Sorry, you need to enable JavaScript to visit this website.

Medical imaging

Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy

Paper Details

Authors:
Hang Xu, Shijie Geng, Yu Qiao, Kuan Xu, Yueyang Gu
Submitted On:
21 May 2020 - 11:00pm
Short Link:
Type:
Event:
Paper Code:
Document Year:
Cite

Document Files

Paper 2748 ICASSP 2020.pdf

(9)

Keywords

Subscribe

[1] Hang Xu, Shijie Geng, Yu Qiao, Kuan Xu, Yueyang Gu, "Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5429. Accessed: Jun. 07, 2020.
@article{5429-20,
url = {http://sigport.org/5429},
author = {Hang Xu; Shijie Geng; Yu Qiao; Kuan Xu; Yueyang Gu },
publisher = {IEEE SigPort},
title = {Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy},
year = {2020} }
TY - EJOUR
T1 - Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy
AU - Hang Xu; Shijie Geng; Yu Qiao; Kuan Xu; Yueyang Gu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5429
ER -
Hang Xu, Shijie Geng, Yu Qiao, Kuan Xu, Yueyang Gu. (2020). Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy. IEEE SigPort. http://sigport.org/5429
Hang Xu, Shijie Geng, Yu Qiao, Kuan Xu, Yueyang Gu, 2020. Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy. Available at: http://sigport.org/5429.
Hang Xu, Shijie Geng, Yu Qiao, Kuan Xu, Yueyang Gu. (2020). "Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy." Web.
1. Hang Xu, Shijie Geng, Yu Qiao, Kuan Xu, Yueyang Gu. Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5429

Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System


Conventional Computed Tomography (CT) systems use a single X-ray source and an arc of detectors mounted on a rotating gantry to acquire a set of projection data. Novel CT systems are now being pioneered in which a complete ring of distributed X-ray sources and detectors are electronically turned on and off, without any mechanical motion, to acquire a set of projections for tomographic reconstruction. This paper discusses new sensing and reconstruction paradigms enabled by this new CT architecture.

Paper Details

Authors:
Alankar Kowtal, Avilash Cramer, Dufan Wu, Kai Yang, Wolfgang Krull, Ioannis Gkioulekas, Rajiv Gupta
Submitted On:
15 May 2020 - 10:05am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

PDF of presentation

(10)

Subscribe

[1] Alankar Kowtal, Avilash Cramer, Dufan Wu, Kai Yang, Wolfgang Krull, Ioannis Gkioulekas, Rajiv Gupta, "Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5348. Accessed: Jun. 07, 2020.
@article{5348-20,
url = {http://sigport.org/5348},
author = {Alankar Kowtal; Avilash Cramer; Dufan Wu; Kai Yang; Wolfgang Krull; Ioannis Gkioulekas; Rajiv Gupta },
publisher = {IEEE SigPort},
title = {Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System},
year = {2020} }
TY - EJOUR
T1 - Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System
AU - Alankar Kowtal; Avilash Cramer; Dufan Wu; Kai Yang; Wolfgang Krull; Ioannis Gkioulekas; Rajiv Gupta
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5348
ER -
Alankar Kowtal, Avilash Cramer, Dufan Wu, Kai Yang, Wolfgang Krull, Ioannis Gkioulekas, Rajiv Gupta. (2020). Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System. IEEE SigPort. http://sigport.org/5348
Alankar Kowtal, Avilash Cramer, Dufan Wu, Kai Yang, Wolfgang Krull, Ioannis Gkioulekas, Rajiv Gupta, 2020. Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System. Available at: http://sigport.org/5348.
Alankar Kowtal, Avilash Cramer, Dufan Wu, Kai Yang, Wolfgang Krull, Ioannis Gkioulekas, Rajiv Gupta. (2020). "Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System." Web.
1. Alankar Kowtal, Avilash Cramer, Dufan Wu, Kai Yang, Wolfgang Krull, Ioannis Gkioulekas, Rajiv Gupta. Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5348

