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save_middle_slices_as_images.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save middle slices of scans as images\n",
"Most image models expect a three-channel input image. Will take the middle three grayscale slices, and dilated middle three slices centered on the middle, and save them as three-channel images."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from PIL import Image\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[01;34m..\u001b[00m\n",
"├── \u001b[01;34mdata\u001b[00m\n",
"│ ├── \u001b[01;34m__MACOSX\u001b[00m\n",
"│ │ └── \u001b[01;34mMRNet-v1.0\u001b[00m\n",
"│ │ ├── \u001b[01;34mtrain\u001b[00m\n",
"│ │ │ ├── \u001b[01;34maxial\u001b[00m\n",
"│ │ │ ├── \u001b[01;34mcoronal\u001b[00m\n",
"│ │ │ └── \u001b[01;34msagittal\u001b[00m\n",
"│ │ └── \u001b[01;34mvalid\u001b[00m\n",
"│ ├── \u001b[01;34mmid1\u001b[00m\n",
"│ │ ├── \u001b[01;34mtrain\u001b[00m\n",
"│ │ │ ├── \u001b[01;34maxial\u001b[00m\n",
"│ │ │ ├── \u001b[01;34mcoronal\u001b[00m\n",
"│ │ │ └── \u001b[01;34msagittal\u001b[00m\n",
"│ │ └── \u001b[01;34mvalid\u001b[00m\n",
"│ │ ├── \u001b[01;34maxial\u001b[00m\n",
"│ │ ├── \u001b[01;34mcoronal\u001b[00m\n",
"│ │ └── \u001b[01;34msagittal\u001b[00m\n",
"│ ├── \u001b[01;34mMRNet-small\u001b[00m\n",
"│ │ ├── \u001b[01;34mtrain\u001b[00m\n",
"│ │ │ ├── \u001b[01;34maxial\u001b[00m\n",
"│ │ │ ├── \u001b[01;34mcoronal\u001b[00m\n",
"│ │ │ └── \u001b[01;34msagittal\u001b[00m\n",
"│ │ └── \u001b[01;34mvalid\u001b[00m\n",
"│ │ ├── \u001b[01;34maxial\u001b[00m\n",
"│ │ ├── \u001b[01;34mcoronal\u001b[00m\n",
"│ │ └── \u001b[01;34msagittal\u001b[00m\n",
"│ └── \u001b[01;34mMRNet-v1.0\u001b[00m\n",
"│ ├── \u001b[01;34mtrain\u001b[00m\n",
"│ │ ├── \u001b[01;34maxial\u001b[00m\n",
"│ │ ├── \u001b[01;34mcoronal\u001b[00m\n",
"│ │ └── \u001b[01;34msagittal\u001b[00m\n",
"│ └── \u001b[01;34mvalid\u001b[00m\n",
"│ ├── \u001b[01;34maxial\u001b[00m\n",
"│ ├── \u001b[01;34mcoronal\u001b[00m\n",
"│ └── \u001b[01;34msagittal\u001b[00m\n",
"└── \u001b[01;34mmrnet-fastai\u001b[00m\n",
" └── \u001b[01;34mexp\u001b[00m\n",
"\n",
"37 directories\n"
]
}
],
"source": [
"! tree -d .."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copy data to another directory, for example `/mid3d0`, for middle 3 slices, 0 dilation.\n",
"Do this for d in (0,1,2) and perhaps greater than 2."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"!cp -r ../data/MRNet-v1.0 ../data/mid3d0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recurse through /train and /valid directories, load the scan file (.npy), select middle 3 slices, possibly with gaps of size dilation between them, and save as 3-channel images. Discard the copied files."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----------------------\n",
"Creating png files centered on middle slices with dilations of size 2\n",
"Processing 1130 .npy files in ../data/mid3d2/train/axial\n",
"===============================\n",
"Processing 1130 .npy files in ../data/mid3d2/train/coronal\n",
"===============================\n",
"Processing 1130 .npy files in ../data/mid3d2/train/sagittal\n",
"===============================\n",
"Processing 120 .npy files in ../data/mid3d2/valid/axial\n",
"===============================\n",
"Processing 120 .npy files in ../data/mid3d2/valid/coronal\n",
"===============================\n",
"Processing 120 .npy files in ../data/mid3d2/valid/sagittal\n",
