pySegUpsampler 0.1.0

A python package that upsample segmentated medical image

Segmentation Upsampler

This is a Python-based software for upsampling 3-dimensional segmented medical images using a mesh-based method. This method may be useful for reducing staircasing artifacts and increasing accuracy in medical image-derived ultrasound simulations, among other applications requiring accurate segmentation upsampling.

Installation

*The software is Python-based and MATLAB is used to generate sample shapes and run tests.

The software was developed in following environment:
Python version 3.9
MATLAB R2023b
Please check the compatibility of lateral Python versions using this link: https://www.mathworks.com/support/requirements/python-compatibility.html.

Python package Installation:
pip install pySegUpsampler==0.0.8

MATLAB package:
k-Wave (http://www.k-wave.org/)

I/O

The algorithm accepts and returns the following variables:

Input:

originalImage: low resolution input image (binary 3D array - two labels, integer 3D array - more than two labels)
spacing: spacing of the original image (1 x 3 floating point array)
$dx$: scale of upsampling (scalar float for isotropic upsampling, 1 x 3 floating point array for an anisotropic medical image)
$\sigma$: the standard deviation for the Gaussian smoothing kernel (scalar float, recommended range: 0 - 1)
$I$: isovalue for isosurface extraction (scalar float, recommand range: 0.4 - 0.5)

Output:

newImage: high resolution output with defined spacing

Examples

example_multilabel_testobject.m upsamples a code-generated complex shape and compares it to a high-resolution code-generated ground truth.

example_vertebra.m resamples a medical image-based segementation of a human spine. Figure 1 depicts slices through the 3D spine volume, demonstrating the upsampling of a multi-label spine segmentation with input parameters $\sigma = 0.7$ and isovalue = 0.4. The input image is sourced from Liebl $et$ $al$. 2021 ¹. This demonstration resamples the original anisotropic voxel spacing of [0.2910, 0.2910, 1.2500] millimetres to an isotropic [0.8, 0.8, 0.8] millimetre voxel spacing. Gap post-processing was not applied.

Figure 1: Mesh-based upsampling demonstration with a segmented spine (subverse003) from the Verse2020 spine segmentation dataset ¹.

example_AustinWoman_Kidney.m upsamples a segmented medical image of a kidney and surrounding organs from the female Visible Human Project dataset ² with input parameters $\sigma = 0.4$ and isovalue = 0.4. This demonstration upsamples the original voxel spacing of [1, 1, 1] millimetres to an isotropic [0.8, 0.8, 0.8] millimetre voxel spacing. Gap post-processing was applied.

Figure 2: Mesh-based upsampling demonstration with a kidney and surrounding organs from the female Visible Human Project dataset ².

Test

SinglelabelGridSearch.m provides a grid-search method to find optimal parameter ($\sigma$, isovalue) settings for upsampling a single-label code-based image. Mis-labelling errors are evaluated using a high-resolution code-based image.

MultilabelGridSearch.m provides a grid-search method to find optimal parameter ($\sigma$, isovalue) settings for upsampling a multi-label code-based image. Errors resulting from gaps between labels and volume differences are evaluated using a high-resolution code-based image.

methodComparsion.m provides a comparsion of the mesh-based upsampling method against naive (nearest-neighbour and trilinear interpolation) upsampling methods.

normalizationComparsion.m compares error metrics for evaluating the accuracy of the upsampled result using a high-resolution ground truth.

IsovalueVSVolume.m provides the variation of volume ratio against isovalue, which can be used by the user to select the isovalue based on the desired volume.

References

1. Liebl, H., Schinz, D., Sekuboyina, A., Malagutti, L., Löffler, M. T., Bayat, A., ... & Kirschke, J. S. (2021). A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data. Scientific data, 8(1), 284. ↩ ↩²

2. Massey, J. W., & Yilmaz, A. E. (2016, August). AustinMan and AustinWoman: High-fidelity, anatomical voxel models developed from the VHP color images. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3346-3349). IEEE. ↩ ↩²