2 research outputs found
Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN
Data-driven automatic approaches have demonstrated their great potential in
resolving various clinical diagnostic dilemmas for patients with malignant
gliomas in neuro-oncology with the help of conventional and advanced molecular
MR images. However, the lack of sufficient annotated MRI data has vastly
impeded the development of such automatic methods. Conventional data
augmentation approaches, including flipping, scaling, rotation, and distortion
are not capable of generating data with diverse image content. In this paper,
we propose a method, called synthesis of anatomic and molecular MR images
network (SAMR), which can simultaneously synthesize data from arbitrary
manipulated lesion information on multiple anatomic and molecular MRI
sequences, including T1-weighted (T1w), gadolinium enhanced T1w (Gd-T1w),
T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR), and amide
proton transfer-weighted (APTw). The proposed framework consists of a
stretch-out up-sampling module, a brain atlas encoder, a segmentation
consistency module, and multi-scale label-wise discriminators. Extensive
experiments on real clinical data demonstrate that the proposed model can
perform significantly better than the state-of-the-art synthesis methods.Comment: MICCAI 202
Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images in Patients with Post-treatment Malignant Gliomas
Data-driven automatic approaches have demonstrated their great potential in
resolving various clinical diagnostic dilemmas in neuro-oncology, especially
with the help of standard anatomic and advanced molecular MR images. However,
data quantity and quality remain a key determinant of, and a significant limit
on, the potential of such applications. In our previous work, we explored
synthesis of anatomic and molecular MR image network (SAMR) in patients with
post-treatment malignant glioms. Now, we extend it and propose Confidence
Guided SAMR (CG-SAMR) that synthesizes data from lesion information to
multi-modal anatomic sequences, including T1-weighted (T1w), gadolinium
enhanced T1w (Gd-T1w), T2-weighted (T2w), and fluid-attenuated inversion
recovery (FLAIR), and the molecular amide proton transfer-weighted (APTw)
sequence. We introduce a module which guides the synthesis based on confidence
measure about the intermediate results. Furthermore, we extend the proposed
architecture for unsupervised synthesis so that unpaired data can be used for
training the network. Extensive experiments on real clinical data demonstrate
that the proposed model can perform better than the state-of-theart synthesis
methods.Comment: Submit to IEEE TMI. arXiv admin note: text overlap with
arXiv:2006.1476