Poster - Thur Eve - 05: Semi-Automated Segmentation of Lung Tumours on CT Scans Using Level Set Sparse Field Active Model

2010 ◽  
Vol 37 (7Part2) ◽  
pp. 3887-3887
Author(s):  
J Awad ◽  
L Wilson ◽  
G Parraga ◽  
A Fenster
Keyword(s):  
Level Set ◽  
Ct Scans ◽  
Automated Segmentation ◽  
Lung Tumours ◽  
Active Model

scholarly journals Fully automated preoperative segmentation of temporal bone structures from clinical CT scans

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
C. A. Neves ◽  
E. D. Tran ◽  
I. M. Kessler ◽  
N. H. Blevins
Keyword(s):  
Facial Nerve ◽  
Inner Ear ◽  
Temporal Bone ◽  
Hausdorff Distance ◽  
Sigmoid Sinus ◽  
Ct Scans ◽  
Automated Segmentation ◽  
Dice Coefficient ◽  
Lateral Skull Base ◽  
End To End

AbstractMiddle- and inner-ear surgery is a vital treatment option in hearing loss, infections, and tumors of the lateral skull base. Segmentation of otologic structures from computed tomography (CT) has many potential applications for improving surgical planning but can be an arduous and time-consuming task. We propose an end-to-end solution for the automated segmentation of temporal bone CT using convolutional neural networks (CNN). Using 150 manually segmented CT scans, a comparison of 3 CNN models (AH-Net, U-Net, ResNet) was conducted to compare Dice coefficient, Hausdorff distance, and speed of segmentation of the inner ear, ossicles, facial nerve and sigmoid sinus. Using AH-Net, the Dice coefficient was 0.91 for the inner ear; 0.85 for the ossicles; 0.75 for the facial nerve; and 0.86 for the sigmoid sinus. The average Hausdorff distance was 0.25, 0.21, 0.24 and 0.45 mm, respectively. Blinded experts assessed the accuracy of both techniques, and there was no statistical difference between the ratings for the two methods (p = 0.93). Objective and subjective assessment confirm good correlation between automated segmentation of otologic structures and manual segmentation performed by a specialist. This end-to-end automated segmentation pipeline can help to advance the systematic application of augmented reality, simulation, and automation in otologic procedures.

Automated segmentation of skin lesions: Modified Fuzzy C mean thresholding based level set method

INMIC ◽  
2013 ◽  
Author(s):  
Ammara Masood ◽  
Adel Ali Al Jumaily ◽  
Azadeh Noori Hoshyar ◽  
Omama Masood
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Skin Lesions ◽  
Automated Segmentation

Lung tumours segmentation on CT using sparse field active model

2011 ◽  
Author(s):  
Joseph Awad ◽  
Laura Wilson ◽  
Grace Parraga ◽  
Aaron Fenster
Keyword(s):  
Lung Tumours ◽  
Active Model

scholarly journals Automated segmentation of intraretinal cystoid macular edema based on Gaussian mixture model

2019 ◽  
Vol 13 (01) ◽  
pp. 1950020
Author(s):  
Jinghong Wu ◽  
Sijie Niu ◽  
Qiang Chen ◽  
Wen Fan ◽  
Songtao Yuan ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Level Set ◽  
Spatial Information ◽  
High Sensitivity ◽  
Gaussian Mixture ◽  
Automated Segmentation ◽  
Potential Fluid ◽  
Sd Oct

We introduce a method based on Gaussian mixture model (GMM) clustering and level-set to automatically detect intraretina fluid on diabetic retinopathy (DR) from spectral domain optical coherence tomography (SD-OCT) images in this paper. First, each B-scan is segmented using GMM clustering. The original clustering results are refined using location and thickness information. Then, the spatial information among every consecutive five B-scans is used to search potential fluid. Finally, the improved level-set method is used to obtain the accurate boundaries. The high sensitivity and accuracy demonstrated here show its potential for detection of fluid.

scholarly journals A “loop” shape descriptor and its application to automated segmentation of airways from CT scans

2015 ◽  
Vol 42 (6Part1) ◽  
pp. 3076-3084 ◽  
Author(s):  
Jiantao Pu ◽  
Chenwang Jin ◽  
Nan Yu ◽  
Yongqiang Qian ◽  
Xiaohua Wang ◽  
...  
Keyword(s):  
Shape Descriptor ◽  
Ct Scans ◽  
Automated Segmentation ◽  
Loop Shape

scholarly journals Automated Segmentation of Abdominal Skeletal Muscle in Pediatric CT Scans Using Deep Learning

2021 ◽  
pp. e200130
Author(s):  
James Castiglione ◽  
Elanchezhian Somasundaram ◽  
Leah A. Gilligan ◽  
Andrew T. Trout ◽  
Samuel Brady
Keyword(s):  
Skeletal Muscle ◽  
Deep Learning ◽  
Ct Scans ◽  
Automated Segmentation ◽  
Pediatric Ct
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