The purpose of this study was to demonstrate the performance of a fully automated, deep learning-based brain segmentation (DLS) method in healthy controls and in patients with neurodevelopmental ...disorders, SCN1A mutation, under eleven. The whole, cortical, and subcortical volumes of previously enrolled 21 participants, under 11 years of age, with a SCN1A mutation, and 42 healthy controls, were obtained using a DLS method, and compared to volumes measured by Freesurfer with manual correction. Additionally, the volumes which were calculated with the DLS method between the patients and the control group. The volumes of total brain gray and white matter using DLS method were consistent with that volume which were measured by Freesurfer with manual correction in healthy controls. Among 68 cortical parcellated volume analysis, the volumes of only 7 areas measured by DLS methods were significantly different from that measured by Freesurfer with manual correction, and the differences decreased with increasing age in the subgroup analysis. The subcortical volume measured by the DLS method was relatively smaller than that of the Freesurfer volume analysis. Further, the DLS method could perfectly detect the reduced volume identified by the Freesurfer software and manual correction in patients with SCN1A mutations, compared with healthy controls. In a pediatric population, this new, fully automated DLS method is compatible with the classic, volumetric analysis with Freesurfer software and manual correction, and it can also well detect brain morphological changes in children with a neurodevelopmental disorder.
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PurposeTo validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentation algorithm for classifying the grades of the ...Fazekas scale and differentiating subcortical vascular dementia.MethodsThis retrospective, observational, single-institution study investigated the diagnostic performance of a deep learning-based automatic WMH volume segmentation to classify the grades of the Fazekas scale and differentiate subcortical vascular dementia. The VUNO Med-DeepBrain was used for the WMH segmentation system. The system for segmentation of WMH was designed with convolutional neural networks, in which the input image was comprised of a pre-processed axial FLAIR image, and the output was a segmented WMH mask and its volume. Patients presented with memory complaint between March 2017 and June 2018 were included and were split into training (March 2017-March 2018, n = 596) and internal validation test set (April 2018-June 2018, n = 204).ResultsOptimal cut-off values to categorize WMH volume as normal vs. mild/moderate/severe, normal/mild vs. moderate/severe, and normal/mild/moderate vs. severe were 3.4 mL, 9.6 mL, and 17.1 mL, respectively, and the AUC were 0.921, 0.956 and 0.960, respectively. When differentiating normal/mild vs. moderate/severe using WMH volume in the test set, sensitivity, specificity, and accuracy were 96.4%, 89.9%, and 91.7%, respectively. For distinguishing subcortical vascular dementia from others using WMH volume, sensitivity, specificity, and accuracy were 83.3%, 84.3%, and 84.3%, respectively.ConclusionDeep learning-based automatic WMH segmentation may be an accurate and promising method for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia.
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Purpose To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentation algorithm for classifying the grades of the ...Fazekas scale and differentiating subcortical vascular dementia. Methods This retrospective, observational, single-institution study investigated the diagnostic performance of a deep learning-based automatic WMH volume segmentation to classify the grades of the Fazekas scale and differentiate subcortical vascular dementia. The VUNO Med-DeepBrain was used for the WMH segmentation system. The system for segmentation of WMH was designed with convolutional neural networks, in which the input image was comprised of a pre-processed axial FLAIR image, and the output was a segmented WMH mask and its volume. Patients presented with memory complaint between March 2017 and June 2018 were included and were split into training (March 2017-March 2018, n = 596) and internal validation test set (April 2018-June 2018, n = 204). Results Optimal cut-off values to categorize WMH volume as normal vs. mild/moderate/severe, normal/mild vs. moderate/severe, and normal/mild/moderate vs. severe were 3.4 mL, 9.6 mL, and 17.1 mL, respectively, and the AUC were 0.921, 0.956 and 0.960, respectively. When differentiating normal/mild vs. moderate/severe using WMH volume in the test set, sensitivity, specificity, and accuracy were 96.4%, 89.9%, and 91.7%, respectively. For distinguishing subcortical vascular dementia from others using WMH volume, sensitivity, specificity, and accuracy were 83.3%, 84.3%, and 84.3%, respectively. Conclusion Deep learning-based automatic WMH segmentation may be an accurate and promising method for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia.
