2 research outputs found
Automatic Pulmonary Artery-Vein Separation in CT Images using Twin-Pipe Network and Topology Reconstruction
With the development of medical computer-aided diagnostic systems, pulmonary
artery-vein(A/V) separation plays a crucial role in assisting doctors in
preoperative planning for lung cancer surgery. However, distinguishing arterial
from venous irrigation in chest CT images remains a challenge due to the
similarity and complex structure of the arteries and veins. We propose a novel
method for automatic separation of pulmonary arteries and veins from chest CT
images. The method consists of three parts. First, global connection
information and local feature information are used to construct a complete
topological tree and ensure the continuity of vessel reconstruction. Second,
the Twin-Pipe network proposed can automatically learn the differences between
arteries and veins at different levels to reduce classification errors caused
by changes in terminal vessel characteristics. Finally, the topology optimizer
considers interbranch and intrabranch topological relationships to maintain
spatial consistency to avoid the misclassification of A/V irrigations. We
validate the performance of the method on chest CT images. Compared with manual
classification, the proposed method achieves an average accuracy of 96.2% on
noncontrast chest CT. In addition, the method has been proven to have good
generalization, that is, the accuracies of 93.8% and 94.8% are obtained for CT
scans from other devices and other modes, respectively. The result of pulmonary
artery-vein obtained by the proposed method can provide better assistance for
preoperative planning of lung cancer surgery
Neuron segmentation using 3D wavelet integrated encoder-decoder network
Motivation: 3D neuron segmentation is a key step for the neuron digital
reconstruction, which is essential for exploring brain circuits and
understanding brain functions. However, the fine line-shaped nerve fibers of
neuron could spread in a large region, which brings great computational cost to
the neuron segmentation. Meanwhile, the strong noises and disconnected nerve
fibers bring great challenges to the task. Results: In this paper, we propose a
3D wavelet and deep learning based 3D neuron segmentation method. The neuronal
image is first partitioned into neuronal cubes to simplify the segmentation
task. Then, we design 3D WaveUNet, the first 3D wavelet integrated
encoder-decoder network, to segment the nerve fibers in the cubes; the wavelets
could assist the deep networks in suppressing data noises and connecting the
broken fibers. We also produce a Neuronal Cube Dataset (NeuCuDa) using the
biggest available annotated neuronal image dataset, BigNeuron, to train 3D
WaveUNet. Finally, the nerve fibers segmented in cubes are assembled to
generate the complete neuron, which is digitally reconstructed using an
available automatic tracing algorithm. The experimental results show that our
neuron segmentation method could completely extract the target neuron in noisy
neuronal images. The integrated 3D wavelets can efficiently improve the
performance of 3D neuron segmentation and reconstruction. Availability: The
data and codes for this work are available at
https://github.com/LiQiufu/3D-WaveUNet.Comment: Bioinformatics accepted pape