We lifted 3 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| biomedia-mira/istn | canonical | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| normalize_to_0_1 | Ran | biomedia-mira/istn/pymira/utils/tensorboard_helpers.py code served (permissive licence) · get_code("087eab5178635d1d") |
| volume_to_batch_image | Ran | biomedia-mira/istn/pymira/utils/tensorboard_helpers.py code served (permissive licence) · get_code("88a24758a68bf835") |
| multi_class_score | Not yet run | biomedia-mira/istn/pymira/utils/metrics.py code served (permissive licence) · get_code("88cefab9e3b50b88") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Image registration with deep neural networks has become an active field of research and exciting avenue for a long standing problem in medical imaging. The goal is to learn a complex function that maps the appearance of input image pairs to parameters of a spatial transformation in order to align corresponding anatomical structures. We argue and show that the current direct, non-iterative approaches are sub-optimal, in particular if we seek accurate alignment of Structures-of-Interest (SoI). Information about SoI is often available at training time, for example, in form of segmentations or landmarks. We introduce a novel, generic framework, Image-and-Spatial Transformer Networks (ISTNs), to leverage SoI information allowing us to learn new image representations that are optimised for the downstream registration task. Thanks to these representations we can employ a test-specific, iterative refinement over the transformation parameters which yields highly accurate registration even with very limited training data. Performance is demonstrated on pairwise 3D brain registration and illustrative synthetic data.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1907.09200")
get_code_for_paper("1907.09200")
have("1907.09200")
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