TY - GEN
T1 - Fully convolutional structured LSTM networks for joint 4D medical image segmentation
AU - Gao, Yang
AU - Phillips, Jeff M.
AU - Zheng, Yan
AU - Min, Renqiang
AU - Fletcher, P. Thomas
AU - Gerig, Guido
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/23
Y1 - 2018/5/23
N2 - Longitudinal medical image analysis has great potential to reveal developmental trajectories and monitor disease progression. This process relies on consistent and robust joint 4D segmentation. Traditional methods highly depend on the similarity of images over time and either build a template or assume the images could be co-registered. This process may fail when image sequences present major appearance changes. Recently, deep learning (DL) approaches have achieved state-of-the-art results for related challenges in computer vision. These approaches make use of models such as fully convolutional networks (FCNs) for end-to-end pixel-wise segmentation and recurrent neural networks (RNNs) with long short-term memory (LSTM) units for sequence-to-sequence modeling. In this paper, we propose a new DL framework called FCSLSTM for 4D image segmentation with FCNs for the spatial model and LSTM for the temporal model. This is the first DL framework with deep integration of FCNs and LSTM for joint 4D segmentation that could be trained end-to-end. Our approach achieves promising results with the demonstrated application to longitudinal pediatric magnetic resonance imaging (MRI) segmentation.
AB - Longitudinal medical image analysis has great potential to reveal developmental trajectories and monitor disease progression. This process relies on consistent and robust joint 4D segmentation. Traditional methods highly depend on the similarity of images over time and either build a template or assume the images could be co-registered. This process may fail when image sequences present major appearance changes. Recently, deep learning (DL) approaches have achieved state-of-the-art results for related challenges in computer vision. These approaches make use of models such as fully convolutional networks (FCNs) for end-to-end pixel-wise segmentation and recurrent neural networks (RNNs) with long short-term memory (LSTM) units for sequence-to-sequence modeling. In this paper, we propose a new DL framework called FCSLSTM for 4D image segmentation with FCNs for the spatial model and LSTM for the temporal model. This is the first DL framework with deep integration of FCNs and LSTM for joint 4D segmentation that could be trained end-to-end. Our approach achieves promising results with the demonstrated application to longitudinal pediatric magnetic resonance imaging (MRI) segmentation.
UR - http://www.scopus.com/inward/record.url?scp=85048126617&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048126617&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2018.8363764
DO - 10.1109/ISBI.2018.8363764
M3 - Conference contribution
AN - SCOPUS:85048126617
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1104
EP - 1108
BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PB - IEEE Computer Society
T2 - 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Y2 - 4 April 2018 through 7 April 2018
ER -