Datasets
Models
Downloads: 1
Diff of
/MRNet_EDA_ns.ipynb
[000000]
..
[dc3c86]
Switch to unified view
| a | b/MRNet_EDA_ns.ipynb | ||
|---|---|---|---|
| 1 | { |
||
| 2 | "cells": [ |
||
| 3 | { |
||
| 4 | "cell_type": "code", |
||
| 5 | "execution_count": 1, |
||
| 6 | "metadata": {}, |
||
| 7 | "outputs": [], |
||
| 8 | "source": [ |
||
| 9 | "import numpy as np\n", |
||
| 10 | "import pandas as pd\n", |
||
| 11 | "import matplotlib.pyplot as plt\n", |
||
| 12 | "from fastai.core import *\n", |
||
| 13 | "\n", |
||
| 14 | "%matplotlib notebook" |
||
| 15 | ] |
||
| 16 | }, |
||
| 17 | { |
||
| 18 | "cell_type": "code", |
||
| 19 | "execution_count": 2, |
||
| 20 | "metadata": {}, |
||
| 21 | "outputs": [ |
||
| 22 | { |
||
| 23 | "name": "stdout", |
||
| 24 | "output_type": "stream", |
||
| 25 | "text": [ |
||
| 26 | "LICENSE MRNet_EDA.ipynb README.md\r\n" |
||
| 27 | ] |
||
| 28 | } |
||
| 29 | ], |
||
| 30 | "source": [ |
||
| 31 | "! ls -R " |
||
| 32 | ] |
||
| 33 | }, |
||
| 34 | { |
||
| 35 | "cell_type": "code", |
||
| 36 | "execution_count": 3, |
||
| 37 | "metadata": {}, |
||
| 38 | "outputs": [], |
||
| 39 | "source": [ |
||
| 40 | "data_path = Path('../data')\n", |
||
| 41 | "train_path = data_path/'smalltrain'/'train'\n", |
||
| 42 | "valid_path = data_path/'smallvalid'/'valid'" |
||
| 43 | ] |
||
| 44 | }, |
||
| 45 | { |
||
| 46 | "cell_type": "code", |
||
| 47 | "execution_count": 5, |
||
| 48 | "metadata": {}, |
||
| 49 | "outputs": [ |
||
| 50 | { |
||
| 51 | "name": "stdout", |
||
| 52 | "output_type": "stream", |
||
| 53 | "text": [ |
||
| 54 | " Case\n", |
||
| 55 | "Abnormal \n", |
||
| 56 | "0 217\n", |
||
| 57 | "1 913\n" |
||
| 58 | ] |
||
| 59 | }, |
||
| 60 | { |
||
| 61 | "data": { |
||
| 62 | "text/html": [ |
||
| 63 | "<div>\n", |
||
| 64 | "<style scoped>\n", |
||
| 65 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 66 | " vertical-align: middle;\n", |
||
| 67 | " }\n", |
||
| 68 | "\n", |
||
| 69 | " .dataframe tbody tr th {\n", |
||
| 70 | " vertical-align: top;\n", |
||
| 71 | " }\n", |
||
| 72 | "\n", |
||
| 73 | " .dataframe thead th {\n", |
||
| 74 | " text-align: right;\n", |
||
| 75 | " }\n", |
||
| 76 | "</style>\n", |
||
| 77 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 78 | " <thead>\n", |
||
| 79 | " <tr style=\"text-align: right;\">\n", |
||
| 80 | " <th></th>\n", |
||
| 81 | " <th>Case</th>\n", |
||
| 82 | " <th>Abnormal</th>\n", |
||
| 83 | " </tr>\n", |
||
| 84 | " </thead>\n", |
||
| 85 | " <tbody>\n", |
||
| 86 | " <tr>\n", |
||
| 87 | " <th>0</th>\n", |
||
| 88 | " <td>0000</td>\n", |
||
| 89 | " <td>1</td>\n", |
