Classifying functional nuclear images with convolutional neural networks: a survey

Qiang Lin, Zhengxing Man, Yongchun Cao, Tao Deng, Chengcheng Han, Chuangui Cao, Linjun Zhang, Sitao Zeng, Ruiting Gao, Weilan Wang, Jinshui Ji, Xiaodi Huang

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
1 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)3300-3313
Number of pages14
JournalIET Image Processing
Volume14
Issue number14
Early online date21 Oct 2020
DOIs
Publication statusPublished - Dec 2020

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