MO-D-220-07: Semi-Automated Segmentation Method to Quantify Carotid Atherosclerosis from 3D Ultrasound Images

2011 ◽  
Vol 38 (6Part26) ◽  
pp. 3718-3718
Author(s):  
E Ukwatta ◽  
J Awad ◽  
A Ward ◽  
D Buchanan ◽  
J Samarabandu ◽  
...  
Keyword(s):  
Carotid Atherosclerosis ◽  
3D Ultrasound ◽  
Ultrasound Images ◽  
Automated Segmentation ◽  
Segmentation Method

scholarly journals Towards ultrasound-guided adaptive radiotherapy for cervical cancer: Evaluation of Elekta's semiautomated uterine segmentation method on 3D ultrasound images

2017 ◽  
Vol 44 (7) ◽  
pp. 3630-3638 ◽  
Author(s):  
Sarah A. Mason ◽  
Tuathan P. O’Shea ◽  
Ingrid M. White ◽  
Susan Lalondrelle ◽  
Kate Downey ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
3D Ultrasound ◽  
Adaptive Radiotherapy ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Ultrasound Guided

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.

Automatic superpixel-based segmentation method for breast ultrasound images

2019 ◽  
Vol 121 ◽  
pp. 78-96 ◽  
Author(s):  
Mohammad I. Daoud ◽  
Ayman A. Atallah ◽  
Falah Awwad ◽  
Mahasen Al-Najjar ◽  
Rami Alazrai
Keyword(s):  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Segmentation Method
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