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Medical image analysis

VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE


Numerous recent papers have demonstrated the utility of graph theoretical analysis in conjunction with sparse inverse covariance estimation (SICE) in understanding the modulation of brain connectivity associated with neuropathology. These concepts may complement established knowledge of functional covariance obtained using principal component analysis (PCA) that can reduce whole data representations of brain data to essential disease specific patterns.

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Authors:
Phoebe G. Spetsieris, Vijay Dhawan, David Eidelberg
Submitted On:
7 October 2018 - 11:56pm
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SSM-PCA / SICE-GLASSO PD SUBNETWORK VISUALIZATION

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[1] Phoebe G. Spetsieris, Vijay Dhawan, David Eidelberg, "VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3571. Accessed: Sep. 30, 2020.
@article{3571-18,
url = {http://sigport.org/3571},
author = {Phoebe G. Spetsieris; Vijay Dhawan; David Eidelberg },
publisher = {IEEE SigPort},
title = {VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE},
year = {2018} }
TY - EJOUR
T1 - VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE
AU - Phoebe G. Spetsieris; Vijay Dhawan; David Eidelberg
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3571
ER -
Phoebe G. Spetsieris, Vijay Dhawan, David Eidelberg. (2018). VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE. IEEE SigPort. http://sigport.org/3571
Phoebe G. Spetsieris, Vijay Dhawan, David Eidelberg, 2018. VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE. Available at: http://sigport.org/3571.
Phoebe G. Spetsieris, Vijay Dhawan, David Eidelberg. (2018). "VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE." Web.
1. Phoebe G. Spetsieris, Vijay Dhawan, David Eidelberg. VISUALIZING NETWORK CONNECTIVITY IN PARKINSON’S DISEASE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3571

Deep Networks with Shape Priors for Nucleus Detection


Detection of cell nuclei in microscopic images is a challenging research topic, because of limitations in cellular image quality and diversity of nuclear morphology, i.e. varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduring interest with promising recent success shown by deep learning methods. These methods train for example convolutional neural networks (CNNs) with a training set of input images and known, labeled nuclei locations. Many of these methods are supplemented by spatial or morphological processing.

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Authors:
Mohammad Tofighi, Tiantong Guo, Jairam K.P. Vanamala, Vishal Monga
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6 October 2018 - 1:43am
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Tofighi_SP-CNN_ICIP18

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[1] Mohammad Tofighi, Tiantong Guo, Jairam K.P. Vanamala, Vishal Monga, "Deep Networks with Shape Priors for Nucleus Detection", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3561. Accessed: Sep. 30, 2020.
@article{3561-18,
url = {http://sigport.org/3561},
author = {Mohammad Tofighi; Tiantong Guo; Jairam K.P. Vanamala; Vishal Monga },
publisher = {IEEE SigPort},
title = {Deep Networks with Shape Priors for Nucleus Detection},
year = {2018} }
TY - EJOUR
T1 - Deep Networks with Shape Priors for Nucleus Detection
AU - Mohammad Tofighi; Tiantong Guo; Jairam K.P. Vanamala; Vishal Monga
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3561
ER -
Mohammad Tofighi, Tiantong Guo, Jairam K.P. Vanamala, Vishal Monga. (2018). Deep Networks with Shape Priors for Nucleus Detection. IEEE SigPort. http://sigport.org/3561
Mohammad Tofighi, Tiantong Guo, Jairam K.P. Vanamala, Vishal Monga, 2018. Deep Networks with Shape Priors for Nucleus Detection. Available at: http://sigport.org/3561.
Mohammad Tofighi, Tiantong Guo, Jairam K.P. Vanamala, Vishal Monga. (2018). "Deep Networks with Shape Priors for Nucleus Detection." Web.
1. Mohammad Tofighi, Tiantong Guo, Jairam K.P. Vanamala, Vishal Monga. Deep Networks with Shape Priors for Nucleus Detection [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3561

DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS

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Authors:
venkateswararao cherukuri, tiantong guo, steven j schiff, vishal monga
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5 October 2018 - 4:05pm
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ICIP_VENKAT_SLIDES

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[1] venkateswararao cherukuri, tiantong guo, steven j schiff, vishal monga, "DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3554. Accessed: Sep. 30, 2020.
@article{3554-18,
url = {http://sigport.org/3554},
author = {venkateswararao cherukuri; tiantong guo; steven j schiff; vishal monga },
publisher = {IEEE SigPort},
title = {DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS},
year = {2018} }
TY - EJOUR
T1 - DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS
AU - venkateswararao cherukuri; tiantong guo; steven j schiff; vishal monga
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3554
ER -
venkateswararao cherukuri, tiantong guo, steven j schiff, vishal monga. (2018). DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS. IEEE SigPort. http://sigport.org/3554
venkateswararao cherukuri, tiantong guo, steven j schiff, vishal monga, 2018. DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS. Available at: http://sigport.org/3554.
venkateswararao cherukuri, tiantong guo, steven j schiff, vishal monga. (2018). "DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS." Web.
1. venkateswararao cherukuri, tiantong guo, steven j schiff, vishal monga. DEEP MR BRAIN IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3554

MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS


We propose a new multiclass weighted loss function for instance segmentation of cluttered cells. We are primarily motivated by the need of developmental biologists to quantify and model the behavior of blood T-cells which might help us in understanding their regulation mechanisms and ultimately help researchers in their quest for developing an effective immunotherapy cancer treatment.

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Authors:
Fidel A. Guerrero Peña, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, Alexandre Cunha
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5 October 2018 - 8:22am
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Poster ICIP

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[1] Fidel A. Guerrero Peña, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, Alexandre Cunha, "MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3538. Accessed: Sep. 30, 2020.
@article{3538-18,
url = {http://sigport.org/3538},
author = {Fidel A. Guerrero Peña; Pedro D. Marrero Fernandez; Tsang Ing Ren; Mary Yui; Ellen Rothenberg; Alexandre Cunha },
publisher = {IEEE SigPort},
title = {MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS},
year = {2018} }
TY - EJOUR
T1 - MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS
AU - Fidel A. Guerrero Peña; Pedro D. Marrero Fernandez; Tsang Ing Ren; Mary Yui; Ellen Rothenberg; Alexandre Cunha
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3538
ER -
Fidel A. Guerrero Peña, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, Alexandre Cunha. (2018). MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS. IEEE SigPort. http://sigport.org/3538
Fidel A. Guerrero Peña, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, Alexandre Cunha, 2018. MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS. Available at: http://sigport.org/3538.
Fidel A. Guerrero Peña, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, Alexandre Cunha. (2018). "MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS." Web.
1. Fidel A. Guerrero Peña, Pedro D. Marrero Fernandez, Tsang Ing Ren, Mary Yui, Ellen Rothenberg, Alexandre Cunha. MULTICLASS WEIGHTED LOSS FOR INSTANCE SEGMENTATION OF CLUTTERED CELLS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3538

CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING


We present a region based method for segmenting and splitting
images of cells in an automatic and unsupervised manner.
The detection of cell nuclei is based on the Bradley’s method.
False positives are automatically identified and rejected based
on shape and intensity features. Additionally, the proposed
method is able to automatically detect and split touching cells.
To do so, we employ a variant of a region based multi-ellipse
fitting method (DEFA) that makes use of constraints on the

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Authors:
Costas Panagiotakis, Antonis A. Argyros
Submitted On:
5 October 2018 - 3:50am
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CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING

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[1] Costas Panagiotakis, Antonis A. Argyros, "CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3514. Accessed: Sep. 30, 2020.
@article{3514-18,
url = {http://sigport.org/3514},
author = {Costas Panagiotakis; Antonis A. Argyros },
publisher = {IEEE SigPort},
title = {CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING},
year = {2018} }
TY - EJOUR
T1 - CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING
AU - Costas Panagiotakis; Antonis A. Argyros
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3514
ER -
Costas Panagiotakis, Antonis A. Argyros. (2018). CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING. IEEE SigPort. http://sigport.org/3514
Costas Panagiotakis, Antonis A. Argyros, 2018. CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING. Available at: http://sigport.org/3514.
Costas Panagiotakis, Antonis A. Argyros. (2018). "CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING." Web.
1. Costas Panagiotakis, Antonis A. Argyros. CELL SEGMENTATION VIA REGION-BASED ELLIPSE FITTING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3514

LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES


Retinal vessel information is helpful in retinal disease screening and diagnosis. Retinal vessel segmentation provides useful information about vessels and can be used by physicians during intraocular surgery and retinal diagnostic operations. Convolutional neural networks (CNNs) are powerful tools for classification and segmentation of medical images. However, complexity of CNNs makes it difficult to implement them in portable devices such as binocular indirect ophthalmoscopes. In this paper a simplification approach is proposed for CNNs based on combination of quantization and pruning.

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Authors:
Mohsen Hajabdollahi, Reza Esfandiarpoor, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr
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4 October 2018 - 4:50pm
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Poster

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[1] Mohsen Hajabdollahi, Reza Esfandiarpoor, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr, "LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3465. Accessed: Sep. 30, 2020.
@article{3465-18,
url = {http://sigport.org/3465},
author = {Mohsen Hajabdollahi; Reza Esfandiarpoor; Kayvan Najarian; Nader Karimi; Shadrokh Samavi; S.M.Reza Soroushmehr },
publisher = {IEEE SigPort},
title = {LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES},
year = {2018} }
TY - EJOUR
T1 - LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES
AU - Mohsen Hajabdollahi; Reza Esfandiarpoor; Kayvan Najarian; Nader Karimi; Shadrokh Samavi; S.M.Reza Soroushmehr
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3465
ER -
Mohsen Hajabdollahi, Reza Esfandiarpoor, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr. (2018). LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES. IEEE SigPort. http://sigport.org/3465
Mohsen Hajabdollahi, Reza Esfandiarpoor, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr, 2018. LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES. Available at: http://sigport.org/3465.
Mohsen Hajabdollahi, Reza Esfandiarpoor, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr. (2018). "LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES." Web.
1. Mohsen Hajabdollahi, Reza Esfandiarpoor, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr. LOW COMPLEXITY CONVOLUTIONAL NEURAL NETWORK FOR VESSEL SEGMENTATION IN PORTABLE RETINAL DIAGNOSTIC DEVICES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3465

ADAPTIVE SPECULAR REFLECTION DETECTION AND INPAINTING IN COLONOSCOPY VIDEO FRAMES


Colonoscopy video frames might be contaminated by bright spots with unsaturated values known as specular reflection. Detection and removal of such reflections could enhance the quality of colonoscopy images and facilitate diagnosis procedure. In this paper, we propose a novel two-phase method for this purpose, consisting of detection and removal phases. In the detection phase, we employ both HSV and RGB color space information for segmentation of specular reflections.

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Authors:
Mojtaba Akbari, Majid Mohrekesh, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr
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4 October 2018 - 4:50pm
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Poster-ReflectionNoise.pdf