||
| 90 | " </tr>\n", |
||
| 91 | " <tr>\n", |
||
| 92 | " <th>1</th>\n", |
||
| 93 | " <td>0001</td>\n", |
||
| 94 | " <td>1</td>\n", |
||
| 95 | " </tr>\n", |
||
| 96 | " <tr>\n", |
||
| 97 | " <th>2</th>\n", |
||
| 98 | " <td>0002</td>\n", |
||
| 99 | " <td>1</td>\n", |
||
| 100 | " </tr>\n", |
||
| 101 | " <tr>\n", |
||
| 102 | " <th>3</th>\n", |
||
| 103 | " <td>0003</td>\n", |
||
| 104 | " <td>1</td>\n", |
||
| 105 | " </tr>\n", |
||
| 106 | " <tr>\n", |
||
| 107 | " <th>4</th>\n", |
||
| 108 | " <td>0004</td>\n", |
||
| 109 | " <td>1</td>\n", |
||
| 110 | " </tr>\n", |
||
| 111 | " </tbody>\n", |
||
| 112 | "</table>\n", |
||
| 113 | "</div>" |
||
| 114 | ], |
||
| 115 | "text/plain": [ |
||
| 116 | " Case Abnormal\n", |
||
| 117 | "0 0000 1\n", |
||
| 118 | "1 0001 1\n", |
||
| 119 | "2 0002 1\n", |
||
| 120 | "3 0003 1\n", |
||
| 121 | "4 0004 1" |
||
| 122 | ] |
||
| 123 | }, |
||
| 124 | "execution_count": 5, |
||
| 125 | "metadata": {}, |
||
| 126 | "output_type": "execute_result" |
||
| 127 | } |
||
| 128 | ], |
||
| 129 | "source": [ |
||
| 130 | "train_abnl = pd.read_csv(data_path/'train-abnormal.csv', header=None,\n", |
||
| 131 | " names=['Case', 'Abnormal'], \n", |
||
| 132 | " dtype={'Case': str, 'Abnormal': np.int64})\n", |
||
| 133 | "print(train_abnl.groupby('Abnormal').count())\n", |
||
| 134 | "train_abnl.head()" |
||
| 135 | ] |
||
| 136 | }, |
||
| 137 | { |
||
| 138 | "cell_type": "code", |
||
| 139 | "execution_count": 6, |
||
| 140 | "metadata": {}, |
||
| 141 | "outputs": [ |
||
| 142 | { |
||
| 143 | "name": "stdout", |
||
| 144 | "output_type": "stream", |
||
| 145 | "text": [ |
||
| 146 | " Case\n", |
||
| 147 | "ACL_tear \n", |
||
| 148 | "0 922\n", |
||
| 149 | "1 208\n" |
||
| 150 | ] |
||
| 151 | }, |
||
| 152 | { |
||
| 153 | "data": { |
||
| 154 | "text/html": [ |
||
| 155 | "<div>\n", |
||
| 156 | "<style scoped>\n", |
||
| 157 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 158 | " vertical-align: middle;\n", |
||
| 159 | " }\n", |
||
| 160 | "\n", |
||
| 161 | " .dataframe tbody tr th {\n", |
||
| 162 | " vertical-align: top;\n", |
||
| 163 | " }\n", |
||
| 164 | "\n", |
||
| 165 | " .dataframe thead th {\n", |
||
| 166 | " text-align: right;\n", |
||
| 167 | " }\n", |
||
| 168 | "</style>\n", |
||
| 169 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 170 | " <thead>\n", |
||
| 171 | " <tr style=\"text-align: right;\">\n", |
||
| 172 | " <th></th>\n", |
||
| 173 | " <th>Case</th>\n", |
||
| 174 | " <th>ACL_tear</th>\n", |
||
| 175 | " </tr>\n", |
||
| 176 | " </thead>\n", |
||
| 177 | " <tbody>\n", |
||
