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Medical imaging

Restoration of ultrasound images using spatially-variant kernel deconvolution

Mihai Florea
Sergiy Vorobyov

Most of the existing ultrasound image restoration methods consider a spatially-invariant point-spread function (PSF) model and circulant boundary conditions. While computationally efficient, this model is not realistic and severely limits the quality of reconstructed images. In this work, we address ultrasound image restoration under the hypothesis of vertical variation of the PSF. To regularize the solution, we use the classical elastic net constraint.

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Authors:
Mihai Florea
Sergiy Vorobyov
Mihai I. Florea, Adrian Basarab, Denis Kouame, Sergiy A. Vorobyov
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17 April 2018 - 9:01pm
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[1] Mihai I. Florea, Adrian Basarab, Denis Kouame, Sergiy A. Vorobyov, "Restoration of ultrasound images using spatially-variant kernel deconvolution", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2954. Accessed: Sep. 22, 2020.
@article{2954-18,
url = {http://sigport.org/2954},
author = {Mihai I. Florea; Adrian Basarab; Denis Kouame; Sergiy A. Vorobyov },
publisher = {IEEE SigPort},
title = {Restoration of ultrasound images using spatially-variant kernel deconvolution},
year = {2018} }
TY - EJOUR
T1 - Restoration of ultrasound images using spatially-variant kernel deconvolution
AU - Mihai I. Florea; Adrian Basarab; Denis Kouame; Sergiy A. Vorobyov
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2954
ER -
Mihai I. Florea, Adrian Basarab, Denis Kouame, Sergiy A. Vorobyov. (2018). Restoration of ultrasound images using spatially-variant kernel deconvolution. IEEE SigPort. http://sigport.org/2954
Mihai I. Florea, Adrian Basarab, Denis Kouame, Sergiy A. Vorobyov, 2018. Restoration of ultrasound images using spatially-variant kernel deconvolution. Available at: http://sigport.org/2954.
Mihai I. Florea, Adrian Basarab, Denis Kouame, Sergiy A. Vorobyov. (2018). "Restoration of ultrasound images using spatially-variant kernel deconvolution." Web.
1. Mihai I. Florea, Adrian Basarab, Denis Kouame, Sergiy A. Vorobyov. Restoration of ultrasound images using spatially-variant kernel deconvolution [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2954

AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING

YANIS BENANE
Denis Bujoreanu

Recent studies suggest that Resolution Enhancement Compression (REC) can provide significant improvements in terms of imaging quality over Classical Pulsed (CP) ultrasonic imaging techniques, by employing frequency and amplitude modulated transmitted signals. However the performance of coded excitations methods degrades drastically deeper into the tissue where the attenuation effects become more significant. In this work, a technique that allows overcoming the effects of attenuation on REC imaging is proposed (REC-Opt).

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Authors:
YANIS BENANE
Denis Bujoreanu
Yanis Mehdi Benane, Denis Bujoreanu, Roberto Lavarello, Christian Cachard, Olivier Basset
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13 April 2018 - 8:18am
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[1] Yanis Mehdi Benane, Denis Bujoreanu, Roberto Lavarello, Christian Cachard, Olivier Basset, "AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2477. Accessed: Sep. 22, 2020.
@article{2477-18,
url = {http://sigport.org/2477},
author = {Yanis Mehdi Benane; Denis Bujoreanu; Roberto Lavarello; Christian Cachard; Olivier Basset },
publisher = {IEEE SigPort},
title = {AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING},
year = {2018} }
TY - EJOUR
T1 - AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING
AU - Yanis Mehdi Benane; Denis Bujoreanu; Roberto Lavarello; Christian Cachard; Olivier Basset
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2477
ER -
Yanis Mehdi Benane, Denis Bujoreanu, Roberto Lavarello, Christian Cachard, Olivier Basset. (2018). AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING. IEEE SigPort. http://sigport.org/2477
Yanis Mehdi Benane, Denis Bujoreanu, Roberto Lavarello, Christian Cachard, Olivier Basset, 2018. AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING. Available at: http://sigport.org/2477.
Yanis Mehdi Benane, Denis Bujoreanu, Roberto Lavarello, Christian Cachard, Olivier Basset. (2018). "AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING." Web.
1. Yanis Mehdi Benane, Denis Bujoreanu, Roberto Lavarello, Christian Cachard, Olivier Basset. AN ATTENUATION ADAPTED PULSE COMPRESSION TECHNIQUE TO ENHANCE THE BANDWIDTH AND THE RESOLUTION USING ULTRAFAST ULTRASOUND IMAGING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2477

SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM

Ebrahim Karami

Medical research suggests that the area of the IVC and its temporal variation imaged by bedside ultrasound is useful in guiding resuscitation of the critically-ill. Unfortunately, gaps in the vessel wall and intraliminal artifact represents a challenge for both manual and existing algorithm-based segmentation techniques.

