Evaluation of an automated segmentation method based on performances of an automated classification method

2000 ◽  
Author(s):  
Zhimin Huo ◽  
Maryellen L. Giger
Keyword(s):  
Classification Method ◽  
Automated Segmentation ◽  
Automated Classification ◽  
Segmentation Method

scholarly journals Prefrontal and hippocampal atrophy using 7-tesla magnetic resonance imaging in patients with Parkinson’s disease

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012098809
Author(s):  
Byeong H Oh ◽  
Hyeong C Moon ◽  
Aryun Kim ◽  
Hyeon J Kim ◽  
Chae J Cheong ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Magnetic Resonance ◽  
7 Tesla ◽  
Cortical Atrophy ◽  
Automated Segmentation ◽  
Healthy Controls ◽  
Segmentation Method ◽  
Resonance Imaging

Background The pathology of Parkinson’s disease leads to morphological changes in brain structure. Currently, the progressive changes in gray matter volume that occur with time and are specific to patients with Parkinson’s disease, compared to healthy controls, remain unclear. High-tesla magnetic resonance imaging might be useful in differentiating neurological disorders by brain cortical changes. Purpose We aimed to investigate patterns in gray matter changes in patients with Parkinson’s disease by using an automated segmentation method with 7-tesla magnetic resonance imaging. Material and Methods High-resolution T1-weighted 7 tesla magnetic resonance imaging volumes of 24 hemispheres were acquired from 12 Parkinson’s disease patients and 12 age- and sex-matched healthy controls with median ages of 64.5 (range, 41–82) years and 60.5 (range, 25–74) years, respectively. Subgroup analysis was performed according to whether axial motor symptoms were present in the Parkinson’s disease patients. Cortical volume, cortical thickness, and subcortical volume were measured using a high-resolution image processing technique based on the Desikan-Killiany-Tourville atlas and an automated segmentation method (FreeSurfer version 6.0). Results After cortical reconstruction, in 7 tesla magnetic resonance imaging volume segmental analysis, compared with the healthy controls, the Parkinson’s disease patients showed global cortical atrophy, mostly in the prefrontal area (rostral middle frontal, superior frontal, inferior parietal lobule, medial orbitofrontal, rostral anterior cingulate area), and subcortical volume atrophy in limbic/paralimbic areas (fusiform, hippocampus, amygdala). Conclusion We first demonstrated that 7 tesla magnetic resonance imaging detects structural abnormalities in Parkinson’s disease patients compared to healthy controls using an automated segmentation method. Compared with the healthy controls, the Parkinson’s disease patients showed global prefrontal cortical atrophy and hippocampal area atrophy.

Segmentation of design protocol using EEG

2018 ◽  
Vol 33 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Philon Nguyen ◽  
Thanh An Nguyen ◽  
Yong Zeng
Keyword(s):  
Automated Segmentation ◽  
Manual Segmentation ◽  
Engineering Systems ◽  
Verbal Protocols ◽  
Segmentation Method ◽  
Average Deviation ◽  
Domain Experts ◽  
Design Activities ◽  
The Mind ◽  
Design Protocol

AbstractDesign protocol data analysis methods form a well-known set of techniques used by design researchers to further understand the conceptual design process. Verbal protocols are a popular technique used to analyze design activities. However, verbal protocols are known to have some limitations. A recurring problem in design protocol analysis is to segment and code protocol data into logical and semantic units. This is usually a manual step and little work has been done on fully automated segmentation techniques. Physiological signals such as electroencephalograms (EEG) can provide assistance in solving this problem. Such problems are typical inverse problems that occur in the line of research. A thought process needs to be reconstructed from its output, an EEG signal. We propose an EEG-based method for design protocol coding and segmentation. We provide experimental validation of our methods and compare manual segmentation by domain experts to algorithmic segmentation using EEG. The best performing automated segmentation method (when manual segmentation is the baseline) is found to have an average deviation from manual segmentations of 2 s. Furthermore, EEG-based segmentation can identify cognitive structures that simple observation of design protocols cannot. EEG-based segmentation does not replace complex domain expert segmentation but rather complements it. Techniques such as verbal protocols are known to fail in some circumstances. EEG-based segmentation has the added feature that it is fully automated and can be readily integrated in engineering systems and subsystems. It is effectively a window into the mind.

Automated Classification Method for Crime Scene Sketches

2020 ◽  
Vol 57 (4) ◽  
pp. 041009
Author(s):  
王凯旋 Wang Kaixuan ◽  
李卓容 Li Zhuorong ◽  
王晓宾 Wang Xiaobin ◽  
严圣东 Yan Shengdong ◽  
唐云祁 Tang Yunqi
Keyword(s):  
Crime Scene ◽  
Classification Method ◽  
Automated Classification

Quantification of fat in the posterior sacroiliac joint region applying a semi-automated segmentation method

2020 ◽  
Vol 191 ◽  
pp. 105386 ◽  
Author(s):  
Amélie Poilliot ◽  
Murray Tannock ◽  
Ming Zhang ◽  
Johann Zwirner ◽  
Niels Hammer
Keyword(s):  
Sacroiliac Joint ◽  
Automated Segmentation ◽  
Segmentation Method ◽  
Joint Region

scholarly journals Semi-automated classification method addressing marine strategy framework directive (MSFD) zooplankton indicators

2016 ◽  
Vol 71 ◽  
pp. 398-405 ◽  
Author(s):  
Laura Uusitalo ◽  
Jose A. Fernandes ◽  
Eneko Bachiller ◽  
Siru Tasala ◽  
Maiju Lehtiniemi
Keyword(s):  
Classification Method ◽  
Automated Classification ◽  
Marine Strategy Framework Directive
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