2021 JRNL PP Shumao Pang - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Spine parsing (i.e., multi-class segmentation
of vertebrae and intervertebral discs (IVDs)) for volumetric
magnetic resonance (MR) image plays a significant role in
various spinal disease diagnoses and treatments of spine
disorders, yet is still a challenge due to the inter-class
similarity and intra-class variation of spine images. Existing
fully convolutional network based methods failed to
explicitly exploit the dependencies between different spinal
structures. In this article, we propose a novel two-stage
framework named SpineParseNet to achieve automated
spine parsing for volumetricMR images. The SpineParseNet
consists of a 3D graph convolutional segmentation network
(GCSN) for 3D coarse segmentation and a 2D residual
U-Net (ResUNet) for 2D segmentation refinement. In 3D
GCSN, region pooling is employed to project the image
representation to graph representation, in which each node
representation denotes a specific spinal structure. The adjacency
matrix of the graph is designed according to the
connection of spinal structures. The graph representation
is evolved by graph convolutions. Subsequently, the proposed
region unpooling module re-projects the evolved
graph representation to a semantic image representation,
which facilitates the 3D GCSN to generate reliable coarse
segmentation. Finally, the 2D ResUNet refines the segmen- tation. Experiments on T2-weighted volumetric MR images
of 215 subjects show that SpineParseNet achieves impressive
performance with mean Dice similarity coefficients of
87.32 ± 4.75%, 87.78 ± 4.64%, and 87.49 ± 3.81% for the
segmentations of 10 vertebrae, 9 IVDs, and all 19 spinal
structures respectively. The proposed method has great
potential in clinical spinal disease diagnoses and treatments.