Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by
  Learning 4D Longitudinal Patient Data   Prognostic tumor growth modeling via volumetric medical imaging observations
can potentially lead to better outcomes of tumor treatment and surgical
planning. Recent advances of convolutional networks have demonstrated higher
accuracy than traditional mathematical models in predicting future tumor
volumes. This indicates that deep learning-based techniques may have great
potentials on addressing such problem. However, current 2D patch-based modeling
approaches cannot make full use of the spatio-temporal imaging context of the
tumor's longitudinal 4D (3D + time) data. Moreover, they are incapable to
predict clinically-relevant tumor properties, other than volumes. In this
paper, we exploit to formulate the tumor growth process through convolutional
Long Short-Term Memory (ConvLSTM) that extract tumor's static imaging
appearances and capture its temporal dynamic changes within a single network.
We extend ConvLSTM into the spatio-temporal domain (ST-ConvLSTM) by jointly
learning the inter-slice 3D contexts and the longitudinal or temporal dynamics
from multiple patient studies. Our approach can incorporate other non-imaging
patient information in an end-to-end trainable manner. Experiments are
conducted on the largest 4D longitudinal tumor dataset of 33 patients to date.
Results validate that the ST-ConvLSTM produces a Dice score of 83.2%+-5.1% and
a RVD of 11.2%+-10.8%, both significantly outperforming (p<0.05) other compared
methods of linear model, ConvLSTM, and generative adversarial network (GAN)
under the metric of predicting future tumor volumes. Additionally, our new
method enables the prediction of both cell density and CT intensity numbers.
Last, we demonstrate the generalizability of ST-ConvLSTM by employing it in 4D
medical image segmentation task, which achieves an averaged Dice score of
86.3+-1.2% for left-ventricle segmentation in 4D ultrasound with 3 seconds per
patient.

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The categories of this paper are: econ
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
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The categories of this paper are: q-alg
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The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: cond-mat