Treatment for patients with acute ischemic stroke is most commonly determined based on findings on noncontrast computerized tomography (CT). Identifying hypoattenuation of the early ischemic changes on CT images is crucial for diagnosis. However, it is difficult to identify hypoattenuation with certainty. We present an atlas-based computerized method using a convolutional neural network (CNN) to identify hypoattenuation in the lentiform nucleus and the insula, two locations where hypoattenuation appears most frequently. The algorithm for this method consisted of anatomic standardization, setting of regions, creation of input images for classification, training on the CNN and classification of hypoattenuation. The regions of the lentiform nucleus and insula were set according to the Alberta Stroke Programme Early CT score (ASPECTS) method, a visual quantitative CT scoring system. AlexNet was used in the classification of the CNN architecture. We applied this method to the lentiform nucleus and insula using a database of 20 patients with right-sided hypoattenuation, 20 patients with left-sided hypoattenuation, and 20 normal subjects. Our method was evaluated using a leave-one-case-out cross-validation test. This new method had an average accuracy of 88.3%, an average sensitivity of 87.5%, and an average specificity of 90% for identifying hypoattenuation in the two regions. These results indicate that this new method has the potential to accurately identify hypoattenuation in the lentiform nucleus and the insula in patients with acute ischemic stroke.
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.
The early diagnosis of idiopathic normal pressure hydrocephalus (iNPH) considered as a treatable dementia is important. The iNPH causes enlargement of lateral ventricles (LVs). The degree of the enlargement of the LVs on CT or MR images is evaluated by using a diagnostic imaging criterion, Evans index. Evans index is defined as the ratio of the maximal width of frontal horns (FH) of the LVs to the maximal width of the inner skull (IS). Evans index is the most commonly used parameter for the evaluation of ventricular enlargement. However, manual measurement of Evans index is a time-consuming process. In this study, we present an automated method to compute Evans index on brain CT images. The algorithm of the method consisted of five major steps: standardization of CT data to an atlas, extraction of FH and IS regions, the search for the outmost points of bilateral FH regions, determination of the maximal widths of both the FH and the IS, and calculation of Evans index. The standardization to the atlas was performed by using linear affine transformation and non-linear wrapping techniques. The FH regions were segmented by using a three dimensional region growing technique. This scheme was applied to CT scans from 44 subjects, including 13 iNPH patients. The average difference in Evans index between the proposed method and manual measurement was 0.01 (1.6%), and the correlation coefficient of these data for the Evans index was 0.98. Therefore, this computerized method may have the potential to accurately compute Evans index for the diagnosis of iNPH on CT images.
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