mri_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pydicom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pydicom\n",
    "from pydicom.data import get_testdata_files\n",
    "\n",
    "print(__doc__)\n",
    "\n",
    "filename = get_testdata_files('CT_small.dcm')[0]\n",
    "dataset = pydicom.dcmread(filename)\n",
    "\n",
    "# Normal mode:\n",
    "print()\n",
    "print(\"Filename.........:\", filename)\n",
    "print(\"Storage type.....:\", dataset.SOPClassUID)\n",
    "print()\n",
    "\n",
    "pat_name = dataset.PatientName\n",
    "display_name = pat_name.family_name + \", \" + pat_name.given_name\n",
    "print(\"Patient's name...:\", display_name)\n",
    "print(\"Patient id.......:\", dataset.PatientID)\n",
    "print(\"Modality.........:\", dataset.Modality)\n",
    "print(\"Study Date.......:\", dataset.StudyDate)\n",
    "\n",
    "if 'PixelData' in dataset:\n",
    "    rows = int(dataset.Rows)\n",
    "    cols = int(dataset.Columns)\n",
    "    print(\"Image size.......: {rows:d} x {cols:d}, {size:d} bytes\".format(\n",
    "        rows=rows, cols=cols, size=len(dataset.PixelData)))\n",
    "    if 'PixelSpacing' in dataset:\n",
    "        print(\"Pixel spacing....:\", dataset.PixelSpacing)\n",
    "\n",
    "# use .get() if not sure the item exists, and want a default value if missing\n",
    "print(\"Slice location...:\", dataset.get('SliceLocation', \"(missing)\"))\n",
    "\n",
    "# plot the image using matplotlib\n",
    "plt.imshow(dataset.pixel_array, cmap=plt.cm.bone)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = '/home/ADNI/003_S_2374/Accelerated_Sagittal_MPRAGE/2018-12-14_07_58_40.0/S761375/'\n",
    "filename = 'ADNI_003_S_2374_MR_Accelerated_Sagittal_MPRAGE__br_raw_20181217100115865_158_S761375_I1090114.dcm'\n",
    "dataset = pydicom.dcmread(f'{filepath}/{filename}')\n",
    "\n",
    "# Normal mode:\n",
    "print()\n",
    "print(\"Filename.........:\", filename)\n",
    "print(\"Storage type.....:\", dataset.SOPClassUID)\n",
    "print()\n",
    "\n",
    "pat_name = dataset.PatientName\n",
    "display_name = pat_name.family_name + \", \" + pat_name.given_name\n",
    "print(\"Patient's name...:\", display_name)\n",
    "print(\"Patient id.......:\", dataset.PatientID)\n",
    "print(\"Modality.........:\", dataset.Modality)\n",
    "print(\"Study Date.......:\", dataset.StudyDate)\n",
    "\n",
    "if 'PixelData' in dataset:\n",
    "    rows = int(dataset.Rows)\n",
    "    cols = int(dataset.Columns)\n",
    "    print(\"Image size.......: {rows:d} x {cols:d}, {size:d} bytes\".format(\n",
    "        rows=rows, cols=cols, size=len(dataset.PixelData)))\n",
    "    if 'PixelSpacing' in dataset:\n",
    "        print(\"Pixel spacing....:\", dataset.PixelSpacing)\n",
    "\n",
    "# use .get() if not sure the item exists, and want a default value if missing\n",
    "print(\"Slice location...:\", dataset.get('SliceLocation', \"(missing)\"))\n",
    "\n",
    "# plot the image using matplotlib\n",
    "plt.imshow(dataset.pixel_array, cmap=plt.cm.bone)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "from torch.optim.lr_scheduler import MultiStepLR\n",
    "from torch.optim import Adam, RMSprop, SGD\n",
    "\n",
    "from brainiac.models import *\n",
    "from brainiac.loader import ADNIClassificationDataset\n",
    "from brainiac.model_selection import ManualGroupKFold\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "import torch\n",
    "from torch.utils import data\n",
    "from torch.utils.data import DataLoader\n",
    "from PIL import Image\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = ['AD', 'MCI', 'CN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_groups(df, names):\n",
    "    mask = df['Group'].map(lambda x: x in names)\n",
    "    return df[mask].copy()\n",
    "\n",
    "\n",
    "def encoder_it(df, mapper):\n",
    "    df['Group'] = df['Group'].map(mapper)\n",
    "    df['Subject'] = LabelEncoder().fit_transform(df['Subject'])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_split_df(df, classes, random_state=42):\n",
    "    '''\n",
    "        df: pd.DataFrame\n",
    "        classes: list, e.x. ['CN', 'AD', 'MCI']\n",
    "    '''\n",
    "    mapper = {n:i for i, n in enumerate(classes)}\n",
    "    df = encoder_it(extract_groups(df, classes), mapper)\n",
    "    cv = ManualGroupKFold(n_splits=3, random_state=random_state)\n",
    "    train_idx, test_idx = list(cv.split(df['Image Data ID'], df['Group'].values, df['Subject'].values))[0]\n",
