TY - JOUR
T1 - Classifying functional nuclear images with convolutional neural networks
T2 - a survey
AU - Lin, Qiang
AU - Man, Zhengxing
AU - Cao, Yongchun
AU - Deng, Tao
AU - Han, Chengcheng
AU - Cao, Chuangui
AU - Zhang, Linjun
AU - Zeng, Sitao
AU - Gao, Ruiting
AU - Wang, Weilan
AU - Ji, Jinshui
AU - Huang, Xiaodi
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
UR - https://www.mendeley.com/catalogue/47c7065b-d5d0-34aa-b788-04c671cd9654/
U2 - 10.1049/iet-ipr.2019.1690
DO - 10.1049/iet-ipr.2019.1690
M3 - Article
SN - 1751-9659
VL - 14
SP - 3300
EP - 3313
JO - IET Image Processing
JF - IET Image Processing
IS - 14
ER -