| 178 | " <tr>\n", |
||
| 179 | " <th>0</th>\n", |
||
| 180 | " <td>0000</td>\n", |
||
| 181 | " <td>0</td>\n", |
||
| 182 | " </tr>\n", |
||
| 183 | " <tr>\n", |
||
| 184 | " <th>1</th>\n", |
||
| 185 | " <td>0001</td>\n", |
||
| 186 | " <td>1</td>\n", |
||
| 187 | " </tr>\n", |
||
| 188 | " <tr>\n", |
||
| 189 | " <th>2</th>\n", |
||
| 190 | " <td>0002</td>\n", |
||
| 191 | " <td>0</td>\n", |
||
| 192 | " </tr>\n", |
||
| 193 | " <tr>\n", |
||
| 194 | " <th>3</th>\n", |
||
| 195 | " <td>0003</td>\n", |
||
| 196 | " <td>0</td>\n", |
||
| 197 | " </tr>\n", |
||
| 198 | " <tr>\n", |
||
| 199 | " <th>4</th>\n", |
||
| 200 | " <td>0004</td>\n", |
||
| 201 | " <td>0</td>\n", |
||
| 202 | " </tr>\n", |
||
| 203 | " </tbody>\n", |
||
| 204 | "</table>\n", |
||
| 205 | "</div>" |
||
| 206 | ], |
||
| 207 | "text/plain": [ |
||
| 208 | " Case ACL_tear\n", |
||
| 209 | "0 0000 0\n", |
||
| 210 | "1 0001 1\n", |
||
| 211 | "2 0002 0\n", |
||
| 212 | "3 0003 0\n", |
||
| 213 | "4 0004 0" |
||
| 214 | ] |
||
| 215 | }, |
||
| 216 | "execution_count": 6, |
||
| 217 | "metadata": {}, |
||
| 218 | "output_type": "execute_result" |
||
| 219 | } |
||
| 220 | ], |
||
| 221 | "source": [ |
||
| 222 | "train_acl = pd.read_csv(data_path/'train-acl.csv', header=None,\n", |
||
| 223 | " names=['Case', 'ACL_tear'], \n", |
||
| 224 | " dtype={'Case': str, 'ACL_tear': np.int64})\n", |
||
| 225 | "print(train_acl.groupby('ACL_tear').count())\n", |
||
| 226 | "train_acl.head()" |
||
| 227 | ] |
||
| 228 | }, |
||
| 229 | { |
||
| 230 | "cell_type": "code", |
||
| 231 | "execution_count": 7, |
||
| 232 | "metadata": {}, |
||
| 233 | "outputs": [ |
||
| 234 | { |
||
| 235 | "name": "stdout", |
||
| 236 | "output_type": "stream", |
||
| 237 | "text": [ |
||
| 238 | " Case\n", |
||
| 239 | "Meniscus_tear \n", |
||
| 240 | "0 733\n", |
||
| 241 | "1 397\n" |
||
| 242 | ] |
||
| 243 | }, |
||
| 244 | { |
||
| 245 | "data": { |
||
| 246 | "text/html": [ |
||
| 247 | "<div>\n", |
||
| 248 | "<style scoped>\n", |
||
| 249 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 250 | " vertical-align: middle;\n", |
||
| 251 | " }\n", |
||
| 252 | "\n", |
||
| 253 | " .dataframe tbody tr th {\n", |
||
| 254 | " vertical-align: top;\n", |
||
| 255 | " }\n", |
||
| 256 | "\n", |
||
| 257 | " .dataframe thead th {\n", |
||
| 258 | " text-align: right;\n", |
||
| 259 | " }\n", |
||
| 260 | "</style>\n", |
||
| 261 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 262 | " <thead>\n", |
||
| 263 | " <tr style=\"text-align: right;\">\n", |
||
| 264 | " <th></th>\n", |
||
| 265 | " <th>Case</th>\n", |
||