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Kondo et al. (DS 2014) proposed methods for computing distances between unordered rooted trees by transforming an instance of the distance computing problem into an instance of the integer ...programming problem. They showed that the tree edit distance, segmental distance, and bottom-up segmental distance problem can be respectively transformed into an integer program which has \(O(nm)\) variables and \(O(n^2m^2)\) constraints, where \(n\) and \(m\) are the number of nodes of input trees. In this work, we propose new integer programming formulations for these three distances and the bottom-up distance by applying dynamic programming approach. We divide the tree edit distance problem into \(O(nm)\) subproblems each of which has only \(O(n + m)\) constraints. For the other three distances, each subproblem can be reduced to a maximum weighted matching problem in a bipartite graph which can be solved in polynomial time. In order to evaluate our methods, we compare our method to the previous one due to Kondo et al. The experimental results show that the performance of our methods have been improved remarkably compared to that of the previous method.
碩士
國立臺灣大學
資訊工程學研究所
107
We consider the vertex cover problem with multiple covering constraints (VC-MCC), which is a generalization of the vertex cover problem. In this problem, a hypergraph $G=(V,E)$ ...is given as an input with a maximum edge size $f$, a cost function $w: V
ightarrow mathbb{Z}^+$, and edge subsets $P_1,P_2,ldots,P_r$ of $E$ along with covering requirements $k_1,k_2, ldots,k_r$
for each subset. The objective is to find a minimum cost subset $S$ of $V$ such that, for each edge subset $P_i$, at least $k_i$ edges of it are covered by $S$. This problem is a basic yet general form of the classical vertex cover problem and a generalization of the edge-partitioned vertex cover problem considered by Bera et al.
We present a primal-dual algorithm which yields an $left(f cdot H_r + H_r
ight)$-approximation for this problem where $H_r$ is the $r^{th}$ harmonic number. This improves over the previous ratio of $(3cflog r)$ due to Bera et al., where $c$ is a large constant used to ensure a low failure probability for Monte-Carlo randomized algorithms. Compared to the previous result, the proposed algorithm is deterministic and purely combinatorial, which means that no Ellipsoid solver is required for this basic problem. Our result can be seen as a novel reinterpretation of a few classical tight results using the language of LP primal-duality.
The prevalence of sudden sensorineural hearing loss and facial palsy in patients with vestibular schwannoma and the association of sudden sensorineural hearing loss or facial palsy with vestibular ...schwannoma were investigated based on the population data of Korea.
This retrospective study used the Korean National Health Insurance Service data. Patients with vestibular schwannoma and those with a previous history of sudden sensorineural hearing loss or facial palsy were identified based on diagnostic, medication, magnetic resonance imaging, or audiometric codes from 2005 to 2020. The control group was established with propensity score matching. The risk for vestibular schwannoma in patients with a previous history of sudden sensorineural hearing loss or facial palsy was analyzed.
There were 5751 patients in the vestibular schwannoma group and 23004 in the control group. The rate of patients with a previous history of sudden sensorineural hearing loss in the vestibular schwannoma group (25.8%) was significantly higher than in the control group (P -lt; .0001), as was the rate of patients with a previous history of facial palsy in the vestibular schwannoma group (4.7%) (P -lt; .0001). Previous history of sudden sensorineural hearing loss was a significant risk factor for vestibular schwannoma (hazard ratio=7.109, 95% confidence interval=6.696-7.547). Previous history of facial palsy was also a significant risk factor for vestibular schwannoma (hazard ratio=3.048, 95% confidence interval=2.695-3.447).
The prevalence of sudden sensorineural hearing loss or facial palsy was significantly higher in patients with vestibular schwannoma than in those without vestibular schwannoma. Based on the population data of Korea, sudden sensorineural hearing loss and facial palsy were significant risk factors for vestibular schwannoma.
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