Hello, I wanted to share something our team has been working on for a while. I work on an early stage radiology imaging company where we have a blessing and curse of having too much medical imaging data. Something we found internally useful to build was a DICOM Decoder Op for TensorFlow. We are making this available open-source here: https://github.com/gradienthealth/gradient_decode_dicom.

DICOM is an extremely broad standard, so we try to cover the 90% case of image formats (PNG, TIFF, BMP, JPEG, JPEG2000). We also support multi-frame/multi-frame color images. Try images found here: https://barre.dev/medical/samples/. In the case an unsupported format is found, an empty Tensor is returned which can be filtered out. Reading the files directly off of bucket storage has allowed us to prevent data duplication of .dcm data (a single CT can be 300MB). You can play with the op in this Colab notebook: https://colab.research.google.com/drive/1MdjXN3XkYs_mSyVtdRK7zaCbzkjGub_B

We firmly believe that having open-source resources in healthcare is what will enable its use in practice, not AI trade secrets. We plan on opening more of our work in the future. DM me if there is interest in contributing to upcoming toolkits (the next one we are thinking of creating is an operation to decrypt+decompress gzip files). Also, lmk if there is interest in working with our dataset (~300M DICOMs + notes). The goal of these project collaborations is that they are ultimately open-sourced.

Anyway, give the operation a try. If there are problems with loading a file of interest, please make an issue on GitHub. Right now only Linux based systems are supported, and a Dockerfile example will be coming soon.