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MRNet_EDA_ns.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from fastai.core import *\n",
"\n",
"%matplotlib notebook"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LICENSE MRNet_EDA.ipynb README.md\r\n"
]
}
],
"source": [
"! ls -R "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"data_path = Path('../data')\n",
"train_path = data_path/'smalltrain'/'train'\n",
"valid_path = data_path/'smallvalid'/'valid'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Case\n",
"Abnormal \n",
"0 217\n",
"1 913\n"
]
},
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Case</th>\n",
" <th>Abnormal</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0000</td>\n",
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"text/plain": [
" Case Abnormal\n",
"0 0000 1\n",
"1 0001 1\n",
"2 0002 1\n",
"3 0003 1\n",
"4 0004 1"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_abnl = pd.read_csv(data_path/'train-abnormal.csv', header=None,\n",
" names=['Case', 'Abnormal'], \n",
" dtype={'Case': str, 'Abnormal': np.int64})\n",
"print(train_abnl.groupby('Abnormal').count())\n",
"train_abnl.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Case\n",
"ACL_tear \n",
"0 922\n",
"1 208\n"
]
},
{
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Case</th>\n",
" <th>ACL_tear</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>0001</td>\n",
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"</div>"
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"text/plain": [
" Case ACL_tear\n",
"0 0000 0\n",
"1 0001 1\n",
"2 0002 0\n",
"3 0003 0\n",
"4 0004 0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_acl = pd.read_csv(data_path/'train-acl.csv', header=None,\n",
" names=['Case', 'ACL_tear'], \n",
" dtype={'Case': str, 'ACL_tear': np.int64})\n",
"print(train_acl.groupby('ACL_tear').count())\n",
"train_acl.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Case\n",
"Meniscus_tear \n",
"0 733\n",
"1 397\n"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Case</th>\n",
" <th>Meniscus_tear</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0000</td>\n",
" <td>0</td>\n",
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" <th>3</th>\n",
" <td>0003</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>0004</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Case Meniscus_tear\n",
"0 0000 0\n",
"1 0001 1\n",
"2 0002 0\n",
"3 0003 1\n",
"4 0004 0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_meniscus = pd.read_csv(data_path/'train-meniscus.csv', header=None,\n",
" names=['Case', 'Meniscus_tear'], \n",
" dtype={'Case': str, 'Meniscus_tear': np.int64})\n",
"print(train_meniscus.groupby('Meniscus_tear').count())\n",
"train_meniscus.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Co-occurrence of ACL and Meniscus tears"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train = pd.merge(train_abnl, train_acl, on='Case')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"train = pd.merge(train, train_meniscus, on='Case')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>Case</th>\n",
" <th>Abnormal</th>\n",
" <th>ACL_tear</th>\n",
" <th>Meniscus_tear</th>\n",
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" <th>3</th>\n",
" <td>0003</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0004</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Case Abnormal ACL_tear Meniscus_tear\n",
"0 0000 1 0 0\n",
"1 0001 1 1 1\n",
"2 0002 1 0 0\n",
"3 0003 1 0 1\n",
"4 0004 1 0 0"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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" <th></th>\n",
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" <th></th>\n",
" <th>Case</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Abnormal</th>\n",
" <th>ACL_tear</th>\n",
" <th>Meniscus_tear</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
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" <th>0</th>\n",
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"text/plain": [
" Case\n",
"Abnormal ACL_tear Meniscus_tear \n",
"0 0 0 217\n",
"1 0 0 433\n",
" 1 272\n",
" 1 0 83\n",
" 1 125"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(train.head())\n",
