NDLI: Tumour growth prediction of follow-up lung cancer via conditional recurrent variational autoencoder

Content Provider	IET Digital Library
Author	Xiao, Ning Qiang, Yan Zhao, Zijuan Zhao, Juanjuan Lian, Jianhong
Abstract	The prediction of lung tumour growth is the key to early treatment of lung cancer. However, the lack of intuitive and clear judgments about the future development of the tumour often leads patients to miss the best treatment opportunities. Combining the characteristics of the variational autoencoder and recurrent neural networks, this study proposes a tumour growth prediction via a conditional recurrent variational autoencoder. The proposed model uses a variational autoencoder to reconstruct tumour images at different times. Meanwhile, the recurrent units are proposed to infer the relationship between tumour images according to the chronological order. The different tumour development varies in different patients, patients' condition is adopted to achieve personalised prediction. To solve the problem of blurred results, the authors add the total variation regularisation term into the object function. The proposed method was tested on longitudinal studies, National Lung Screening Trial and cooperative hospital dataset, with three points on lung tumours. The precision, recall, and dice similarity coefficient reach 82.22, 79.89 and 82.49%, respectively. Both quantitative and qualitative experimental results show that the proposed method can produce realistic tumour images.
Starting Page	3975
Ending Page	3981
Page Count	7
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 15, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/15
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0496
Journal	IET Image Processing
Publisher Date	2020-12-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Cancer Clear Judgments Computer Vision And Image Processing Technique Computerised Tomography Conditional Recurrent Variational Autoencoder Cooperative Hospital Dataset Different Tumour Development Varies Early Treatment Image Reconstruction Image Segmentation Intuitive Judgments Lung Lung Cancer Lung Tumour Growth Lung Tumours Medical Image Processing Optical, Image And Video Signal Processing Patient Care And Treatment Patient Diagnostic Method And Instrumentation Patient Treatment Personalised Prediction Physiological Model Realistic Tumour Image Recurrent Neural Nets Recurrent Neural Network Recurrent Units Total Variation Regularisation Term Treatment Opportunities Tumour Growth Prediction Tumours
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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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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