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[1] Mojtaba Akbari, Majid Mohrekesh, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr, "ADAPTIVE SPECULAR REFLECTION DETECTION AND INPAINTING IN COLONOSCOPY VIDEO FRAMES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3464. Accessed: Sep. 30, 2020.
@article{3464-18,
url = {http://sigport.org/3464},
author = {Mojtaba Akbari; Majid Mohrekesh; Kayvan Najarian; Nader Karimi; Shadrokh Samavi; S.M.Reza Soroushmehr },
publisher = {IEEE SigPort},
title = {ADAPTIVE SPECULAR REFLECTION DETECTION AND INPAINTING IN COLONOSCOPY VIDEO FRAMES},
year = {2018} }
TY - EJOUR
T1 - ADAPTIVE SPECULAR REFLECTION DETECTION AND INPAINTING IN COLONOSCOPY VIDEO FRAMES
AU - Mojtaba Akbari; Majid Mohrekesh; Kayvan Najarian; Nader Karimi; Shadrokh Samavi; S.M.Reza Soroushmehr
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3464
ER -
Mojtaba Akbari, Majid Mohrekesh, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr. (2018). ADAPTIVE SPECULAR REFLECTION DETECTION AND INPAINTING IN COLONOSCOPY VIDEO FRAMES. IEEE SigPort. http://sigport.org/3464
Mojtaba Akbari, Majid Mohrekesh, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr, 2018. ADAPTIVE SPECULAR REFLECTION DETECTION AND INPAINTING IN COLONOSCOPY VIDEO FRAMES. Available at: http://sigport.org/3464.
Mojtaba Akbari, Majid Mohrekesh, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr. (2018). "ADAPTIVE SPECULAR REFLECTION DETECTION AND INPAINTING IN COLONOSCOPY VIDEO FRAMES." Web.
1. Mojtaba Akbari, Majid Mohrekesh, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr. ADAPTIVE SPECULAR REFLECTION DETECTION AND INPAINTING IN COLONOSCOPY VIDEO FRAMES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3464

LIVER SEGMENTATION IN CT IMAGES USING THREE DIMENSIONAL TO TWO DIMENSIONAL FULLY CONVOLUTIONAL NETWORK


The need for CT scan analysis is growing for diagnosis and therapy of abdominal organs. Automatic organ segmentation of abdominal CT scan can help radiologists analyze the scans faster, and diagnose disease and injury more accurately. However, existing methods are not efficient enough to perform the segmentation process for victims of accidents and emergency situations. In this paper, we propose an efficient liver segmentation with our 3D to 2D fully convolution network (3D-2D-FCN). The segmented mask is enhanced using the conditional random field on the organ’s border.

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Authors:
Shima Rafiei, Ebrahim Nasr-Esfahani, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr
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4 October 2018 - 4:50pm
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Poster

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[1] Shima Rafiei, Ebrahim Nasr-Esfahani, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr, "LIVER SEGMENTATION IN CT IMAGES USING THREE DIMENSIONAL TO TWO DIMENSIONAL FULLY CONVOLUTIONAL NETWORK", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3463. Accessed: Sep. 30, 2020.
@article{3463-18,
url = {http://sigport.org/3463},
author = {Shima Rafiei; Ebrahim Nasr-Esfahani; Kayvan Najarian; Nader Karimi; Shadrokh Samavi; S.M.Reza Soroushmehr },
publisher = {IEEE SigPort},
title = {LIVER SEGMENTATION IN CT IMAGES USING THREE DIMENSIONAL TO TWO DIMENSIONAL FULLY CONVOLUTIONAL NETWORK},
year = {2018} }
TY - EJOUR
T1 - LIVER SEGMENTATION IN CT IMAGES USING THREE DIMENSIONAL TO TWO DIMENSIONAL FULLY CONVOLUTIONAL NETWORK
AU - Shima Rafiei; Ebrahim Nasr-Esfahani; Kayvan Najarian; Nader Karimi; Shadrokh Samavi; S.M.Reza Soroushmehr
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3463
ER -
Shima Rafiei, Ebrahim Nasr-Esfahani, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr. (2018). LIVER SEGMENTATION IN CT IMAGES USING THREE DIMENSIONAL TO TWO DIMENSIONAL FULLY CONVOLUTIONAL NETWORK. IEEE SigPort. http://sigport.org/3463
Shima Rafiei, Ebrahim Nasr-Esfahani, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr, 2018. LIVER SEGMENTATION IN CT IMAGES USING THREE DIMENSIONAL TO TWO DIMENSIONAL FULLY CONVOLUTIONAL NETWORK. Available at: http://sigport.org/3463.
Shima Rafiei, Ebrahim Nasr-Esfahani, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr. (2018). "LIVER SEGMENTATION IN CT IMAGES USING THREE DIMENSIONAL TO TWO DIMENSIONAL FULLY CONVOLUTIONAL NETWORK." Web.
1. Shima Rafiei, Ebrahim Nasr-Esfahani, Kayvan Najarian, Nader Karimi, Shadrokh Samavi, S.M.Reza Soroushmehr. LIVER SEGMENTATION IN CT IMAGES USING THREE DIMENSIONAL TO TWO DIMENSIONAL FULLY CONVOLUTIONAL NETWORK [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3463