"===============================\n"
]
}
],
"source": [
"dilations = [0,1,2]\n",
"for d in dilations:\n",
" print('-----------------------')\n",
" print('Creating png files centered on middle slices with dilations of size {}'.format(d))\n",
" for ds in ('train','valid'):\n",
" for p in ('axial','coronal','sagittal'):\n",
" dirpath = '../data/mid3d{}/{}/{}'.format(d, ds, p)\n",
" npy_files = [f for f in os.listdir(dirpath) if f[-4:]=='.npy']\n",
" print('Processing {} .npy files in {}'.format(len(npy_files), dirpath))\n",
"\n",
" for npyf in npy_files:\n",
" npyfilepath = dirpath + '/' + npyf\n",
" #print('Converting {}'.format(npyfilepath))\n",
" # load the array\n",
" scanarray = np.load(npyfilepath)\n",
" #print('Number of slices for this scan: {}'.format(scanarray.shape[0]))\n",
" # get the middle slice index\n",
" midslice = scanarray.shape[0]//2\n",
" # get indices for three slices centered on midslice, skipping dilation d images between the slices\n",
" slice_indices = range(midslice-(1+d),midslice+(1+d+1),1+d)\n",
" slices3 = scanarray[slice_indices,:,:]\n",
" # put channels last for PIL\n",
" slices3 = np.moveaxis(slices3, 0, -1)\n",
" # convert to rgb\n",
" im = Image.fromarray(slices3)\n",
" # save file\n",
" # replace .npy with .png\n",
" pngf = npyf[:-4] + '.png'\n",
" pngfilepath = dirpath + '/' + pngf\n",
" im.save(pngfilepath)\n",
" # remove corresponding .npy file\n",
" os.remove(npyfilepath)\n",
"\n",
" print('===============================') "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Take just middle slice from each scan"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 1130 .npy files in ../data/mid1/train/axial\n",
"===============================\n",
"===============================\n",
"Found 1130 .npy files in ../data/mid1/train/coronal\n",
"===============================\n",
"===============================\n",
"Found 1130 .npy files in ../data/mid1/train/sagittal\n",
"===============================\n",
"===============================\n",
"Found 120 .npy files in ../data/mid1/valid/axial\n",
"===============================\n",
"===============================\n",
"Found 120 .npy files in ../data/mid1/valid/coronal\n",
"===============================\n",
"===============================\n",
"Found 120 .npy files in ../data/mid1/valid/sagittal\n",
"===============================\n",
"===============================\n"
]
}
],
"source": [
"dilation = ''\n",
"for d in ('train','valid'):\n",
" for p in ('axial','coronal','sagittal'):\n",
" dirpath = '../data/mid1{}/{}/{}'.format(dilation, d, p)\n",
" npy_files = [f for f in os.listdir(dirpath) if f[-4:]=='.npy']\n",
" print('Processing {} .npy files in {}'.format(len(npy_files), dirpath))\n",
"\n",
" for f in npy_files:\n",
" filepath = dirpath + '/' + f\n",
" #print('Converting {}'.format(filepath))\n",
" # load the array\n",
" scanarray = np.load(filepath)\n",
" # calculate interpolation factor(s)\n",
" #print('Number of slices for this scan: {}'.format(scanarray.shape[0]))\n",
" midslice = scanarray.shape[0]//2\n",
" im = Image.fromarray(scanarray[midslice,:,:], 'L').convert('RGB')\n",
" # replace .npy with .png\n",
" newf = f[:-4] + '.png'\n",
" newfilepath = dirpath + '/' + newf\n",
" im.save(newfilepath)\n",
" # remove corresponding .npy file\n",
" os.remove(filepath)\n",
" \n",
" print('===============================') "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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