Automated Detection of Lesions in Patients with Traumatic Brain Injury using Brain CT Images: Concept Note and Proposed Method

Abstract

Accurate and early interpretation of CT scan images in TBI patients reduces the critical time for diagnosis and management. As mentioned in other studies, automated CT interpretation using the feature extraction method is a rapid and accurate tool. Despite several studies on the machine and deep learning employing algorithms for automated CT interpretations, it has its challenges. This study presents a concept note and proposes a feature-based computer-aided diagnostic method to perform automated CT interpretation in TBI. The method consists of preprocessing, segmentation, and extraction. We have described a simple way of classifying the CT scan head into five circumferential zones in this method. The zones are identified quickly based on the anatomic characteristics and specific pathologies that affect each zone. Then, we have provided an overview of different pathologies affecting each of these zones. Utilizing these zones for automated CT interpretation will also be a helpful resource for concerned physicians during the odd and rush hours.

Automated Midline Shift Detection and Quantification in Traumatic Brain Injury: A Comprehensive Review

“…However, a comprehensive investigation and synthesis of these AI-based studies focusing on the identification and quantification of MLS in TBI cases are currently lacking. Building upon our previous work on intracranial hemorrhage (ICH) detection in TBI, which outlined future research directions, 15 this review aims to address this gap by specifically examining the detection and quantification of MLS in TBI cases. 14 The field of automatic MLS measurement has witnessed significant advancements, with several approaches being proposed to quantify MLS from head CT scans.…”