Z-SSMNet: A Zonal-aware Self-Supervised Mesh Network for Prostate Cancer
Detection and Diagnosis in bpMRI
- URL: http://arxiv.org/abs/2212.05808v1
- Date: Mon, 12 Dec 2022 10:08:46 GMT
- Title: Z-SSMNet: A Zonal-aware Self-Supervised Mesh Network for Prostate Cancer
Detection and Diagnosis in bpMRI
- Authors: Yuan Yuan, Euijoon Ahn, Dagan Feng, Mohamad Khadra, Jinman Kim
- Abstract summary: Prostate cancer (PCa) is one of the most prevalent cancers in men and many people around the world die from clinically significant PCa (csa)
Early diagnosis of csPCa in bi-parametric MRI (bpMRI) can contribute to precision care for PCa.
Existing state of the art AI algorithms are often limited to 2D images that fail to capture inter-slice correlations in 3D volumetric images.
We propose a new Zonal-aware Self-supervised Mesh Network (Z-SSMNet) that adaptatively fuses multiple 2D, 2.5D and 3D CNNs to
- Score: 16.950834401030093
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Prostate cancer (PCa) is one of the most prevalent cancers in men and many
people around the world die from clinically significant PCa (csPCa). Early
diagnosis of csPCa in bi-parametric MRI (bpMRI), which is non-invasive,
cost-effective, and more efficient compared to multiparametric MRI (mpMRI), can
contribute to precision care for PCa. The rapid rise in artificial intelligence
(AI) algorithms are enabling unprecedented improvements in providing decision
support systems that can aid in csPCa diagnosis and understanding. However,
existing state of the art AI algorithms which are based on deep learning
technology are often limited to 2D images that fails to capture inter-slice
correlations in 3D volumetric images. The use of 3D convolutional neural
networks (CNNs) partly overcomes this limitation, but it does not adapt to the
anisotropy of images, resulting in sub-optimal semantic representation and poor
generalization. Furthermore, due to the limitation of the amount of labelled
data of bpMRI and the difficulty of labelling, existing CNNs are built on
relatively small datasets, leading to a poor performance. To address the
limitations identified above, we propose a new Zonal-aware Self-supervised Mesh
Network (Z-SSMNet) that adaptatively fuses multiple 2D, 2.5D and 3D CNNs to
effectively balance representation for sparse inter-slice information and dense
intra-slice information in bpMRI. A self-supervised learning (SSL) technique is
further introduced to pre-train our network using unlabelled data to learn the
generalizable image features. Furthermore, we constrained our network to
understand the zonal specific domain knowledge to improve the diagnosis
precision of csPCa. Experiments on the PI-CAI Challenge dataset demonstrate our
proposed method achieves better performance for csPCa detection and diagnosis
in bpMRI.
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