K-SPACE TRAJECTORY DESIGN FOR REDUCED MRI SCAN TIME


The development of compressed sensing (CS) techniques for magnetic resonance imaging (MRI) is enabling a speedup of MRI scanning. To increase the incoherence in the sampling, a random selection of points on the k-space is deployed and a continuous trajectory is obtained by solving a traveling salesman problem (TSP) through these points. A feasible trajectory satisfying the gradient constraints is then obtained by parameterizing it using state-of-the-art methods. In this paper, a constrained convex optimization based method to obtain feasible trajectories is proposed.

Paper Details

Authors:
Shubham Sharma, K.V.S. Hari, Geert Leus
Submitted On:
14 May 2020 - 3:39am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

K-SPACE TRAJECTORY DESIGN FOR REDUCED MRI SCAN TIME

(17)

Keywords

Subscribe

[1] Shubham Sharma, K.V.S. Hari, Geert Leus, "K-SPACE TRAJECTORY DESIGN FOR REDUCED MRI SCAN TIME", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5255. Accessed: Jun. 07, 2020.
@article{5255-20,
url = {http://sigport.org/5255},
author = {Shubham Sharma; K.V.S. Hari; Geert Leus },
publisher = {IEEE SigPort},
title = {K-SPACE TRAJECTORY DESIGN FOR REDUCED MRI SCAN TIME},
year = {2020} }
TY - EJOUR
T1 - K-SPACE TRAJECTORY DESIGN FOR REDUCED MRI SCAN TIME
AU - Shubham Sharma; K.V.S. Hari; Geert Leus
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5255
ER -
Shubham Sharma, K.V.S. Hari, Geert Leus. (2020). K-SPACE TRAJECTORY DESIGN FOR REDUCED MRI SCAN TIME. IEEE SigPort. http://sigport.org/5255
Shubham Sharma, K.V.S. Hari, Geert Leus, 2020. K-SPACE TRAJECTORY DESIGN FOR REDUCED MRI SCAN TIME. Available at: http://sigport.org/5255.
Shubham Sharma, K.V.S. Hari, Geert Leus. (2020). "K-SPACE TRAJECTORY DESIGN FOR REDUCED MRI SCAN TIME." Web.
1. Shubham Sharma, K.V.S. Hari, Geert Leus. K-SPACE TRAJECTORY DESIGN FOR REDUCED MRI SCAN TIME [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5255

SPACE FILLING CURVES FOR MRI SAMPLING


A novel class of k-space trajectories for magnetic resonance imaging (MRI) sampling using space-filling curves (SFCs) is presented here. More specifically, Peano, Hilbert and Sierpinski curves are used. We propose 1-shot and 4-shot variable density SFCs by utilizing the space coverage provided by SFCs in different iterations. The proposed trajectories are compared with state-of-the-art echo-planar imaging (EPI) trajectories for 128 × 128 and 256 × 256 phantom and brain images.

Paper Details

Authors:
Shubham Sharma, K.V.S. Hari, Geert Leus
Submitted On:
14 May 2020 - 3:34am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

SPACE FILLING CURVES FOR MRI SAMPLING

(8)

Keywords

Subscribe

[1] Shubham Sharma, K.V.S. Hari, Geert Leus, "SPACE FILLING CURVES FOR MRI SAMPLING", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5252. Accessed: Jun. 07, 2020.
@article{5252-20,
url = {http://sigport.org/5252},
author = {Shubham Sharma; K.V.S. Hari; Geert Leus },
publisher = {IEEE SigPort},
title = {SPACE FILLING CURVES FOR MRI SAMPLING},
year = {2020} }
TY - EJOUR
T1 - SPACE FILLING CURVES FOR MRI SAMPLING
AU - Shubham Sharma; K.V.S. Hari; Geert Leus
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5252
ER -
Shubham Sharma, K.V.S. Hari, Geert Leus. (2020). SPACE FILLING CURVES FOR MRI SAMPLING. IEEE SigPort. http://sigport.org/5252
Shubham Sharma, K.V.S. Hari, Geert Leus, 2020. SPACE FILLING CURVES FOR MRI SAMPLING. Available at: http://sigport.org/5252.
Shubham Sharma, K.V.S. Hari, Geert Leus. (2020). "SPACE FILLING CURVES FOR MRI SAMPLING." Web.
1. Shubham Sharma, K.V.S. Hari, Geert Leus. SPACE FILLING CURVES FOR MRI SAMPLING [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5252

