ConvLSTM coordinated longitudinal transformer under spatio-temporal features for tumor growth prediction

Accurate quantification of tumor growth patterns can indicate the development process of the disease. According to the important features of tumor growth rate and expansion, physicians can intervene and diagnose patients more efficiently to improve the cure rate. However, the existing longitudinal g...

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Veröffentlicht in:Computers in biology and medicine 2023-09, Vol.164, p.107313-107313, Article 107313
Hauptverfasser: Ma, Manfu, Zhang, Xiaoming, Li, Yong, Wang, Xia, Zhang, Ruigen, Wang, Yang, Sun, Penghui, Wang, Xuegang, Sun, Xuan
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Sprache:eng
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Zusammenfassung:Accurate quantification of tumor growth patterns can indicate the development process of the disease. According to the important features of tumor growth rate and expansion, physicians can intervene and diagnose patients more efficiently to improve the cure rate. However, the existing longitudinal growth model can not well analyze the dependence between tumor growth pixels in the long space-time, and fail to effectively fit the nonlinear growth law of tumors. So, we propose the ConvLSTM coordinated longitudinal Transformer (LCTformer) under spatiotemporal features for tumor growth prediction. We design the Adaptive Edge Enhancement Module (AEEM) to learn static spatial features of different size tumors under time series and make the depth model more focused on tumor edge regions. In addition, we propose the Growth Prediction Module (GPM) to characterize the future growth trend of tumors. It consists of a Longitudinal Transformer and ConvLSTM. Based on the adaptive abstract features of current tumors, Longitudinal Transformer explores the dynamic growth patterns between spatiotemporal CT sequences and learns the future morphological features of tumors under the dual views of residual information and sequence motion relationship in parallel. ConvLSTM can better learn the location information of target tumors, and it complements Longitudinal Transformer to jointly predict future imaging of tumors to reduce the loss of growth information. Finally, Channel Enhancement Fusion Module (CEFM) performs the dense fusion of the generated tumor feature images in the channel and spatial dimensions and realizes accurate quantification of the whole tumor growth process. Our model has been strictly trained and tested on the NLST dataset. The average prediction accuracy can reach 88.52% (Dice score), 89.64% (Recall), and 11.06 (RMSE), which can improve the work efficiency of doctors. •We use dual branch streams to extract static abstract features and collaboratively predict the growth process of tumors in long space-time.•We propose the Adaptive Edge Enhancement Module to extract the static abstract features of different sizes tumors and enhance the weight of the edge region.•Growth Prediction Module is designed to learn the dynamic growth process of the tumor over long space-time and predict the future shape features of target.•Channel Enhancement Fusion Module is introduced to perform the enhancement of feature channels and enrich the spatial pixel information.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2023.107313