| 266 | " <th>Meniscus_tear</th>\n", |
||
| 267 | " </tr>\n", |
||
| 268 | " </thead>\n", |
||
| 269 | " <tbody>\n", |
||
| 270 | " <tr>\n", |
||
| 271 | " <th>0</th>\n", |
||
| 272 | " <td>0000</td>\n", |
||
| 273 | " <td>0</td>\n", |
||
| 274 | " </tr>\n", |
||
| 275 | " <tr>\n", |
||
| 276 | " <th>1</th>\n", |
||
| 277 | " <td>0001</td>\n", |
||
| 278 | " <td>1</td>\n", |
||
| 279 | " </tr>\n", |
||
| 280 | " <tr>\n", |
||
| 281 | " <th>2</th>\n", |
||
| 282 | " <td>0002</td>\n", |
||
| 283 | " <td>0</td>\n", |
||
| 284 | " </tr>\n", |
||
| 285 | " <tr>\n", |
||
| 286 | " <th>3</th>\n", |
||
| 287 | " <td>0003</td>\n", |
||
| 288 | " <td>1</td>\n", |
||
| 289 | " </tr>\n", |
||
| 290 | " <tr>\n", |
||
| 291 | " <th>4</th>\n", |
||
| 292 | " <td>0004</td>\n", |
||
| 293 | " <td>0</td>\n", |
||
| 294 | " </tr>\n", |
||
| 295 | " </tbody>\n", |
||
| 296 | "</table>\n", |
||
| 297 | "</div>" |
||
| 298 | ], |
||
| 299 | "text/plain": [ |
||
| 300 | " Case Meniscus_tear\n", |
||
| 301 | "0 0000 0\n", |
||
| 302 | "1 0001 1\n", |
||
| 303 | "2 0002 0\n", |
||
| 304 | "3 0003 1\n", |
||
| 305 | "4 0004 0" |
||
| 306 | ] |
||
| 307 | }, |
||
| 308 | "execution_count": 7, |
||
| 309 | "metadata": {}, |
||
| 310 | "output_type": "execute_result" |
||
| 311 | } |
||
| 312 | ], |
||
| 313 | "source": [ |
||
| 314 | "train_meniscus = pd.read_csv(data_path/'train-meniscus.csv', header=None,\n", |
||
| 315 | " names=['Case', 'Meniscus_tear'], \n", |
||
| 316 | " dtype={'Case': str, 'Meniscus_tear': np.int64})\n", |
||
| 317 | "print(train_meniscus.groupby('Meniscus_tear').count())\n", |
||
| 318 | "train_meniscus.head()" |
||
| 319 | ] |
||
| 320 | }, |
||
| 321 | { |
||
| 322 | "cell_type": "markdown", |
||
| 323 | "metadata": {}, |
||
| 324 | "source": [ |
||
| 325 | "### Co-occurrence of ACL and Meniscus tears" |
||
| 326 | ] |
||
| 327 | }, |
||
| 328 | { |
||
| 329 | "cell_type": "code", |
||
| 330 | "execution_count": null, |
||
| 331 | "metadata": {}, |
||
| 332 | "outputs": [], |
||
| 333 | "source": [ |
||
| 334 | "train = pd.merge(train_abnl, train_acl, on='Case')" |
||
| 335 | ] |
||
| 336 | }, |
||
| 337 | { |
||
| 338 | "cell_type": "code", |
||
| 339 | "execution_count": 10, |
||
| 340 | "metadata": {}, |
||
| 341 | "outputs": [], |
||
| 342 | "source": [ |
||
| 343 | "train = pd.merge(train, train_meniscus, on='Case')" |
||
| 344 | ] |
||
| 345 | }, |
||
| 346 | { |
||
| 347 | "cell_type": "code", |
||
| 348 | "execution_count": 16, |
||
| 349 | "metadata": {}, |
||
| 350 | "outputs": [ |
||
| 351 | { |
||
| 352 | "data": { |
||
| 353 | "text/html": [ |
||
| 354 | "<div>\n", |
||
