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Orsi Academy Sample Applications

Description
Sample models, segmentations and videos from ORSI Academy to be used with Holoscan SDK
Publisher
NVIDIA
Latest Version
20240206
Modified
February 6, 2024
Compressed Size
186.89 MB

Orsi Academy Sample Applications

This resource contains a binary tool segmentation model [1], a binary out-of-body classification model and sample surgical videos as accompanying assets for the Orsi Academy holohub applications.

[1] Hofman, J., De Backer, P., Manghi, I., Simoens, J., De Groote, R., Van Den Bossche, H., D’Hondt, M., Oosterlinck, T., Lippens, J., Van Praet, C., Ferraguti, F., Debbaut, C., Li, Z., Kutter, O., Mottrie, A., Decaestecker, K.: First-in-human real-time AI-assisted instrument deocclusion during augmented reality robotic surgery. Healthc. Technol. Lett. 1–7 (2023)

Models

The binary segmentation model is used for organic vs. non-organic pixel classification and was trained on a dataset comprised of 31812 images covering 37 classes of non-organic items, including robotic and laparoscopic instruments, needles, wires, clips, vessel loops, bulldogs, gauzes, and more. A train/val/test split of 24087/4545/3180 images was used yielding a 0.94 test set mean IoU.

  • Input: RGB image (bathsize, height, width, channels)
    • shape=[1, 512, 512, 3]
    • dtype=float32
    • range=[0, 255]
  • Output: probability output tensor (batchsize, class, height, width)
    • shape=[1, 2, 512, 512]
    • dtype=float32
    • range=[0, 1]

The pipeline postprocesses the output tensor using a softmax operator to obtain the final classification.

The binary out-of-body classification model was trained on a dataset comprised of 496828 images covering 6 procedure types and 4 robotic systems, yielding a ROC-AUC score of 99.46%.

  • Input: RGB image (batchsize, height, width, channels)
    • shape=[1, 224, 224, 3]
    • dtype=float32
    • range=[0, 255]
  • Output: probability output tensor (batchsize,)
    • shape [1,]
    • dtype=float32
    • range=[0, 1]

The pipeline postprocesses the output tensor using a sigmoid operator to obtain the final classification.

Video Data

The sample data, kindly provided by Orsi Academy are snippets of 3 surgical videos. For the Anonymization and MultiAI applications, snippets from partial nephrectomy procedures were used. For the segmentation application, a snippet was used from a procedure where a migrated endovascular stent was removed as a result of a nutcracker syndrome. The paper describing the deployment of the holohub segmentation application [1] was awarded an outstanding paper award at the joint 17th AE-CAI, 10th CARE and 6th OR 2.0 MICCAI workshop 2023. The work contributing to the anonymization pipeline was presented at CLINICCAI22 and was awarded a best presentation award.

Note: the .mp4 files must be converting into a GXF entity using the convert_videos_to_gxf.sh script.

License

The sample data and models are free for non-commercial use under the Creative Commons Attribution CC BY-NC-ND 4.0. Open to commercial use after review. Please contact Pieter.De.Backer@orsi.be for inquiries. When using segmentation or anonymization models, users must cite [1]. Please also refer to license agreement.