MitkConvertToMultiLabelSegmentation User Guide

Table of Contents

• Overview
• Basic Usage
• Required Arguments
• Optional Arguments
• 🧪 Examples
  • Example 1: Convert a single binary image mask
  • Example 2: Merge multiple binary masks into one group
  • Example 3: Group surface inputs into separate label groups (with reference image)
  • Example 4: Convert a contour set using reference image
  • Example 5: Input image with multiple pixel values (multi-label image)
• 📂 Details
  • 🏷 Label Naming Strategy
  • 📁 Group Naming Strategy
  • 🎯 Label Value Assignment Strategy
• Output Format

Overview

This command-line tool converts various medical data types (images, surfaces, and contours) into a multi-label segmentation using the Medical Imaging Interaction Toolkit (MITK).

Features

• Converts images, surface meshes, and contours into a single multi-label segmentation output.
• Supports grouping inputs into distinct label groups.
• Automatically resolves conflicting label values.
• Ensures geometry consistency and validates spatial alignment.
• Accepts DICOM, NIfTI, and other MITK-compatible image formats.

Basic Usage

convertToMultiLabelSegmentation -i <input1> <input2> ... -o <output.nrrd> [options]

Required Arguments

Argument	Description
-i, --inputs	One or more input files (images, surfaces, or contours).
-o, --output	Output file path for the resulting multi-label segmentation.

Optional Arguments

Argument	Description
-r, --reference	Reference image for defining the output geometry (required if no image inputs are used).
-g, --groups	Enable grouping: each input is placed into a separate label group. Default: all inputs are in one group.
-h, --help	Show help and usage information.

🧪 Examples

Example 1: Convert a single binary image mask

convertToMultiLabelSegmentation -i tumor.nii.gz -o tumor.nrrd

What happens:
A single binary image is converted to a multi-label segmentation with one label.

Label & Group Naming:

• Group: Unnamed default group
• Label: tumor Value 1

Resulting Pixel Values:

• Label tumor Value 1: value 1

---

Example 2: Merge multiple binary masks into one group

convertToMultiLabelSegmentation -i mask1.nii.gz mask2.nii.gz -o merged.nrrd

What happens:
Two binary image masks are merged into a single label group.

Label & Group Naming:

• Group: Unnamed default group
• Labels:
  • mask1 Value 1
  • mask2 Value 1

Resulting Pixel Values:

• Label mask1 Value 1: value 1
  
• Label mask2 Value 1: value 2

---

Example 3: Group surface inputs into separate label groups (with reference image)

convertToMultiLabelSegmentation -i liver.stl spleen.stl kidney.stl -r reference.nii.gz -o organs.nrrd -g

What happens:
Each surface is rasterized using the reference image and placed in its own group.

Label & Group Naming:

• Group liver: Label liver
  
• Group spleen: Label spleen
  
• Group kidney: Label kidney

Resulting Pixel Values:

• Group liver, Label liver: value 1
• Group spleen, Label spleen: value 2
  
• Group kidney, Label kidney: value 3

---

Example 4: Convert a contour set using reference image

convertToMultiLabelSegmentation -i roi.contour -r referenceCT.nii.gz -o contour.nrrd

What happens:
Contours are rasterized into a binary mask using the reference image geometry.

Label & Group Naming:

• Group: Unnamed default group
• Label: roi

Resulting Pixel Values:

• Label roi: value 1

---

Example 5: Input image with multiple pixel values (multi-label image)

convertToMultiLabelSegmentation -i MultiLabelSeg.nii.gz -o resegmented.nrrd

What happens:
An image containing multiple pixel values (e.g., 1 = liver, 2 = spleen, 3 = kidney) is converted.

Label & Group Naming:

• Group: Unnamed default group
  
• Labels:
  • MultiLabelSeg Value 1
    
  • MultiLabelSeg Value 2
    
  • MultiLabelSeg Value 3

Resulting Pixel Values:

• MultiLabelSeg Value 1: value 1
  
• MultiLabelSeg Value 2: value 2
  
• MultiLabelSeg Value 3: value 3

---

📂 Details

🏷 Label Naming Strategy

• Image inputs:
  • Each unique pixel value is treated as a separate label.
  • Label names follow the format: <filename> Value <pixel_value>.
• Non-image inputs (surfaces, contours):
  • Treated as binary masks.
  • Label name is the filename without extension.

📁 Group Naming Strategy

• When --groups is enabled:
  • Each input becomes a separate group, named from the input filename.
• When --groups is not enabled (default):
  • All labels are placed in a single unnamed group.

🎯 Label Value Assignment Strategy

• Image Inputs:
  • Unique pixel values are extracted.
  • If label values overlap across inputs, the tool automatically remaps values to avoid conflicts.
  • Label names still reflect original values (e.g., Value 2), even if actual pixel values are remapped.
• Non-Image Inputs:
  • Converted into binary masks.
  • Default pixel value is 1.
  • If conflicts exist, remapping is applied.
• Label values after conversion are unique over all groups.

Output Format

The resulting file is a standard multi-label segmentation in NRRD format:

• Can be loaded into MITK or compatible toolkits.
• Encodes labels and groups as distinct entities.
• Stores label metadata (names, values, group names).