| 355 | "<style scoped>\n", |
||
| 356 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 357 | " vertical-align: middle;\n", |
||
| 358 | " }\n", |
||
| 359 | "\n", |
||
| 360 | " .dataframe tbody tr th {\n", |
||
| 361 | " vertical-align: top;\n", |
||
| 362 | " }\n", |
||
| 363 | "\n", |
||
| 364 | " .dataframe thead th {\n", |
||
| 365 | " text-align: right;\n", |
||
| 366 | " }\n", |
||
| 367 | "</style>\n", |
||
| 368 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 369 | " <thead>\n", |
||
| 370 | " <tr style=\"text-align: right;\">\n", |
||
| 371 | " <th></th>\n", |
||
| 372 | " <th>Case</th>\n", |
||
| 373 | " <th>Abnormal</th>\n", |
||
| 374 | " <th>ACL_tear</th>\n", |
||
| 375 | " <th>Meniscus_tear</th>\n", |
||
| 376 | " </tr>\n", |
||
| 377 | " </thead>\n", |
||
| 378 | " <tbody>\n", |
||
| 379 | " <tr>\n", |
||
| 380 | " <th>0</th>\n", |
||
| 381 | " <td>0000</td>\n", |
||
| 382 | " <td>1</td>\n", |
||
| 383 | " <td>0</td>\n", |
||
| 384 | " <td>0</td>\n", |
||
| 385 | " </tr>\n", |
||
| 386 | " <tr>\n", |
||
| 387 | " <th>1</th>\n", |
||
| 388 | " <td>0001</td>\n", |
||
| 389 | " <td>1</td>\n", |
||
| 390 | " <td>1</td>\n", |
||
| 391 | " <td>1</td>\n", |
||
| 392 | " </tr>\n", |
||
| 393 | " <tr>\n", |
||
| 394 | " <th>2</th>\n", |
||
| 395 | " <td>0002</td>\n", |
||
| 396 | " <td>1</td>\n", |
||
| 397 | " <td>0</td>\n", |
||
| 398 | " <td>0</td>\n", |
||
| 399 | " </tr>\n", |
||
| 400 | " <tr>\n", |
||
| 401 | " <th>3</th>\n", |
||
| 402 | " <td>0003</td>\n", |
||
| 403 | " <td>1</td>\n", |
||
| 404 | " <td>0</td>\n", |
||
| 405 | " <td>1</td>\n", |
||
| 406 | " </tr>\n", |
||
| 407 | " <tr>\n", |
||
| 408 | " <th>4</th>\n", |
||
| 409 | " <td>0004</td>\n", |
||
| 410 | " <td>1</td>\n", |
||
| 411 | " <td>0</td>\n", |
||
| 412 | " <td>0</td>\n", |
||
| 413 | " </tr>\n", |
||
| 414 | " </tbody>\n", |
||
| 415 | "</table>\n", |
||
| 416 | "</div>" |
||
| 417 | ], |
||
| 418 | "text/plain": [ |
||
| 419 | " Case Abnormal ACL_tear Meniscus_tear\n", |
||
| 420 | "0 0000 1 0 0\n", |
||
| 421 | "1 0001 1 1 1\n", |
||
| 422 | "2 0002 1 0 0\n", |
||
| 423 | "3 0003 1 0 1\n", |
||
| 424 | "4 0004 1 0 0" |
||
| 425 | ] |
||
| 426 | }, |
||
| 427 | "metadata": {}, |
||
| 428 | "output_type": "display_data" |
||
| 429 | }, |
||
| 430 | { |
||
| 431 | "data": { |
||
| 432 | "text/html": [ |
||
| 433 | "<div>\n", |
||
| 434 | "<style scoped>\n", |
||
| 435 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 436 | " vertical-align: middle;\n", |
||
| 437 | " }\n", |
||
| 438 | "\n", |
||
| 439 | " .dataframe tbody tr th {\n", |
||
| 440 | " vertical-align: top;\n", |
