<!-- README.md is generated from README.Rmd. Please edit that file -->

lungct

======

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The lungct R package develops an image processing pipeline for computed tomography (CT) scans of the lungs.

![lungct logo](inst/extdata/logo.png)

![Image Processing Pipeline](inst/extdata/pipeline.png)

Highlights

----------

-   We develop a free and simple segmentation algorithm that is comparable to the proprietary VIDA Diagnostics software

-   We create the first publicly available standard lung template using healthy adults, which is available for download via lungct

-   We show that the standard lung template allows for improved population-level inference of lung CTs using radiomics

-   lungct provides a fully-automated and open-source image processing pipeline for lung CTs, which is accessible to statisticians

Installation

------------

You can install lungct from github with:

``` r

# install.packages("devtools")

devtools::install_github("muschellij2/lungct")

```

Segmentation

------------

To segment the lungs from the CT scan:

``` r

library(lungct)

filename <- "example.nii.gz"

mask <- segment_lung(filename)

```

To segment the left and right lungs from the CT scan:

``` r

img <- ANTsRCore::antsImageRead(filename)

mask2 <- segment_lung2(img)

```

Standard Lung

-------------

The standard lung was created from N=62 healthy controls from [COPDGene](http://www.copdgene.org/) (50% female, 95% white, mean age = 62 years, mean BMI = 28.5)

To load the standard lung:

``` r

# Read in standard lung template

filepath <- system.file(

    "extdata", "lung_template_mask.nii.gz", 

    package = "lungct")

template <- ANTsRCore::antsImageRead(filepath)

```

Registration

------------

To register the mask to the standard lung:

``` r

# Register mask to standard lung

reg <- register_lung_mask(

    moving_mask = mask, 

    fixed_mask = template, 

    moving = img, 

    sides = c("right","left"),

    typeofTransform = "SyN")

```

We recommend the following for lung registration:

-   Separately register the right and left lungs to account for differences in lung shape and size

-   Perform registration on lung masks to preserve biological variability in Hounsfield units (HU)

-   Use [Symmetric Normalization (SyN)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2276735/) for nonlinear registration due to its flexibility and success in the [EMPIRE10](https://empire10.grand-challenge.org/) challenge

Template Creation

-----------------

For template creation, we follow the iterative method from [Avants](https://www.ncbi.nlm.nih.gov/pubmed/19818860), using iterations of register\_lung\_mask, get\_template, and calculate\_DSC from lungct. However, we define convergence as having a Dice similarity coefficient (DSC) between successive iterations of at least 0.99. We recommend using parallelization for the registration step.

![Template Creation](inst/extdata/templatecreation.png)

``` r

# Obtain new template

template_new <- get_template(

    folder_warp = warped_masks, 

    folder_comp = transformations)

# Check convergence     

dice <- calculate_DSC(template_init, template_new)

```

Radiomics

---------

Radiomics, a field of quantitative imaging where large amounts of features are extracted from medical images, is common for lung CTs.

Options in lungct:

-   radiomics\_slice: Calculation on 2D slices in axial, coronal, or sagittal planes

-   radiomics\_lung: Calculation on 3D right and left lungs

-   RIA\_lung: Calculation of advanced radiomic features, such as GLCM and GLRLM [More info](https://github.com/cran/RIA)

``` r

# Calculate radiomics

rad <- RIA_lung(

    img, mask, 

    sides = c("right", "left"), 

    features = c("fo", "glcm"), 

    bins_in = 16, equal_prob = FALSE, distance = 1, 

    statistic = "mean(X, na.rm = TRUE)")

```

COPDGene data usage

-------------------

For the lung CT template, the [COPDGene data](http://www.copdgene.org/) was used:

"This research used data generated by the COPDGene study, which was supported by NIH grants U01HL089856 and U01HL089897. The COPDGene project is also supported by the COPD Foundation through contributions made by an Industry Advisory Board comprised of Pfizer, AstraZeneca, Boehringer Ingelheim, Novartis, and Sunovion."