NDLI: Classifying functional nuclear images with convolutional neural networks: a survey

Content Provider	IET Digital Library
Author	Lin, Qiang Man, Zhengxing Cao, Yongchun Deng, Tao Han, Chengcheng Cao, Chuangui Zhang, Linjun Zeng, Sitao Gao, Ruiting Wang, Weilan Ji, Jinshui Huang, Xiaodi
Abstract	Functional imaging has successfully been applied to capture functional changes in the pathological tissues of a body in recent years. Nuclear medicine functional imaging has been used to acquire information about areas of concerns (e.g. lesions and organs) in a non-invasive manner, enabling semi-automated or automated decision-making for disease diagnosis, treatment, evaluation, and prediction. Focusing on functional nuclear medicine images, in this study, the authors review existing work on the classification of single-photon emission computed tomography, positron emission tomography, and their hybrid modalities with computed tomography and magnetic resonance imaging images by using convolutional neural network (CNN) techniques. Specifically, they first present an overview of nuclear imaging and the CNN technique, such as nuclear imaging modalities, nuclear image data format, CNN architecture, and the main CNN classification models. According to the diseases of concern, they then classify the existing CNN-based work on the classification of functional nuclear images into three different categories. For the typical work in each of these categories, they present details about their research objectives, adopted CNN models, and achieved main results. Finally, they discuss research challenges and directions for developing technological solutions to classify nuclear medicine images based on the CNN technique.
Starting Page	3300
Ending Page	3313
Page Count	14
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 14, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1690
Journal	IET Image Processing
Publisher	The Institution of Engineering and Technology
Publisher Date	2020-09-11
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Biological Tissues Biology And Medical Computing Biomedical Imaging/measurement Biomedical Magnetic Resonance Imaging Biomedical MRI CNN Classification Model Computer Vision And Image Processing Technique Computerised Tomography Convolutional Neural Nets Convolutional Neural Network Decision Making Disease Diseases Functional Nuclear Medicine Image Image Classification Magnetic Resonance Imaging Image Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy Neural Computing Technique Nuclear Medicine Functional Imaging Nuclear Medicine, Emission Tomography Optical, Image And Video Signal Processing Pathological Tissues Patient Diagnostic Method And Instrumentation Positron Emission Tomography Radiography And Computed Tomography Review Reviews And Tutorial Papers Rsource Letters Single Photon Emission Computed Tomography Single-photon Emission Computed Tomography Spectroscopy X-Ray Technique X-Rays And Particle Beam
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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