Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging

Purpose We propose an approach of 3D convolutional neural network to segment the prostate in MR images. Methods A 3D deep dense multi-path convolutional neural network that follows the framework of the encoder–decoder design is proposed. The encoder is built based upon densely connected layers that...

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Vydané v:International journal for computer assisted radiology and surgery Ročník 13; číslo 11; s. 1687 - 1696
Hlavní autori: To, Minh Nguyen Nhat, Vu, Dang Quoc, Turkbey, Baris, Choyke, Peter L., Kwak, Jin Tae
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 01.11.2018
Springer Nature B.V
Predmet:
Artificial neural networks
Computer Imaging
Computer Science
Convolution
Datasets
Ground truth
Health Informatics
Humans
Image segmentation
Imaging
Imaging, Three-Dimensional - methods
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Medical imaging
Medicine
Medicine & Public Health
Neural networks
Neural Networks (Computer)
Original Article
Pattern Recognition and Graphics
Prostate
Prostate - diagnostic imaging
Radiology
Surgery
Vision
Deep learning
Dense connections
Prostate segmentation
Grouped convolution
Magnetic resonance imaging
ISSN:1861-6410, 1861-6429, 1861-6429
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Shrnutí:Purpose We propose an approach of 3D convolutional neural network to segment the prostate in MR images. Methods A 3D deep dense multi-path convolutional neural network that follows the framework of the encoder–decoder design is proposed. The encoder is built based upon densely connected layers that learn the high-level feature representation of the prostate. The decoder interprets the features and predicts the whole prostate volume by utilizing a residual layout and grouped convolution. A set of sub-volumes of MR images, centered at the prostate, is generated and fed into the proposed network for training purpose. The performance of the proposed network is compared to previously reported approaches. Results Two independent datasets were employed to assess the proposed network. In quantitative evaluations, the proposed network achieved 95.11 and 89.01 Dice coefficients for the two datasets. The segmentation results were robust to variations in MR images. In comparison experiments, the segmentation performance of the proposed network was comparable to the previously reported approaches. In qualitative evaluations, the segmentation results by the proposed network were well matched to the ground truth provided by human experts. Conclusions The proposed network is capable of segmenting the prostate in an accurate and robust manner. This approach can be applied to other types of medical images.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1861-6410
1861-6429
1861-6429
DOI:10.1007/s11548-018-1841-4