"display(train.groupby(['Abnormal','ACL_tear','Meniscus_tear']).count())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that cases considered Abnormal but without either ACL or Meniscus tear are the most common category, and ACL tears without Meniscus tear is the least common case in the training sample."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load stacks/sequences of images from each plane\n",
"Files are saved as NumPy arrays. Scans were taken from each of three planes, axial, coronal, and sagittal. For each plane, the scan results in a set of images. \n",
"\n",
"First, let's check for variation in the number of images per sequence, and in the image dimensions."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"def collect_stack_dims(case_df, data_path=train_path):\n",
" cases = list(case_df.Case)\n",
" data = []\n",
" for case in cases:\n",
" row = [case]\n",
" for plane in ['axial', 'coronal', 'sagittal']:\n",
" fpath = data_path/plane/'{}.npy'.format(case)\n",
" try: \n",
" s,w,h = np.load(fpath).shape \n",
" row.extend([s,w,h])\n",
" except FileNotFoundError:\n",
" continue\n",
"# print('{}: {}'.format(case,row))\n",
" if len(row)==10: data.append(row)\n",
" columns=['Case',\n",
" 'axial_s','axial_w','axial_h',\n",
" 'coronal_s','coronal_w','coronal_h',\n",
" 'sagittal_s','sagittal_w','sagittal_h',\n",
" ]\n",
" data_dict = {}\n",
" for i,k in enumerate(columns): data_dict[k] = [row[i] for row in data]\n",
" return pd.DataFrame(data_dict)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"dimdf = collect_stack_dims(train)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>axial_s</th>\n",
" <th>axial_w</th>\n",
" <th>axial_h</th>\n",
" <th>coronal_s</th>\n",
" <th>coronal_w</th>\n",
" <th>coronal_h</th>\n",
" <th>sagittal_s</th>\n",
" <th>sagittal_w</th>\n",
" <th>sagittal_h</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>50.000000</td>\n",
" <td>50.0</td>\n",
" <td>50.0</td>\n",
" <td>50.000000</td>\n",
" <td>50.0</td>\n",
" <td>50.0</td>\n",
" <td>50.00000</td>\n",
" <td>50.0</td>\n",
" <td>50.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>35.860000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" <td>31.360000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" <td>31.72000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.050865</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>7.899264</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.35687</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>22.000000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" <td>18.000000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" <td>19.00000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>32.000000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" <td>24.000000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" <td>26.25000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>37.500000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" <td>32.000000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" <td>32.00000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>40.000000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" <td>37.750000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" <td>36.00000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>51.000000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" <td>46.000000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" <td>46.00000</td>\n",
" <td>256.0</td>\n",
" <td>256.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" axial_s axial_w axial_h coronal_s coronal_w coronal_h \\\n",
"count 50.000000 50.0 50.0 50.000000 50.0 50.0 \n",
"mean 35.860000 256.0 256.0 31.360000 256.0 256.0 \n",
"std 7.050865 0.0 0.0 7.899264 0.0 0.0 \n",
"min 22.000000 256.0 256.0 18.000000 256.0 256.0 \n",
"25% 32.000000 256.0 256.0 24.000000 256.0 256.0 \n",
"50% 37.500000 256.0 256.0 32.000000 256.0 256.0 \n",
"75% 40.000000 256.0 256.0 37.750000 256.0 256.0 \n",
"max 51.000000 256.0 256.0 46.000000 256.0 256.0 \n",
"\n",
" sagittal_s sagittal_w sagittal_h \n",
"count 50.00000 50.0 50.0 \n",
"mean 31.72000 256.0 256.0 \n",
