Classifying functional nuclear images with convolutional neural networks: a survey. Issue 14 (21st October 2020)

• Record Type: Journal Article
• Authors: Lin, Qiang; Man, Zhengxing; Cao, Yongchun; Deng, Tao; Han, Chengcheng; Cao, Chuangui; Zhang, Linjun; Zeng, Sitao; Gao, Ruiting; Wang, Weilan; Ji, Jinshui; Huang, Xiaodi
• Is Part Of: IET image processing. Volume 14:Issue 14(2020)
• Page Start: 3300
• Page End: 3313
• Publication Date: 2020-10-21
• Subjects: computerised tomography -- biomedical MRI -- single photon emission computed tomography -- biological tissues -- diseases -- medical image processing -- positron emission tomography -- convolutional neural nets -- reviews -- image classification; positron emission tomography -- CNN classification models -- convolutional neural networks -- nuclear medicine functional imaging -- functional nuclear medicine images -- single‐photon emission computed tomography -- pathological tissues -- decision making -- disease -- magnetic resonance imaging images; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1690
• Languages: English
• ISSNs: 1751-9659

Abstract

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.