Abstract
We present a new unsupervised learning algorithm, “FAIM”, for 3D medical image registration. With a different architecture than the popular “U-net” [10], the network takes a pair of full image volumes and predicts the displacement fields needed to register source to target. Compared with “U-net” based registration networks such as VoxelMorph [2], FAIM has fewer trainable parameters but can achieve higher registration accuracy as judged by Dice score on region labels in the Mindboggle-101 dataset. Moreover, with the proposed penalty loss on negative Jacobian determinants, FAIM produces deformations with many fewer “foldings”, i.e. regions of non-invertibility where the surface folds over itself. We varied the strength of this penalty and found that FAIM is able to maintain both the advantages of higher accuracy and fewer “folding” locations over VoxelMorph, over a range of hyper-parameters. We also evaluated Probabilistic VoxelMorph [3], both in its original form and with its U-net backbone replaced with our FAIM network. We found that the choice of backbone makes little difference. The original version of FAIM outperformed Probabilistic VoxelMorph for registration accuracy, and also for invertibility if FAIM is trained using an anti-folding penalty. Code for this paper is freely available at https://github.com/dykuang/Medical-image-registration.
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Notes
- 1.
Their corresponding labels are not used in training.
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This work was supported in part by a Discovery Grant from NSERC Canada.
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Kuang, D., Schmah, T. (2019). FAIM – A ConvNet Method for Unsupervised 3D Medical Image Registration. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_74
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