image texture, volume size, and scan quality variability. This retrospective
validation study evaluated a DL-based algorithm identifying ICH and infarct
from head-NCCT scans. The head-NCCT scans dataset was collected consecutively
from multiple diagnostic imaging centers across India. The study exhibits the
potential and limitations of such DL-based software for introduction in routine
workflow in extensive healthcare facilities.