Abstract
Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propose a weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence, thereby bypassing classical intensity-based image similarity measures. During training, a convolutional neural network is optimised by outputting a dense displacement field (DDF) that warps a set of available anatomical labels from the moving image to match their corresponding counterparts in the fixed image. These label pairs, including solid organs, ducts, vessels, point landmarks and other ad hoc structures, are only required at training time and can be spatially aligned by minimising a cross-entropy function of the warped moving label and the fixed label. During inference, the trained network takes a new image pair to predict an optimal DDF, resulting in a fully-automatic, label-free, real-time and deformable registration. For interventional applications where large global transformation prevails, we also propose a neural network architecture to jointly optimise the global- and local displacements. Experiment results are presented based on cross-validating registrations of 111 pairs of T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients with a total of over 4000 anatomical labels, yielding a median target registration error of 4.2 mm on landmark centroids and a median Dice of 0.88 on prostate glands.
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 |
| Publisher | IEEE Computer Society |
| Pages | 1070-1074 |
| Number of pages | 5 |
| Volume | 2018-April |
| ISBN (Electronic) | 9781538636367 |
| DOIs | |
| Publication status | E-pub ahead of print - 24 May 2018 |
| Event | 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) - Washington, DC, USA Duration: 4 Apr 2018 → 7 Apr 2018 |
Conference
| Conference | 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) |
|---|---|
| Period | 4/04/2018 → 7/04/2018 |
Keywords
- Convolutional neural network
- Image-guided intervention
- Multimodal medical image registration
- Prostate cancer
- Weakly-supervised learning
Access to Document
- 1711.01666v2
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