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27 February 2018 Automated volumetry of temporal horn of lateral ventricle for detection of Alzheimer's disease in CT scan
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The rapid increase in the incidence of Alzheimer’s disease (AD) has become a critical issue in low and middle income countries. In general, MR imaging has become sufficiently suitable in clinical situations, while CT scan might be uncommonly used in the diagnosis of AD due to its low contrast between brain tissues. However, in those countries, CT scan, which is less costly and readily available, will be desired to become useful for the diagnosis of AD. For CT scan, the enlargement of the temporal horn of the lateral ventricle (THLV) is one of few findings for the diagnosis of AD. In this paper, we present an automated volumetry of THLV with segmentation based on Bayes’ rule on CT images. In our method, first, all CT data sets are normalized into an atlas by using linear affine transformation and non-linear wrapping techniques. Next, a probability map of THLV is constructed in the normalized data. Then, THLV regions are extracted based on Bayes’ rule. Finally, the volume of the THLV is evaluated. This scheme was applied to CT scans from 20 AD patients and 20 controls to evaluate the performance of the method for detecting AD. The estimated THLV volume was markedly increased in the AD group compared with the controls (P < .0001), and the area under the receiver operating characteristic curve (AUC) was 0.921. Therefore, this computerized method may have the potential to accurately detect AD on CT images.
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Noriyuki Takahashi, Toshibumi Kinoshita, Tomomi Ohmura, Eri Matsuyama, and Hideto Toyoshima "Automated volumetry of temporal horn of lateral ventricle for detection of Alzheimer's disease in CT scan", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105752E (27 February 2018);

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