||
| 441 | " }\n", |
||
| 442 | "\n", |
||
| 443 | " .dataframe thead th {\n", |
||
| 444 | " text-align: right;\n", |
||
| 445 | " }\n", |
||
| 446 | "</style>\n", |
||
| 447 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 448 | " <thead>\n", |
||
| 449 | " <tr style=\"text-align: right;\">\n", |
||
| 450 | " <th></th>\n", |
||
| 451 | " <th></th>\n", |
||
| 452 | " <th></th>\n", |
||
| 453 | " <th>Case</th>\n", |
||
| 454 | " </tr>\n", |
||
| 455 | " <tr>\n", |
||
| 456 | " <th>Abnormal</th>\n", |
||
| 457 | " <th>ACL_tear</th>\n", |
||
| 458 | " <th>Meniscus_tear</th>\n", |
||
| 459 | " <th></th>\n", |
||
| 460 | " </tr>\n", |
||
| 461 | " </thead>\n", |
||
| 462 | " <tbody>\n", |
||
| 463 | " <tr>\n", |
||
| 464 | " <th>0</th>\n", |
||
| 465 | " <th>0</th>\n", |
||
| 466 | " <th>0</th>\n", |
||
| 467 | " <td>217</td>\n", |
||
| 468 | " </tr>\n", |
||
| 469 | " <tr>\n", |
||
| 470 | " <th rowspan=\"4\" valign=\"top\">1</th>\n", |
||
| 471 | " <th rowspan=\"2\" valign=\"top\">0</th>\n", |
||
| 472 | " <th>0</th>\n", |
||
| 473 | " <td>433</td>\n", |
||
| 474 | " </tr>\n", |
||
| 475 | " <tr>\n", |
||
| 476 | " <th>1</th>\n", |
||
| 477 | " <td>272</td>\n", |
||
| 478 | " </tr>\n", |
||
| 479 | " <tr>\n", |
||
| 480 | " <th rowspan=\"2\" valign=\"top\">1</th>\n", |
||
| 481 | " <th>0</th>\n", |
||
| 482 | " <td>83</td>\n", |
||
| 483 | " </tr>\n", |
||
| 484 | " <tr>\n", |
||
| 485 | " <th>1</th>\n", |
||
| 486 | " <td>125</td>\n", |
||
| 487 | " </tr>\n", |
||
| 488 | " </tbody>\n", |
||
| 489 | "</table>\n", |
||
| 490 | "</div>" |
||
| 491 | ], |
||
| 492 | "text/plain": [ |
||
| 493 | " Case\n", |
||
| 494 | "Abnormal ACL_tear Meniscus_tear \n", |
||
| 495 | "0 0 0 217\n", |
||
| 496 | "1 0 0 433\n", |
||
| 497 | " 1 272\n", |
||
| 498 | " 1 0 83\n", |
||
| 499 | " 1 125" |
||
| 500 | ] |
||
| 501 | }, |
||
| 502 | "metadata": {}, |
||
| 503 | "output_type": "display_data" |
||
| 504 | } |
||
| 505 | ], |
||
| 506 | "source": [ |
||
| 507 | "display(train.head())\n", |
||
| 508 | "display(train.groupby(['Abnormal','ACL_tear','Meniscus_tear']).count())" |
||
| 509 | ] |
||
| 510 | }, |
||
| 511 | { |
||
| 512 | "cell_type": "markdown", |
||
| 513 | "metadata": {}, |
||
| 514 | "source": [ |
||
| 515 | "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." |
||
| 516 | ] |
||
| 517 | }, |
||
| 518 | { |
||
| 519 | "cell_type": "markdown", |
||
| 520 | "metadata": {}, |
||
| 521 | "source": [ |
||
| 522 | "## Load stacks/sequences of images from each plane\n", |
||
| 523 | "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", |
||
| 524 | "\n", |
||
