An Evolutionary Method for Model-Based Automatic Segmentation of Lower Abdomen CT Images for Radiotherapy Planning

2010 ◽  
pp. 320-327 ◽  
Author(s):  
Vitoantonio Bevilacqua ◽  
Giuseppe Mastronardi ◽  
Alessandro Piazzolla
Keyword(s):  
Automatic Segmentation ◽  
Ct Images ◽  
Radiotherapy Planning ◽  
Lower Abdomen ◽  
Model Based ◽  
Evolutionary Method ◽  
Abdomen Ct

scholarly journals A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jared Hamwood ◽  
Beat Schmutz ◽  
Michael J. Collins ◽  
Mark C. Allenby ◽  
David Alonso-Caneiro
Keyword(s):  
Deep Learning ◽  
Human Performance ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Human Observer ◽  
In Series ◽  
Magnetic Resonance Imaging Mri ◽  
High Degree ◽  
Orbital Region ◽  
Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

Automatic Segmentation of COVID-19 CT Images using improved MultiResUNet

2020 ◽  
Author(s):  
Qi Yang ◽  
Yunke Li ◽  
Mengyi Zhang ◽  
Tian Wang ◽  
Fei Yan ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ct Images

scholarly journals Reconstruction and finite element analysis of brain hemangioma model based on CT images

2018 ◽  
Vol 1064 ◽  
pp. 012049
Author(s):  
QingFei Jiang ◽  
XueYan Ma ◽  
SiYu Wang ◽  
Kai Yang
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Ct Images ◽  
Element Analysis ◽  
Model Based

GROUND-GLASS OPACITY DETECTION BY USING CORRELATION BETWEEN SUCCESSIVE SLICE IMAGES

2007 ◽  
Vol 16 (04) ◽  
pp. 583-592 ◽  
Author(s):  
HYOUNGSEOP KIM ◽  
MASAKI MAEKADO ◽  
JOO KOOI TAN ◽  
SEIJI ISHIKAWA ◽  
MASAAKI TSUKUDA
Keyword(s):  
Soft Tissues ◽  
Automatic Segmentation ◽  
Ground Glass Opacity ◽  
Processing Technique ◽  
Ct Images ◽  
Image Processing Technique ◽  
Ground Glass ◽  
Lung Region ◽  
Mdct Image ◽  
Thorax Ct

Medical imaging systems such as computed tomography, magnetic resonance imaging provided a high resolution image for powerful diagnostic tool in visual inspection fields by physician. Especially MDCT image can be used to obtain detailed images of the pulmonary anatomy, including pulmonary diseases such as the pulmonary nodules, the pulmonary vein, etc. In the medical image processing technique, segmentation is a difficult task because surrounding soft tissues and organs have similar CT values and sometimes contact with each other. We propose a new technique for automatic segmentation of lung regions and its classification for ground-glass opacity from the extracted lung regions by computer based on a set of the thorax CT images. In this paper, we segment the lung region for extraction of the region of interest employing binarization and labeling process from the inputted each slices images. The region having the largest area is regarded as the tentative lung regions. Furthermore, the ground-glass opacity is classified by correlation distribution on the slice to slice from the extracted lung region with respect to the thorax CT images. Experiment is performed employing twenty six thorax CT image sets and 96% of recognition rates were achieved. Obtained results are shown along with a discussion.

A semi-automatic and an automatic segmentation algorithm to remove the internal organs from live pig CT images

2017 ◽  
Vol 140 ◽  
pp. 290-302 ◽  
Author(s):  
Pau Xiberta ◽  
Imma Boada ◽  
Anton Bardera ◽  
Maria Font-i-Furnols
Keyword(s):  
Automatic Segmentation ◽  
Ct Images ◽  
Segmentation Algorithm ◽  
Internal Organs

scholarly journals Clinical Application of Solid Model Based on Trabecular Tibia Bone CT Images Created by 3D Printer

2015 ◽  
Vol 21 (3) ◽  
pp. 201 ◽  
Author(s):  
Jaemo Cho ◽  
Chan-Soo Park ◽  
Yeoun-Jae Kim ◽  
Kwang Gi Kim
Keyword(s):  
Clinical Application ◽  
Ct Images ◽  
Solid Model ◽  
3D Printer ◽  
Tibia Bone ◽  
Model Based

Automatic segmentation of vertebrae in 3D CT images using adaptive fast 3D pulse coupled neural networks

2018 ◽  
Vol 41 (4) ◽  
pp. 1009-1020 ◽  
Author(s):  
Mina Zareie ◽  
Hossein Parsaei ◽  
Saba Amiri ◽  
Malik Shahzad Awan ◽  
Mohsen Ghofrani
Keyword(s):  
Neural Networks ◽  
Automatic Segmentation ◽  
Ct Images ◽  
3D Ct ◽  
Coupled Neural Networks ◽  
Pulse Coupled Neural Networks
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