Identification of Hemorrhage and Infarct Lesions on Brain CT Images using Deep Learning

TL;DR

This study evaluates a deep learning algorithm for automatically detecting hemorrhage and infarct lesions on brain CT images, aiming to improve diagnostic accuracy and efficiency in clinical settings.

Contribution

It presents a validated deep learning model specifically designed for identifying hemorrhage and infarct on brain CT scans, addressing challenges of variability and manual annotation limitations.

Findings

The DL model achieved high accuracy in lesion detection.

The study demonstrates potential for routine clinical integration.

Limitations include variability in scan quality and texture.

Abstract

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.