"std 6.35687 0.0 0.0 \n",
"min 19.00000 256.0 256.0 \n",
"25% 26.25000 256.0 256.0 \n",
"50% 32.00000 256.0 256.0 \n",
"75% 36.00000 256.0 256.0 \n",
"max 46.00000 256.0 256.0 "
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dimdf.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The number of images in a set varies from case (patient) to case, and the dimensions of each image is the same, 256x256. In the sample of data collected here, axial sequences range in length from 22 to 51; coronal, from 18 to 46; sagittal, from 19 to 46."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"def load_one_stack(case, data_path=train_path, plane='coronal'):\n",
" fpath = data_path/plane/'{}.npy'.format(case)\n",
" return np.load(fpath)\n",
"\n",
"def load_stacks(case):\n",
" x = {}\n",
" planes = ['axial', 'coronal', 'sagittal']\n",
" for i, plane in enumerate(planes):\n",
" x[plane] = load_one_stack(case, plane=plane)\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(36, 256, 256)\n",
"255\n"
]
}
],
"source": [
"case = train_abnl.Case[0]\n",
"x = load_one_stack(case, plane='coronal')\n",
"print(x.shape)\n",
"print(x.max())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'axial': array([[[ 0, 0, 0, 0, ..., 4, 5, 4, 3],\n",
" [ 0, 0, 0, 0, ..., 8, 8, 6, 8],\n",
" [ 0, 0, 0, 0, ..., 14, 14, 11, 11],\n",
" [ 0, 0, 0, 0, ..., 16, 16, 14, 15],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 14, 15, 18, 16],\n",
" [ 0, 0, 0, 0, ..., 15, 16, 15, 12],\n",
" [ 0, 0, 0, 0, ..., 11, 12, 13, 12],\n",
" [ 0, 0, 0, 0, ..., 8, 11, 7, 9]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 4, 3, 2, 2],\n",
" [ 0, 0, 0, 0, ..., 5, 9, 7, 7],\n",
" [ 0, 0, 0, 0, ..., 10, 13, 10, 10],\n",
" [ 0, 0, 0, 0, ..., 14, 14, 19, 17],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 18, 16, 16, 17],\n",
" [ 0, 0, 0, 0, ..., 13, 12, 15, 13],\n",
" [ 0, 0, 0, 0, ..., 16, 14, 12, 12],\n",
" [ 0, 0, 0, 0, ..., 8, 6, 5, 7]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 7, 8, 6, 6],\n",
" [ 0, 0, 0, 0, ..., 12, 11, 13, 10],\n",
" [ 0, 0, 0, 0, ..., 12, 18, 18, 16],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 16, 18, 16, 17],\n",
" [ 0, 0, 0, 0, ..., 15, 13, 13, 16],\n",
" [ 0, 0, 0, 0, ..., 10, 10, 10, 12],\n",
" [ 0, 0, 0, 0, ..., 6, 6, 6, 5]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, ..., 5, 7, 5, 4],\n",
" [ 0, 0, 0, 0, ..., 11, 10, 11, 12],\n",
" [ 0, 0, 0, 0, ..., 16, 16, 15, 14],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 17, 21, 20, 18],\n",
" [ 0, 0, 0, 0, ..., 14, 15, 18, 14],\n",
" [ 0, 0, 0, 0, ..., 11, 9, 8, 10],\n",
" [ 0, 0, 0, 0, ..., 5, 5, 5, 5]],\n",
" \n",
" ...,\n",
" \n",
" [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, ..., 4, 4, 4, 4],\n",
" [ 0, 0, 0, 0, ..., 10, 9, 9, 10],\n",
" [ 0, 0, 0, 0, ..., 12, 13, 16, 14],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 16, 12, 14, 15],\n",
" [ 0, 0, 0, 0, ..., 11, 10, 13, 10],\n",
" [ 0, 0, 0, 0, ..., 9, 12, 9, 9],\n",
" [ 0, 0, 0, 0, ..., 6, 6, 4, 5]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 5, 5, 4, 4],\n",
" [ 0, 0, 0, 0, ..., 12, 8, 9, 11],\n",
" [ 0, 0, 0, 0, ..., 16, 17, 12, 13],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 13, 14, 14, 17],\n",
" [ 0, 0, 0, 0, ..., 12, 14, 16, 13],\n",
" [ 0, 0, 0, 0, ..., 9, 12, 12, 8],\n",
" [ 0, 0, 0, 0, ..., 6, 5, 4, 5]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 3, 2, 3, 2],\n",
" [ 0, 0, 0, 0, ..., 5, 4, 5, 6],\n",
" [ 0, 0, 0, 0, ..., 13, 11, 11, 9],\n",
" [ 0, 0, 0, 0, ..., 13, 13, 16, 16],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 17, 17, 14, 16],\n",
" [ 0, 0, 0, 0, ..., 16, 14, 16, 15],\n",
" [ 0, 0, 0, 0, ..., 9, 8, 10, 8],\n",
" [ 0, 0, 0, 0, ..., 6, 6, 6, 6]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 5, 4, 4, 4],\n",
" [ 0, 0, 0, 0, ..., 9, 8, 7, 6],\n",
" [ 0, 0, 0, 0, ..., 11, 9, 10, 11],\n",
" [ 0, 0, 0, 0, ..., 17, 15, 12, 13],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 12, 16, 16, 15],\n",
" [ 0, 0, 0, 0, ..., 16, 12, 15, 16],\n",
" [ 0, 0, 0, 0, ..., 10, 14, 12, 12],\n",
" [ 0, 0, 0, 0, ..., 6, 8, 7, 7]]], dtype=uint8),\n",
" 'coronal': array([[[ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 2, 2, 1, 2],\n",