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Authors:
Ebrahim Karami
Ebrahim Karami, Mohamed Shehata, Andrew Smith
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13 November 2017 - 12:50am
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[1] Ebrahim Karami, Mohamed Shehata, Andrew Smith, "SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2330. Accessed: Sep. 22, 2020.
@article{2330-17,
url = {http://sigport.org/2330},
author = {Ebrahim Karami; Mohamed Shehata; Andrew Smith },
publisher = {IEEE SigPort},
title = {SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM},
year = {2017} }
TY - EJOUR
T1 - SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM
AU - Ebrahim Karami; Mohamed Shehata; Andrew Smith
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2330
ER -
Ebrahim Karami, Mohamed Shehata, Andrew Smith. (2017). SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM. IEEE SigPort. http://sigport.org/2330
Ebrahim Karami, Mohamed Shehata, Andrew Smith, 2017. SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM. Available at: http://sigport.org/2330.
Ebrahim Karami, Mohamed Shehata, Andrew Smith. (2017). "SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM." Web.
1. Ebrahim Karami, Mohamed Shehata, Andrew Smith. SEGMENTATION AND TRACKING OF INFERIOR VENA CAVA IN ULTRASOUND IMAGES USING A NOVEL POLAR ACTIVE CONTOUR ALGORITHM [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2330

3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES

Ola Ahmad

Analyzing the asymmetry of anatomical shapes is one of the cornerstones of efficient computerized diagnosis. In the application of scoliotic trunk analysis, one major challenge is the high variability and complexity of deformations due to the pathology itself, and to changes of body poses, for instance, torsos acquired in lateral bending poses for surgical planning. In this paper, we present a novel and fully automatic approach to analyzing the asymmetry of deformable trunk shapes.

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Authors:
Ola Ahmad
Submitted On:
15 November 2017 - 11:28am
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GlobalSIP17_Slides

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[1] , "3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2328. Accessed: Sep. 22, 2020.
@article{2328-17,
url = {http://sigport.org/2328},
author = { },
publisher = {IEEE SigPort},
title = {3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES},
year = {2017} }
TY - EJOUR
T1 - 3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2328
ER -
. (2017). 3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES. IEEE SigPort. http://sigport.org/2328
, 2017. 3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES. Available at: http://sigport.org/2328.
. (2017). "3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES." Web.
1. . 3D SHAPE ASYMMETRY ANALYSIS USING CORRESPONDENCE BETWEEN PARTIAL GEODESIC CURVES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2328

LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS

Dazhou Guo

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Dazhou Guo
Dazhou Guo, Kang Zheng, Song Wang
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14 September 2017 - 1:58pm
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ICIP 2017 poster

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[1] Dazhou Guo, Kang Zheng, Song Wang, "LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2041. Accessed: Sep. 22, 2020.
@article{2041-17,
url = {http://sigport.org/2041},
author = {Dazhou Guo; Kang Zheng; Song Wang },
publisher = {IEEE SigPort},
title = {LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS},
year = {2017} }
TY - EJOUR
T1 - LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS
AU - Dazhou Guo; Kang Zheng; Song Wang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2041
ER -
Dazhou Guo, Kang Zheng, Song Wang. (2017). LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS. IEEE SigPort. http://sigport.org/2041
Dazhou Guo, Kang Zheng, Song Wang, 2017. LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS. Available at: http://sigport.org/2041.
Dazhou Guo, Kang Zheng, Song Wang. (2017). "LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS." Web.
1. Dazhou Guo, Kang Zheng, Song Wang. LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2041

COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION

JUHYEOK MUN

We investigate the impacts of objective functions on the performance of deep-learning-based prostate magnetic resonance image segmentation. To this end, we first develop a baseline convolutional neural network (BCNN) for the prostate image segmentation, which consists of encoding, bridge, decoding, and classification modules. In the BCNN, we use 3D convolutional layers to consider volumetric information. Also, we adopt the residual feature forwarding and intermediate feature propagation techniques to make the BCNN reliably trainable for various objective functions.

