.. _Endoscopic Tool Segmentation:

Endoscopic Tool Segmentation
============================

Performs endoscopic tool segmentation using selectable algorithm backends, including feature-based and machine learning models.

Input
-----

One 2D image set. Only 8-bit RGB images with a single slice per image are supported.

Output
------

Label image with two classes: "Background" and "Endoscopic Tool". Visualization includes label names and colors.

Description
-----------

The algorithm supports multiple segmentation backends, selectable via the parameter ``endoscopicToolSegmentationAlgorithm``. Available backends may include feature-based methods and machine learning models (e.g., "Custom Model").

For the "Custom Model" backend, a machine learning model, Torch, ONNX or TensorRT is loaded from a user-specified path (``modelConfigurationPath``). The model processes the input image set and outputs a segmentation label image. An example model is the MONAI model (`model.ts`) at https://huggingface.co/MONAI/endoscopic_tool_segmentation/tree/0.6.2/models/. 

```yaml
Version: '8.0'
Type: NeuralNetwork
Name: CustomEndoscopicToolSegmentation
Description:
Engine:
  Name: torch
  ModelFile: model.ts
  ForceCPU: false
  Verbose: false
  InputFields: [Image]
  OutputFields: [ToolSeg]
  DisableJITRecompile: true
PreprocessingInputFields: [Image]
PredictionOutput: [Image]
Sampling:
- SkipUnpadding: True
PreProcessing:
- ResampleDims:
    target_dims: 736 480 1
- MakeFloat:    
- NormalizeUniform:
    min: 0
    max: 1
PostProcessing:
- Softmax:
- ArgMax:
- ResampleToInput:
MaxBatchSize: 2
```

Configuration parameters:

- **endoscopicToolSegmentationAlgorithm**: Selects the segmentation backend.
- **modelConfigurationPath** (for Custom Model): Path to the model configuration file.

Output labels:

- **Background** (label 0)
- **Endoscopic Tool** (label 1, color: green)

The output is a label image with these classes, suitable for visualization and further analysis.