 |
||
| T-RPMS Home | Editorial Board | T-RPMS in IEEE Xplore | Early Access | Manuscript Submission | ||
| FEATURED STORIES - MARCH 2019 | ||
Machine (Deep) Learning Methods for Image Processing and Radiomicsby Mathieu Hatt, Chintan Parmar, Jinyi Qi, Issam El Naqa Methods from the field of machine (deep) learning have been successful in tackling a number of tasks in medical imaging, from image reconstruction or processing to predictive modeling, clinical planning and decision-aid systems. The ever growing availability of data and the improving ability of algorithms to learn from them has led to the rise of methods based on neural networks to address most of these tasks with higher efficiency and often superior performance than previous, “shallow” machine learning methods. The present editorial aims at contextualizing within this framework the recent developments of these techniques, including these described in the papers published in the present special issue on machine (deep) learning for image processing and radiomics in radiation-based medical sciences. more... |
||
 |
||
Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstructionby Hoyeon Lee, Jongha Lee, Hyeongseok Kim, Byungchul Cho, and Seungryong Cho Recently, a number of approaches to low-dose computed tomography (CT) have been developed and deployed in commercialized CT scanners. Tube current reduction is perhaps the most actively explored technology with advanced image reconstruction algorithms. Sparse data sampling is another viable option to the low-dose CT, and sparse-view CT has been particularly of interest among the researchers in CT community. Since analytic image reconstruction algorithms would lead to severe image artifacts, various iterative algorithms have been developed for reconstructing images from sparsely view-sampled projection data. However, iterative algorithms take much longer computation time than the analytic algorithms, and images are usually prone to different types of image artifacts that heavily depend on the reconstruction parameters. more... |
||
|
||
PET Image Denoising Using a Deep Neural Network Through Fine Tuningby Kuang Gong, Jiahui Guan, Chih-Chieh Liu, and Jinyi Qi Positron emission tomography (PET) is a functional imaging modality widely used in clinical diagnosis. In this paper, we trained a deep convolutional neural network to improve PET image quality. Perceptual loss based on features derived from a pretrained VGG network, instead of the conventional mean squared error, was employed as the training loss function to preserve image details. more... |
||
|
||
Deep Learning-Based Image Segmentation on Multimodal Medical Imagingby Zhe Guo, Xiang Li, Heng Huang, Ning Guo, and Quanzheng Li Multimodality medical imaging techniques have been increasingly applied in clinical practice and research studies. Corresponding multimodal image analysis and ensemble learning schemes have seen rapid growth and bring unique value to medical applications. Motivated by the recent success of applying deep learning methods to medical image processing, we first propose an algorithmic architecture for supervised multimodal image analysis with cross-modality fusion at the feature learning level, classifier level, and decision-making level. more... |
||
|
||
Use of Generative Disease Models for Analysis and Selection of Radiomic Features in PETby Ivan S. Klyuzhin, Jessie F. Fu, Nikolay Shenkov, Arman Rahmim, and Vesna Sossi Radiomic positron emission tomography (PET) image features are increasingly used in conjunction with machine learning to predict clinical disease measures. However, a thorough understanding of these image features remains challenging due to their relatively high complexity, hampering a-priori selection of optimal features and model parameters for a predictive task. In this paper, we explore the use of a generative disease model (GDM) for feature analysis. more... |
||
|
||
