Resources
MONAI Endoscopic Tool Segmentation Model

MONAI Endoscopic Tool Segmentation Model

Logo for MONAI Endoscopic Tool Segmentation Model
Description
Model for HoloHub Sample App for MONAI Endoscopic Tool Segmentation
Publisher
-
Latest Version
v0.1
Modified
March 28, 2023
Compressed Size
36.42 MB

Model for HoloHub Sample App for MONAI Endoscopic Tool Segmentation

This resource contains a MONAI endoscopic surgical tool segmentation model. For details on this model, please see MONAI GitHub repository. We convert the PyTorch model from MONAI to ONNX, and deploy the ONNX model in a HoloHub application, where at the first app runtime it will be converted to a TensorRT engine. Go to the HoloHub repository to find the sample app that utilizes this model.

Model

Given an RGB image of 480 x 736, this model provides semantic segmentation of surgical tools. Output channel 0 is tools and 1 is everything else.

Note: The provided model is in ONNX format. It will automatically be converted into a TensorRT model (.engine) the first time it is processed by a Holoscan application.

Inputs

  • INPUT__0 - Input RGB image (batchsize, height, width, channels)
    • shape=[1, 480, 736, 3]
    • dtype=float32

Outputs

  • OUTPUT__0 - Segmentation output with two channels.
    • shape=[1, 2, 480, 736]
    • dtype=float32

References

[1] Tan, M. and Le, Q. V. Efficientnet: Rethinking model scaling for convolutional neural networks. ICML, 2019a. https://arxiv.org/pdf/1905.11946.pdf

[2] O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015. https://arxiv.org/pdf/1505.04597.pdf

License

Refer to the MONAI Endoscopic Tool Segmentation Model License as well as the data license for the data used in training this model. 

Copyright (c) MONAI Consortium

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.