We are committed to exploring the application of synthesis or fusion for multi-sequence MRI (also including other modalities such as CT) in clinical settings.
Seq2Seq is a series of dynamic multi-domain models that can translate an arbitrary sequence to a target sequence.
- If you are looking for a straightforward way to use it without much thought, please try nnSeq2Seq.
- To learn more information about our work, please refer to our publications.
Referring to nnU-Net, we propose nnSeq2Seq, a tool for adaptively training Seq2Seq models with a given dataset. It will analyze the provided training cases and automatically configure a matching synthesis pipeline. No expertise is required on your end! You can easily train the models and use them for your application.
- One image
$\rightarrow$ one image- Missing sequence/modality synthesis
- Imaging differentiation map generation
- Multiple images
$\rightarrow$ one image- Missing sequence/modality synthesis
- Metrics calculation
- Sequence contribution
Read these:
Additional information:
Follow for our publications, which contain new features that have not yet been added to nnSeq2Seq.
If you use Seq2Seq or some part of the code, please cite (see bibtex):
-
Seq2Seq: an arbitrary sequence to a target sequence synthesis, the sequence contribution ranking, and associated imaging-differentiation maps.
Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRI
Medical Image Analysis. -
TSF-Seq2Seq: an explainable task-specific synthesis network, which adapts weights automatically for specific sequence generation tasks and provides interpretability and reliability.
An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis
MICCAI2023.