Creating Robust Predictive Radiomic Models for Data From Independent Institutions Using Normalizationby Avishek Chatterjee, Martin Vallières, Anthony Dohan, Ives R. Levesque, Yoshiko Ueno, Sameh Saif, Caroline Reinhold, and Jan Seuntjens The distribution of a radiomic feature can differ between two institutions due to, for example, different image acquisition parameters, imaging systems, and contouring (i.e., tumor delineation) variations between clinicians. We aimed to develop effective statistical methods to successfully apply a radiomics-based predictive model to an external dataset. Theory: Two common feature normalization methods, rescaling and standardization, were evaluated for suitability in reducing feature variability between institutions. Standardization was chosen as the preferred approach, since rescaling was more sensitive to statistical outliers, and potentially reduced the discrimination power of a feature. more... |
||
|
||
Artificial Neural Network With Composite Architectures for Prediction of Local Control in Radiotherapyby Sunan Cui, Yi Luo, Huan-Hsin Tseng, Randall K. Ten Haken, and Issam El Naqa In this paper, we investigated the application of artificial neural networks with composite architectures into the prediction of local control (LC) of lung cancer patients after radiotherapy. The motivation of this paper was to take advantage of the temporal associations among longitudinal (sequential) data to improve the predictive performance of outcome models under the circumstance of limited sample sizes. Two composite architectures: 1) a 1-D convolutional + fully connected and 2) a locally connected + fully connected architectures were implemented for this purpose. more... |
||
|
||
  |
||
A PUBLICATION OF THE IEEE NUCLEAR AND PLASMA SCIENCES SOCIETY |
||
| MARCH 2019 | VOLUME 3 | NUMBER 2 | ITRPFI | (SSN 2469-7311) | ||
| SPECIAL ISSUE ON MACHINE LEARNING FOR IMAGE PROCESSING AND RADIOMICS EDITORIAL Machine (Deep) Learning Methods for Image Processing and Radiomics . . . . . . . . . . . . . . . . . . . . M. Hatt, C. Parmar, J. Qi, and I. El Naqa SPECIAL ISSUE PAPERS Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Lee, J. Lee, H. Kim, B. Cho, and S. Cho Deep Learning-Based Super-Resolution Applied to Dental Computed Tomography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Hatvani, A. Horváth, J. Michetti, A. Basarab, D. Kouamé, and M. Gyöngy Investigation of Semi-Coupled Dictionary Learning in 3-D Super Resolution HR-pQCT Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Y. Li, B. Sixou, A. Burghard, and F. Peyrin Convolutional Neural Network-Based Robust Denoising of Low-Dose Computed Tomography Perfusion Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. S. Kadimesetty, S. Gutta, S. Ganapathy, and P. K. Yalavarthy PET Image Denoising Using a Deep Neural Network Through Fine Tuning . . . . . . . . . . . . . . . . . . . K. Gong, J. Guan, C.-C. Liu, and J. Qi Deep Learning-Based Image Segmentation on Multimodal Medical Imaging . . . . . . . . . . . . . . Z. Guo, X. Li, H. Huang, N. Guo, and Q. Li Machine Learning Based Classification Using Clinical and DaTSCAN SPECT Imaging Features: A Study on Parkinson’s Disease and SWEDD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Mabrouk, B. Chikhaoui, and L. Bentabet Use of Generative Disease Models for Analysis and Selection of Radiomic Features in PET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. S. Klyuzhin, J. F. Fu, N. Shenkov, A. Rahmim, and V. Sossi Comparison of Radiomics Models Built Through Machine Learning in a Multicentric Context With Independent Testing: Identical Data, Similar Algorithms, Different Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. Upadhaya, M. Vallières, A. Chatterjee, F. Lucia, P. A. Bonaffini, I. Masson, A. Mervoyer, C. Reinhold, U. Schick, J. Seuntjens, C. Cheze Le Rest, D. Visvikis, and M. Hatt An Empirical Approach for Avoiding False Discoveries When Applying High-Dimensional Radiomics to Small Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Chatterjee, M. Vallières, A. Dohan, I. R. Levesque, Y. Ueno, V. Bist, S. Saif, C. Reinhold, and J. Seuntjens Creating Robust Predictive Radiomic Models for Data From Independent Institutions Using Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Chatterjee, M. Vallières, A. Dohan, I. R. Levesque, Y. Ueno, S. Saif, C. Reinhold, and J. Seuntjens |
||
|
Home |
Contact & Support |
Accessibility |
Nondiscrimination Policy |
Feedback |
Privacy and Opting Out of Cookies
© Copyright 2019 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. If you have questions about this mailing, or need assistance, please direct your inquiries to the IEEE Contact Center. Replies to this message do not reach IEEE. |
||