An unsupervised convolutional neural network-based algorithm for deformable image registration

By V. Kearney, Sam Haaf, A. Sudhyadhom, Gilmer Valdes, Timothy D. Solberg. 2018

58 citations recorded by the source corpus.

The purpose of the work is to develop a deep unsupervised learning strategy for cone-beam CT (CBCT) to CT deformable image registration (DIR). This technique uses a deep convolutional inverse graphics network (DCIGN) based DIR algorithm implemented on 2 Nvidia 1080 Ti graphics processing units. The model is comprised of an encoding and decoding stage. The fully-convolutional encoding stage learns hierarchical features and simultaneously forms an information bottleneck, while the decoding stage restores the original dimensionality of the input image. Activations from the encoding stage are used as the input channels to a sparse DIR algorithm. DCIGN was trained using a distributive learning-based convolutional neural network architecture and used 285 head and neck patients to train, validate, and test the algorithm. The accuracy of the DCIGN algorithm was evaluated on 100 synthetic cases and 12 hold out test patient cases. The results indicate that DCIGN performed better than rigid registration, intensity corrected Demons, and landmark-guided deformable image registration for all evaluation metrics. DCIGN required ~14 h to train, and ~3.5 s to make a prediction on a 512  ×  512  ×  120 voxel image. In conclusion, DCIGN is able to maintain high accuracy in the presence of CBCT noise contamination, while simultaneously preserving high computational efficiency.

Subfields: Biomedical Engineering, Radiation, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Pingkun Yan
2. Arvid Lundervold
3. Uwe Kruger
4. Grant Haskins

In Radiation

1. Arvid Lundervold

In Radiology, Nuclear Medicine and Imaging

1. Pingkun Yan
2. Arvid Lundervold
3. Uwe Kruger
4. Grant Haskins
5. Florian Tramèr
6. Dan Boneh

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “network-based algorithm for deformable image registration of cone-beam CT to CT using a deep convolutional inverse graphics network [98]; deep learning-based 2D/3D Figure 2.”
• Deep learning in medical image registration: a survey (2019, background): “mable [155] Cardiac 2D-2D No MR Deformable [177] Neural tissue 2D-2D No EM Deformable [14] Brain 3D-3D No MR Affine [37,38] Brain 3D-3D Yes MR Deformable [41] Lung, Cardiac 2D-2D No MR, Xray Deformable [72] HN 3D-3D Yes CT Deformable [77] Cardiac 3D-3D No MR Deformable [90] Brain 3D-3D No MR Deformabl”
• Deep learning in medical image registration: a review (2019, background): “[41] Lung, Cardiac 2D-2D No MR, Xray Deformable [72] HN 3D-3D Yes CT Deformable”