AUTOMATIC 3-D SKELETON-BASED SEGMENTATION OF LIVER VESSELS FROM MRI AND CT FOR COUINAUD REPRESENTATION

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4 October 2018 - 12:20pm
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ICIP_2018_lebre.pdf

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[1] , "AUTOMATIC 3-D SKELETON-BASED SEGMENTATION OF LIVER VESSELS FROM MRI AND CT FOR COUINAUD REPRESENTATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3449. Accessed: Sep. 30, 2020.
@article{3449-18,
url = {http://sigport.org/3449},
author = { },
publisher = {IEEE SigPort},
title = {AUTOMATIC 3-D SKELETON-BASED SEGMENTATION OF LIVER VESSELS FROM MRI AND CT FOR COUINAUD REPRESENTATION},
year = {2018} }
TY - EJOUR
T1 - AUTOMATIC 3-D SKELETON-BASED SEGMENTATION OF LIVER VESSELS FROM MRI AND CT FOR COUINAUD REPRESENTATION
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3449
ER -
. (2018). AUTOMATIC 3-D SKELETON-BASED SEGMENTATION OF LIVER VESSELS FROM MRI AND CT FOR COUINAUD REPRESENTATION. IEEE SigPort. http://sigport.org/3449
, 2018. AUTOMATIC 3-D SKELETON-BASED SEGMENTATION OF LIVER VESSELS FROM MRI AND CT FOR COUINAUD REPRESENTATION. Available at: http://sigport.org/3449.
. (2018). "AUTOMATIC 3-D SKELETON-BASED SEGMENTATION OF LIVER VESSELS FROM MRI AND CT FOR COUINAUD REPRESENTATION." Web.
1. . AUTOMATIC 3-D SKELETON-BASED SEGMENTATION OF LIVER VESSELS FROM MRI AND CT FOR COUINAUD REPRESENTATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3449

Multimodal Image Registration through Simultaneous Segmentation


Multimodal image registration facilitates the combination of complementary information from images acquired with different modalities. Most existing methods require computation of the joint histogram of the images, while some perform joint segmentation and registration in alternate iterations. In this work, we introduce a new non-information-theoretical method for pairwise multimodal image registration, in which the error of segmentation – using both images – is considered as the registration cost function.

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Authors:
Iman Aganj, Bruce Fischl
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4 October 2018 - 10:23am
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MGH-SAC-SBReg_iman_poster.pptx

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[1] Iman Aganj, Bruce Fischl, "Multimodal Image Registration through Simultaneous Segmentation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3423. Accessed: Sep. 30, 2020.
@article{3423-18,
url = {http://sigport.org/3423},
author = {Iman Aganj; Bruce Fischl },
publisher = {IEEE SigPort},
title = {Multimodal Image Registration through Simultaneous Segmentation},
year = {2018} }
TY - EJOUR
T1 - Multimodal Image Registration through Simultaneous Segmentation
AU - Iman Aganj; Bruce Fischl
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3423
ER -
Iman Aganj, Bruce Fischl. (2018). Multimodal Image Registration through Simultaneous Segmentation. IEEE SigPort. http://sigport.org/3423
Iman Aganj, Bruce Fischl, 2018. Multimodal Image Registration through Simultaneous Segmentation. Available at: http://sigport.org/3423.
Iman Aganj, Bruce Fischl. (2018). "Multimodal Image Registration through Simultaneous Segmentation." Web.
1. Iman Aganj, Bruce Fischl. Multimodal Image Registration through Simultaneous Segmentation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3423

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