In neuroconductor/flexconn: FLEXCONN Model

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(neurobase)
library(flexconn)

Installing FLEXCONN

FLEXCONN stands for Fast Lesion Extraction using Convolutional Neural Networks, which the main goal is to segment lesions from magnetic resonance images (MRIs) from patients with multiple sclerosis (MS). The methods are described here: https://arxiv.org/abs/1803.09172. Patients with MS commonly have 2 MRI sequences done, T1-weighted images and fluid attenuated inversion recovery (FLAIR). FLEXCONN requires a number of Python modules to run. You can install them either system wide (not shown) or using reticulate. The reticulate way is wrapped in install_flexconn:

flexconn::install_flexconn()

Downloading a Model

Here we download one of the fit models from FLEXCONN 1.1 (https://www.nitrc.org/projects/flexconn/), which is from one reader, a 35x35 patch size (2D model) and it was fit using 21 atlases.

model_file = tempfile(fileext = ".h5")
base_url = paste0(
  "https://github.com/muschellij2/flexconnr",
  "/raw/master/inst/extdata/")
model_url = paste0(
  base_url, "21atlases/",
  "atlas_with_mask1/FLEXCONNmodel2D_35x35_17-10-2017_21-53-35.h5")
download.file(model_url, destfile = model_file)

We will load the model into R so we can predict using it

model = keras::load_model_hdf5(model_file)
model

Downloading Data

Again, we need a T1-weighted and FLAIR image to do prediction. We use some example data here from the package and plot each one. The gt object is the ground truth, manual segmentation:

t1_file = system.file("extdata", "MPRAGE.nii.gz", package = "flexconn")
t1 = readnii(t1_file)
ortho2(robust_window(t1))

flair_file = system.file("extdata", "FLAIR.nii.gz", package = "flexconn")
flair = readnii(flair_file)
ortho2(robust_window(flair))

gt_file = system.file("extdata", "MPRAGE_manual.nii.gz", package = "flexconn")
gt = readnii(gt_file)
ortho2(robust_window(flair), gt, xyz = xyz(gt))

Predicting Lesions

Here we will predict using the pre-trained model and we

res = flexconn_predict(model,
                       t1 = t1_file,
                       flair = flair_file)

Here we overlay the probabilities on the FLAIR image:

col.y =  c("#010000", "#0C0000", "#170000", "#210000", "#2C0000",
            "#360000", "#410000", "#4C0000", "#560000", "#610000",
            "#6C0000", "#760000", "#810000", "#8B0000", "#960000",
            "#A10000", "#AB0000", "#B60000", "#C10000", "#CB0000",
            "#D60000", "#E00000", "#EB0000", "#F60000", "#FF0100",
            "#FF0C00", "#FF1700", "#FF2100", "#FF2C00", "#FF3600",
            "#FF4100", "#FF4C00", "#FF5600", "#FF6100", "#FF6C00",
            "#FF7600", "#FF8100", "#FF8B00", "#FF9600", "#FFA100",
            "#FFAB00", "#FFB600", "#FFC100", "#FFCB00", "#FFD600",
            "#FFE000", "#FFEB00", "#FFF600", "#FFFF02", "#FFFF12",
            "#FFFF22", "#FFFF32", "#FFFF42", "#FFFF52", "#FFFF62",
            "#FFFF72", "#FFFF81", "#FFFF91", "#FFFFA1", "#FFFFB1",
            "#FFFFC1", "#FFFFD1", "#FFFFE1", "#FFFFF1")
col.y = grDevices::col2rgb(col.y, alpha = 0.5)
col.y = grDevices::rgb(col.y[1,], col.y[2,], col.y[3,], col.y[4,], maxColorValue = 255)
ortho2(robust_window(flair), res, col.y = col.y)

Here we see we have a probability map of lesions. To get a binary mask, we can use the remove_small_components, which thresholds the image and removes voxels with fewer than 27 connected voxels:

pred = remove_small_components(res)

ortho2(robust_window(flair), pred, xyz = xyz(pred), col.y = "#FF000080")

neurobase::fast_dice(pred, gt)

neuroconductor/flexconn documentation built on Sept. 25, 2020, 10:51 a.m.