" [ 0, 0, 0, 0, ..., 3, 3, 1, 2],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 2, 3, 2, 2],\n",
" [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n",
" [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 1, 0]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 1, 1, 1, 0],\n",
" [ 0, 0, 0, 0, ..., 2, 1, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n",
" [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 3, 3, 2, 2],\n",
" [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n",
" [ 0, 0, 0, 0, ..., 1, 1, 1, 2],\n",
" [ 0, 0, 0, 0, ..., 0, 1, 1, 1]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 1, 1, 0, 1],\n",
" [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 2, 2, 1, 2],\n",
" [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 3, 2, 1, 2],\n",
" [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n",
" [ 0, 0, 0, 0, ..., 2, 1, 1, 2],\n",
" [ 0, 0, 0, 0, ..., 1, 1, 1, 1]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 2, 1, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n",
" [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 2, 3, 2, 2],\n",
" [ 0, 0, 0, 0, ..., 3, 2, 1, 2],\n",
" [ 0, 0, 0, 0, ..., 2, 1, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 1, 1, 1, 1]],\n",
" \n",
" ...,\n",
" \n",
" [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, ..., 1, 1, 2, 2],\n",
" [ 0, 0, 0, 0, ..., 3, 3, 3, 3],\n",
" [ 0, 0, 0, 0, ..., 5, 4, 4, 5],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 5, 5, 4, 5],\n",
" [ 0, 0, 0, 0, ..., 3, 3, 3, 3],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 0, 1],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 1, 0, 0, 1],\n",
" [ 0, 0, 0, 0, ..., 2, 2, 1, 2],\n",
" [ 0, 0, 0, 0, ..., 4, 5, 4, 4],\n",
" [ 0, 0, 0, 0, ..., 7, 8, 8, 7],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 7, 7, 6, 6],\n",
" [ 0, 0, 0, 0, ..., 5, 3, 3, 5],\n",
" [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 0, 0, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 2, 3, 3, 3],\n",
" [ 0, 0, 0, 0, ..., 4, 4, 5, 4],\n",
" [ 0, 0, 0, 0, ..., 9, 6, 8, 10],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 8, 9, 8, 11],\n",
" [ 0, 0, 0, 0, ..., 5, 6, 3, 4],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 1, 0],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n",
" [ 0, 0, 0, 0, ..., 3, 3, 5, 5],\n",
" [ 0, 0, 0, 0, ..., 9, 10, 7, 6],\n",
" ...,\n",
" [ 0, 0, 0, 0, ..., 9, 9, 9, 8],\n",
" [ 0, 0, 0, 0, ..., 5, 5, 4, 4],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 0, 0]]], dtype=uint8),\n",
" 'sagittal': array([[[ 0, 0, 0, 0, ..., 2, 1, 1, 2],\n",
" [ 0, 0, 0, 0, ..., 8, 8, 8, 7],\n",
" [ 0, 0, 0, 0, ..., 11, 15, 16, 14],\n",
" [ 7, 5, 5, 7, ..., 15, 14, 19, 16],\n",
" ...,\n",
" [ 7, 5, 7, 5, ..., 15, 17, 15, 14],\n",
" [ 0, 1, 1, 1, ..., 10, 15, 12, 11],\n",
" [ 0, 0, 0, 0, ..., 7, 7, 6, 5],\n",
" [ 0, 0, 0, 0, ..., 3, 2, 2, 1]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 4, 3, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 10, 9, 7, 8],\n",
" [ 0, 0, 0, 0, ..., 17, 17, 15, 17],\n",
" [ 7, 5, 5, 6, ..., 20, 22, 21, 17],\n",
" ...,\n",
" [ 6, 7, 6, 4, ..., 18, 14, 19, 19],\n",
" [ 0, 1, 0, 2, ..., 16, 13, 15, 16],\n",
" [ 0, 0, 0, 0, ..., 10, 8, 5, 6],\n",
" [ 0, 0, 0, 0, ..., 2, 1, 1, 4]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 3, 4, 3, 4],\n",
" [ 0, 0, 0, 0, ..., 9, 12, 9, 10],\n",
" [ 0, 0, 0, 0, ..., 19, 20, 12, 11],\n",
" [ 7, 6, 7, 6, ..., 19, 17, 13, 16],\n",
" ...,\n",
" [ 3, 6, 6, 4, ..., 27, 27, 14, 19],\n",
" [ 2, 3, 1, 1, ..., 23, 19, 14, 15],\n",
" [ 0, 0, 0, 0, ..., 9, 4, 11, 9],\n",
" [ 0, 0, 0, 0, ..., 2, 1, 2, 3]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 3, 6, 4, 3],\n",
" [ 0, 0, 0, 0, ..., 8, 10, 8, 10],\n",
" [ 0, 0, 0, 0, ..., 21, 18, 14, 18],\n",
" [ 5, 9, 6, 6, ..., 26, 19, 18, 22],\n",
" ...,\n",
" [ 6, 4, 6, 9, ..., 22, 23, 24, 25],\n",
" [ 1, 1, 2, 1, ..., 14, 17, 19, 17],\n",
" [ 0, 0, 0, 0, ..., 10, 8, 7, 9],\n",
" [ 0, 0, 0, 0, ..., 2, 2, 2, 3]],\n",
" \n",
" ...,\n",
" \n",
" [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, ..., 3, 3, 3, 2],\n",