    "    df_train, df_test = df.iloc[train_idx], df.iloc[test_idx]\n",
    "    return df_train, df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/home/ADNI-processed/data.csv')\n",
    "df_train, df_test = train_test_split_df(df, classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ADNIClassificationDataset(data.Dataset):\n",
    "    def __init__(\n",
    "        self, \n",
    "        dataset_csv, \n",
    "        method='train', \n",
    "        target_size=(64, 64), \n",
    "        normalize=True\n",
    "    ):\n",
    "        self.target_size = np.asarray(target_size)\n",
    "        self.dataset_csv = dataset_csv  # pd.read_csv('/home/basimova_nf/ADNI-processed/data.csv')\n",
    "        self.normalize = normalize\n",
    "        self.method = method\n",
    "        self.images_path = '/home/ADNI-processed/images/'\n",
    "\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.dataset_csv)\n",
    "\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        row = self.dataset_csv.iloc[index]\n",
    "        filename = row['File Names']\n",
    "        im_path = f'{self.images_path}{filename}.npy'\n",
    "        im = np.load(im_path, allow_pickle=True)\n",
    "        \n",
    "        if len(im.shape) == 4:\n",
    "            im = im[0]\n",
    "            \n",
    "        if self.normalize:\n",
    "            im = im / im.max()\n",
    "        \n",
    "        new_im = np.zeros((im.shape[0], *self.target_size))\n",
    "        for i, layer in enumerate(im):\n",
    "            layer = Image.fromarray(np.uint8(layer * 255))\n",
    "            layer = layer.resize((self.target_size[1], self.target_size[0]), Image.NEAREST) \n",
    "            new_im[i] = np.array(layer)\n",
    "            \n",
    "        new_im = np.moveaxis(new_im, 0, -1)[None, :]\n",
    "        if self.method == 'test':\n",
    "            return new_im\n",
    "        \n",
    "        # maybe better to delete Patient \n",
    "        return torch.Tensor(new_im), row['Group']  # ,f'{self.images_path}{filename}.npy', index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_mri(index):\n",
    "    images_path = '/home/ADNI-processed/images/'\n",
    "\n",
    "    row = df.iloc[index]\n",
    "    print(row)\n",
    "    filename = row['File Names']\n",
    "    print(filename)\n",
    "    im_path = f'{images_path}{filename}.npy'\n",
    "    print(im_path)\n",
    "    im = np.load(im_path, allow_pickle=True)\n",
    "\n",
    "    print(im.shape)\n",
    "\n",
    "    num_rows = 34\n",
    "    num_cols = 5\n",
    "    max_images = num_rows * num_cols\n",
    "    plt.figure(figsize=(20, 200))\n",
    "    for i in range(max_images):\n",
    "        plt.subplot(num_rows, num_cols, i+1)\n",
    "        plt.imshow(im[i,:,:], cmap='gray')\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_mri(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals, counts = np.unique(df['Description'], return_counts=True)\n",
    "list(zip(vals, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_mri_special(row):\n",
    "    images_path = '/home/ADNI-processed/images/'\n",
    "\n",
    "    filename = row['File Names']\n",
    "    print(filename)\n",
    "    im_path = f'{images_path}{filename}.npy'\n",
    "    print(im_path)\n",
    "    im = np.load(im_path, allow_pickle=True)\n",
    "\n",
    "    print(im.shape)\n",
    "\n",
    "    num_rows = 48\n",
    "    num_cols = 5\n",
    "    max_images = num_rows * num_cols\n",
    "    plt.figure(figsize=(20, 200))\n",
    "    for i in range(max_images):\n",
    "        plt.subplot(num_rows, num_cols, i+1)\n",
    "        plt.imshow(im[:,:,i], cmap='gray')\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type1 = df[df['Description'] == 'Accelerated Sag IR-FSPGR'].iloc[0]\n",
    "show_mri_special(type1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type2 = df[df['Description'] == 'Accelerated Sagittal IR-FSPGR'].iloc[0]\n",
    "show_mri_special(type2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type3 = df[df['Description'] == 'Accelerated Sagittal MPRAGE'].iloc[0]\n",
    "show_mri_special(type3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type4 = df[df['Description'] == 'Accelerated Sagittal MPRAGE_ND'].iloc[0]\n",
    "show_mri_special(type4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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