Generation of head models for brain stimulation using deep convolution networks


Transcranial magnetic stimulation (TMS) is a non-invasive clinical technique used for treatment of several neurological diseases such as depression, Alzheimer’s disease and Parkinson’s disease. However, it is always challenging to accurately adjust the electric field on different specific brain regions due to the requirement of several stimulation parameters’ optimizations.

Paper Details

Authors:
Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata
Submitted On:
18 September 2019 - 12:31am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICIP2019_poster_2190.pdf

(62)

Subscribe

[1] Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata, "Generation of head models for brain stimulation using deep convolution networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4663. Accessed: Jun. 07, 2020.
@article{4663-19,
url = {http://sigport.org/4663},
author = {Essam A. Rashed; Jose Gomez-Tames; Akimasa Hirata },
publisher = {IEEE SigPort},
title = {Generation of head models for brain stimulation using deep convolution networks},
year = {2019} }
TY - EJOUR
T1 - Generation of head models for brain stimulation using deep convolution networks
AU - Essam A. Rashed; Jose Gomez-Tames; Akimasa Hirata
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4663
ER -
Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata. (2019). Generation of head models for brain stimulation using deep convolution networks. IEEE SigPort. http://sigport.org/4663
Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata, 2019. Generation of head models for brain stimulation using deep convolution networks. Available at: http://sigport.org/4663.
Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata. (2019). "Generation of head models for brain stimulation using deep convolution networks." Web.
1. Essam A. Rashed, Jose Gomez-Tames, Akimasa Hirata. Generation of head models for brain stimulation using deep convolution networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4663

COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS

Paper Details

Authors:
Submitted On:
10 September 2019 - 9:57pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

2019ICIP-PosterPresentation.pdf

(47)

Subscribe

[1] , "COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4580. Accessed: Jun. 07, 2020.
@article{4580-19,
url = {http://sigport.org/4580},
author = { },
publisher = {IEEE SigPort},
title = {COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS},
year = {2019} }
TY - EJOUR
T1 - COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4580
ER -
. (2019). COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS. IEEE SigPort. http://sigport.org/4580
, 2019. COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS. Available at: http://sigport.org/4580.
. (2019). "COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS." Web.
1. . COMPRESSED SENSING MRI WITH JOINT IMAGE-LEVEL AND PATCH-LEVEL PRIORS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4580

ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES

Paper Details

Authors:
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang
Submitted On:
12 May 2019 - 4:02pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster_icassp2019.pdf

(93)

Keywords

Subscribe

[1] Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang, "ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4470. Accessed: Jun. 07, 2020.
@article{4470-19,
url = {http://sigport.org/4470},
author = {Karim Armanious; Youssef Mecky; Sergios Gatidis; Bin Yang },
publisher = {IEEE SigPort},
title = {ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES},
year = {2019} }
TY - EJOUR
T1 - ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES
AU - Karim Armanious; Youssef Mecky; Sergios Gatidis; Bin Yang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4470
ER -
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang. (2019). ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES. IEEE SigPort. http://sigport.org/4470
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang, 2019. ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES. Available at: http://sigport.org/4470.
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang. (2019). "ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES." Web.
1. Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang. ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4470

Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition

Paper Details

Authors:
Zhipeng Li, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler
Submitted On:
21 December 2018 - 3:48pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

GlobalSIP2018_DECTMULTRA_Slides.pdf

(19)

Keywords

Subscribe

[1] Zhipeng Li, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler, "Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3705. Accessed: Jun. 07, 2020.
@article{3705-18,
url = {http://sigport.org/3705},
author = {Zhipeng Li; Saiprasad Ravishankar; Yong Long; Jeffrey A. Fessler },
publisher = {IEEE SigPort},
title = {Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition},
year = {2018} }
TY - EJOUR
T1 - Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition
AU - Zhipeng Li; Saiprasad Ravishankar; Yong Long; Jeffrey A. Fessler
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3705
ER -
Zhipeng Li, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler. (2018). Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition. IEEE SigPort. http://sigport.org/3705
Zhipeng Li, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler, 2018. Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition. Available at: http://sigport.org/3705.
Zhipeng Li, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler. (2018). "Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition." Web.
1. Zhipeng Li, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler. Learned Mixed Material Models for Efficient Clustering Based Dual-Energy CT Image Decomposition [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3705

BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training


One of the main difficulties in the use of deep learning strategies in medical contexts is the training set size. While these methods need large annotated training sets, this data is costly to obtain in medical contexts and suffers from intra and iter-subject variability.

In the present work, two new pre-processing techniques are introduced to improve a classifier performance. First, data augmentation based on co-registration is suggested. Then, multi-scale enhancement based on Difference of Gaussians is proposed.

Paper Details

Authors:
Inês Domingues, Pedro H. Abreu, João Santos
Submitted On:
4 October 2018 - 12:24pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster_A0.pdf

(122)

Keywords

Subscribe

[1] Inês Domingues, Pedro H. Abreu, João Santos, "BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3452. Accessed: Jun. 07, 2020.
@article{3452-18,
url = {http://sigport.org/3452},
author = {Inês Domingues; Pedro H. Abreu; João Santos },
publisher = {IEEE SigPort},
title = {BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training},
year = {2018} }
TY - EJOUR
T1 - BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training
AU - Inês Domingues; Pedro H. Abreu; João Santos
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3452
ER -
Inês Domingues, Pedro H. Abreu, João Santos. (2018). BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training. IEEE SigPort. http://sigport.org/3452
Inês Domingues, Pedro H. Abreu, João Santos, 2018. BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training. Available at: http://sigport.org/3452.
Inês Domingues, Pedro H. Abreu, João Santos. (2018). "BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training." Web.
1. Inês Domingues, Pedro H. Abreu, João Santos. BI-RADS classification of breat cancer: a new pre-processing pipeline for deep models training [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3452

Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework


Model-Based Iterative Reconstruction (MBIR) has shown promising results in clinical studies as they allow significant

Paper Details

Authors:
Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman
Submitted On:
19 April 2018 - 7:12pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP_DongHyeYe.pdf

(204)

Keywords

Additional Categories

Subscribe

[1] Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman, "Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3038. Accessed: Jun. 07, 2020.
@article{3038-18,
url = {http://sigport.org/3038},
author = {Dong Hye Ye; Somesh Srivastava; Jean-Baptiste Thibault; Ken Sauer; Charles Bouman },
publisher = {IEEE SigPort},
title = {Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework},
year = {2018} }
TY - EJOUR
T1 - Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework
AU - Dong Hye Ye; Somesh Srivastava; Jean-Baptiste Thibault; Ken Sauer; Charles Bouman
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3038
ER -
Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman. (2018). Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework. IEEE SigPort. http://sigport.org/3038
Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman, 2018. Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework. Available at: http://sigport.org/3038.
Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman. (2018). "Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework." Web.
1. Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman. Deep Residual Learning for Model-Based Iterative CT Reconstruction using Plug-and-Play Framework [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3038

Pages