| 525 | "First, let's check for variation in the number of images per sequence, and in the image dimensions." |
||
| 526 | ] |
||
| 527 | }, |
||
| 528 | { |
||
| 529 | "cell_type": "code", |
||
| 530 | "execution_count": 67, |
||
| 531 | "metadata": {}, |
||
| 532 | "outputs": [], |
||
| 533 | "source": [ |
||
| 534 | "def collect_stack_dims(case_df, data_path=train_path):\n", |
||
| 535 | " cases = list(case_df.Case)\n", |
||
| 536 | " data = []\n", |
||
| 537 | " for case in cases:\n", |
||
| 538 | " row = [case]\n", |
||
| 539 | " for plane in ['axial', 'coronal', 'sagittal']:\n", |
||
| 540 | " fpath = data_path/plane/'{}.npy'.format(case)\n", |
||
| 541 | " try: \n", |
||
| 542 | " s,w,h = np.load(fpath).shape \n", |
||
| 543 | " row.extend([s,w,h])\n", |
||
| 544 | " except FileNotFoundError:\n", |
||
| 545 | " continue\n", |
||
| 546 | "# print('{}: {}'.format(case,row))\n", |
||
| 547 | " if len(row)==10: data.append(row)\n", |
||
| 548 | " columns=['Case',\n", |
||
| 549 | " 'axial_s','axial_w','axial_h',\n", |
||
| 550 | " 'coronal_s','coronal_w','coronal_h',\n", |
||
| 551 | " 'sagittal_s','sagittal_w','sagittal_h',\n", |
||
| 552 | " ]\n", |
||
| 553 | " data_dict = {}\n", |
||
| 554 | " for i,k in enumerate(columns): data_dict[k] = [row[i] for row in data]\n", |
||
| 555 | " return pd.DataFrame(data_dict)" |
||
| 556 | ] |
||
| 557 | }, |
||
| 558 | { |
||
| 559 | "cell_type": "code", |
||
| 560 | "execution_count": 68, |
||
| 561 | "metadata": {}, |
||
| 562 | "outputs": [], |
||
| 563 | "source": [ |
||
| 564 | "dimdf = collect_stack_dims(train)" |
||
| 565 | ] |
||
| 566 | }, |
||
| 567 | { |
||
| 568 | "cell_type": "code", |
||
| 569 | "execution_count": 70, |
||
| 570 | "metadata": {}, |
||
| 571 | "outputs": [ |
||
| 572 | { |
||
| 573 | "data": { |
||
| 574 | "text/html": [ |
||
| 575 | "<div>\n", |
||
| 576 | "<style scoped>\n", |
||
| 577 | " .dataframe tbody tr th:only-of-type {\n", |
||
| 578 | " vertical-align: middle;\n", |
||
| 579 | " }\n", |
||
| 580 | "\n", |
||
| 581 | " .dataframe tbody tr th {\n", |
||
| 582 | " vertical-align: top;\n", |
||
| 583 | " }\n", |
||
| 584 | "\n", |
||
| 585 | " .dataframe thead th {\n", |
||
| 586 | " text-align: right;\n", |
||
| 587 | " }\n", |
||
| 588 | "</style>\n", |
||
| 589 | "<table border=\"1\" class=\"dataframe\">\n", |
||
| 590 | " <thead>\n", |
||
| 591 | " <tr style=\"text-align: right;\">\n", |
||
| 592 | " <th></th>\n", |
||
| 593 | " <th>axial_s</th>\n", |
||
| 594 | " <th>axial_w</th>\n", |
||
| 595 | " <th>axial_h</th>\n", |
||
| 596 | " <th>coronal_s</th>\n", |
||
| 597 | " <th>coronal_w</th>\n", |