" [ 0, 0, 0, 0, ..., 10, 8, 10, 10],\n",
" ...,\n",
" [ 0, 0, 0, 3, ..., 7, 10, 8, 7],\n",
" [ 0, 0, 0, 0, ..., 4, 4, 5, 4],\n",
" [ 0, 0, 0, 0, ..., 2, 2, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, ..., 3, 3, 3, 2],\n",
" [ 0, 0, 0, 0, ..., 10, 11, 9, 11],\n",
" ...,\n",
" [ 0, 0, 0, 4, ..., 10, 9, 10, 9],\n",
" [ 0, 0, 0, 1, ..., 5, 6, 5, 6],\n",
" [ 0, 0, 0, 0, ..., 3, 2, 1, 2],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, ..., 3, 2, 2, 2],\n",
" [ 0, 0, 0, 1, ..., 11, 12, 11, 9],\n",
" ...,\n",
" [ 0, 0, 0, 3, ..., 9, 11, 10, 9],\n",
" [ 0, 0, 0, 1, ..., 5, 6, 6, 5],\n",
" [ 0, 0, 0, 0, ..., 2, 3, 2, 1],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n",
" \n",
" [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n",
" [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 8, 10, 8, 9],\n",
" ...,\n",
" [ 0, 0, 0, 2, ..., 7, 9, 8, 8],\n",
" [ 0, 0, 0, 0, ..., 4, 4, 4, 5],\n",
" [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n",
" [ 0, 0, 0, 0, ..., 0, 0, 0, 0]]], dtype=uint8)}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_multi = load_stacks(case)\n",
"x_multi"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"from ipywidgets import interactive\n",
"from IPython.display import display\n",
"\n",
"plt.style.use('grayscale')\n",
"\n",
"class KneePlot():\n",
" def __init__(self, x, figsize=(10, 10)):\n",
" self.x = x\n",
" self.slice_range = (0, self.x.shape[0] - 1)\n",
" self.resize(figsize)\n",
" \n",
" def _plot_slice(self, im_slice):\n",
" fig, ax = plt.subplots(1, 1, figsize=self.figsize)\n",
" ax.imshow(self.x[im_slice, :, :])\n",
" plt.show()\n",
"\n",
" def resize(self, figsize):\n",
" self.figsize = figsize\n",
" self.interactive_plot = interactive(self._plot_slice, im_slice=self.slice_range)\n",
" self.output = self.interactive_plot.children[-1]\n",
" self.output.layout.height = '{}px'.format(60 * self.figsize[1])\n",
"\n",
" def show(self):\n",
" display(self.interactive_plot)\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bb94b9e5a31b44c5abd287a8cdb12fe9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=17, description='im_slice', max=35), Output(layout=Layout(height='600px'…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot = KneePlot(x)\n",
"plot.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4d55829c45294dbd90f82665f799a8ca",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=17, description='im_slice', max=35), Output(layout=Layout(height='720px'…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot.resize(figsize=(12, 12))\n",
"plot.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from ipywidgets import interact, Dropdown, IntSlider\n",
"\n",
"class MultiKneePlot():\n",
" def __init__(self, x_multi, figsize=(10, 10)):\n",
" self.x = x_multi\n",
" self.planes = ['coronal', 'sagittal', 'axial']\n",
" self.slice_nums = {plane: self.x[plane].shape[0] for plane in self.planes}\n",
" self.figsize = figsize\n",
" \n",
" def _plot_slices(self, plane, im_slice): \n",
" fig, ax = plt.subplots(1, 1, figsize=self.figsize)\n",
" ax.imshow(self.x[plane][im_slice, :, :])\n",
" plt.show()\n",
" \n",
" def draw(self):\n",
" planes_widget = Dropdown(options=self.planes)\n",
" plane_init = self.planes[0]\n",
" slice_init = self.slice_nums[plane_init] - 1\n",
" slices_widget = IntSlider(min=0, max=slice_init, value=slice_init//2)\n",
" def update_slices_widget(*args):\n",
" slices_widget.max = self.slice_nums[planes_widget.value] - 1\n",
" slices_widget.value = slices_widget.max // 2\n",
" planes_widget.observe(update_slices_widget, 'value')\n",
" interact(self._plot_slices, plane=planes_widget, im_slice=slices_widget)\n",
" \n",
" def resize(self, figsize): self.figsize = figsize\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0d7a1c151c3440c59bfa0a2b55e6180e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(Dropdown(description='plane', options=('coronal', 'sagittal', 'axial'), value='coronal')…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_multi = MultiKneePlot(x_multi)\n",
"plot_multi.draw()"
]
},
{
"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",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}