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Authors:
JUHYEOK MUN
Juhyeok Mun, Won-Dong Jang, Deuk Jae Sung, Chang-Su Kim
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13 September 2017 - 10:57pm
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ICIP_JHMUN_POSTER.pdf

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[1] Juhyeok Mun, Won-Dong Jang, Deuk Jae Sung, Chang-Su Kim, "COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1991. Accessed: Sep. 22, 2020.
@article{1991-17,
url = {http://sigport.org/1991},
author = {Juhyeok Mun; Won-Dong Jang; Deuk Jae Sung; Chang-Su Kim },
publisher = {IEEE SigPort},
title = {COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION},
year = {2017} }
TY - EJOUR
T1 - COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION
AU - Juhyeok Mun; Won-Dong Jang; Deuk Jae Sung; Chang-Su Kim
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1991
ER -
Juhyeok Mun, Won-Dong Jang, Deuk Jae Sung, Chang-Su Kim. (2017). COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION. IEEE SigPort. http://sigport.org/1991
Juhyeok Mun, Won-Dong Jang, Deuk Jae Sung, Chang-Su Kim, 2017. COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION. Available at: http://sigport.org/1991.
Juhyeok Mun, Won-Dong Jang, Deuk Jae Sung, Chang-Su Kim. (2017). "COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION." Web.
1. Juhyeok Mun, Won-Dong Jang, Deuk Jae Sung, Chang-Su Kim. COMPARISON OF OBJECTIVE FUNCTIONS IN CNN-BASED PROSTATE MAGNETIC RESONANCE IMAGE SEGMENTATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1991

Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network

Shrimanti Ghosh

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Authors:
Shrimanti Ghosh
Submitted On:
11 September 2017 - 12:17pm
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[1] , "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1913. Accessed: Sep. 22, 2020.
@article{1913-17,
url = {http://sigport.org/1913},
author = { },
publisher = {IEEE SigPort},
title = {Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network},
year = {2017} }
TY - EJOUR
T1 - Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1913
ER -
. (2017). Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. IEEE SigPort. http://sigport.org/1913
, 2017. Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. Available at: http://sigport.org/1913.
. (2017). "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network." Web.
1. . Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1913

Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network

Shrimanti Ghosh

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Authors:
Shrimanti Ghosh
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11 September 2017 - 12:17pm
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[1] , "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1912. Accessed: Sep. 22, 2020.
@article{1912-17,
url = {http://sigport.org/1912},
author = { },
publisher = {IEEE SigPort},
title = {Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network},
year = {2017} }
TY - EJOUR
T1 - Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1912
ER -
. (2017). Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. IEEE SigPort. http://sigport.org/1912
, 2017. Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. Available at: http://sigport.org/1912.
. (2017). "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network." Web.
1. . Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1912

Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network

Shrimanti Ghosh

ICIP2017_poster.pdf

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Authors:
Shrimanti Ghosh
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11 September 2017 - 12:17pm
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[1] , "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1911. Accessed: Sep. 22, 2020.
@article{1911-17,
url = {http://sigport.org/1911},
author = { },
publisher = {IEEE SigPort},
title = {Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network},
year = {2017} }
TY - EJOUR
T1 - Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1911
ER -
. (2017). Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. IEEE SigPort. http://sigport.org/1911
, 2017. Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. Available at: http://sigport.org/1911.
. (2017). "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network." Web.
1. . Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1911

Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network

Shrimanti Ghosh

ICIP2017_poster.pdf

PDF icon ICIP2017_poster.pdf (450)

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Authors:
Shrimanti Ghosh
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11 September 2017 - 12:17pm
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[1] , "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1910. Accessed: Sep. 22, 2020.
@article{1910-17,
url = {http://sigport.org/1910},
author = { },
publisher = {IEEE SigPort},
title = {Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network},
year = {2017} }
TY - EJOUR
T1 - Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1910
ER -
. (2017). Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. IEEE SigPort. http://sigport.org/1910
, 2017. Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network. Available at: http://sigport.org/1910.
. (2017). "Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network." Web.
1. . Automated 3D Muscle Segmentation From MRI Data Using Convolutional Neural Network [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1910

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