Acute Intracranial hemorrhage (AIH) requires urgent diagnosis in the emergency setting to mitigate eventual sequelae.
However, experienced radiologists may not always be available to make a timely diagnosis. This is especially true for
small AIH, defined as lesion smaller than 10 mm in size. A computer-aided detection (CAD) system for the detection of
small AIH would facilitate timely diagnosis. A previously developed 2D algorithm shows high false positive rates in the
evaluation based on LAC/USC cases, due to the limitation of setting up correct coordinate system for the
knowledge-based classification system. To achieve a higher sensitivity and specificity, a new 3D algorithm is developed.
The algorithm utilizes a top-hat transformation and dynamic threshold map to detect small AIH lesions. Several key
structures of brain are detected and are used to set up a 3D anatomical coordinate system. A rule-based classification of
the lesion detected is applied based on the anatomical coordinate system. For convenient evaluation in clinical
environment, the CAD module is integrated with a stand-alone system. The CAD is evaluated by small AIH cases and
matched normal collected in LAC/USC. The result of 3D CAD and the previous 2D CAD has been compared.
Timely detection of Acute Intra-cranial Hemorrhage (AIH) in an emergency environment is essential for the triage of
patients suffering from Traumatic Brain Injury. Moreover, the small size of lesions and lack of experience on the
reader's part could lead to difficulties in the detection of AIH. A CT based CAD algorithm for the detection of AIH has
been developed in order to improve upon the current standard of identification and treatment of AIH. A retrospective
analysis of the algorithm has already been carried out with 135 AIH CT studies with 135 matched normal head CT
studies from the Los Angeles County General Hospital/ University of Southern California Hospital System (LAC/USC).
In the next step, AIH studies have been collected from Walter Reed Army Medical Center, and are currently being processed using the AIH CAD system as part of implementing a multi-site assessment and evaluation of the performance of the algorithm. The sensitivity and specificity numbers from the Walter Reed study will be compared with the numbers from the LAC/USC study to determine if there are differences in the presentation and detection due to the difference in the nature of trauma between the two sites. Simultaneously, a stand-alone system with a user friendly GUI has been developed to facilitate implementation in a clinical setting.
Detection of acute intracranial hemorrhage (AIH) is a primary task in the interpretation of computed tomography (CT)
brain scans of patients suffering from acute neurological disturbances or after head trauma. Interpretation can be difficult
especially when the lesion is inconspicuous or the reader is inexperienced. We have previously developed a computeraided
detection (CAD) algorithm to detect small AIH. One hundred and thirty five small AIH CT studies from the Los
Angeles County (LAC) + USC Hospital were identified and matched by age and sex with one hundred and thirty five
normal studies. These cases were then processed using our AIH CAD system to evaluate the efficacy and constraints of
the algorithm.
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