||
| 598 | " <th>coronal_h</th>\n", |
||
| 599 | " <th>sagittal_s</th>\n", |
||
| 600 | " <th>sagittal_w</th>\n", |
||
| 601 | " <th>sagittal_h</th>\n", |
||
| 602 | " </tr>\n", |
||
| 603 | " </thead>\n", |
||
| 604 | " <tbody>\n", |
||
| 605 | " <tr>\n", |
||
| 606 | " <th>count</th>\n", |
||
| 607 | " <td>50.000000</td>\n", |
||
| 608 | " <td>50.0</td>\n", |
||
| 609 | " <td>50.0</td>\n", |
||
| 610 | " <td>50.000000</td>\n", |
||
| 611 | " <td>50.0</td>\n", |
||
| 612 | " <td>50.0</td>\n", |
||
| 613 | " <td>50.00000</td>\n", |
||
| 614 | " <td>50.0</td>\n", |
||
| 615 | " <td>50.0</td>\n", |
||
| 616 | " </tr>\n", |
||
| 617 | " <tr>\n", |
||
| 618 | " <th>mean</th>\n", |
||
| 619 | " <td>35.860000</td>\n", |
||
| 620 | " <td>256.0</td>\n", |
||
| 621 | " <td>256.0</td>\n", |
||
| 622 | " <td>31.360000</td>\n", |
||
| 623 | " <td>256.0</td>\n", |
||
| 624 | " <td>256.0</td>\n", |
||
| 625 | " <td>31.72000</td>\n", |
||
| 626 | " <td>256.0</td>\n", |
||
| 627 | " <td>256.0</td>\n", |
||
| 628 | " </tr>\n", |
||
| 629 | " <tr>\n", |
||
| 630 | " <th>std</th>\n", |
||
| 631 | " <td>7.050865</td>\n", |
||
| 632 | " <td>0.0</td>\n", |
||
| 633 | " <td>0.0</td>\n", |
||
| 634 | " <td>7.899264</td>\n", |
||
| 635 | " <td>0.0</td>\n", |
||
| 636 | " <td>0.0</td>\n", |
||
| 637 | " <td>6.35687</td>\n", |
||
| 638 | " <td>0.0</td>\n", |
||
| 639 | " <td>0.0</td>\n", |
||
| 640 | " </tr>\n", |
||
| 641 | " <tr>\n", |
||
| 642 | " <th>min</th>\n", |
||
| 643 | " <td>22.000000</td>\n", |
||
| 644 | " <td>256.0</td>\n", |
||
| 645 | " <td>256.0</td>\n", |
||
| 646 | " <td>18.000000</td>\n", |
||
| 647 | " <td>256.0</td>\n", |
||
| 648 | " <td>256.0</td>\n", |
||
| 649 | " <td>19.00000</td>\n", |
||
| 650 | " <td>256.0</td>\n", |
||
| 651 | " <td>256.0</td>\n", |
||
| 652 | " </tr>\n", |
||
| 653 | " <tr>\n", |
||
| 654 | " <th>25%</th>\n", |
||
| 655 | " <td>32.000000</td>\n", |
||
| 656 | " <td>256.0</td>\n", |
||
| 657 | " <td>256.0</td>\n", |
||
| 658 | " <td>24.000000</td>\n", |
||
| 659 | " <td>256.0</td>\n", |
||
| 660 | " <td>256.0</td>\n", |
||
| 661 | " <td>26.25000</td>\n", |
||
| 662 | " <td>256.0</td>\n", |
||
| 663 | " <td>256.0</td>\n", |
||
| 664 | " </tr>\n", |
||
| 665 | " <tr>\n", |
||
| 666 | " <th>50%</th>\n", |
||
| 667 | " <td>37.500000</td>\n", |
||
| 668 | " <td>256.0</td>\n", |
||
| 669 | " <td>256.0</td>\n", |
||
| 670 | " <td>32.000000</td>\n", |
||
| 671 | " <td>256.0</td>\n", |
||
| 672 | " <td>256.0</td>\n", |
||
| 673 | " <td>32.00000</td>\n", |
||
| 674 | " <td>256.0</td>\n", |
||
| 675 | " <td>256.0</td>\n", |
||
| 676 | " </tr>\n", |
||
| 677 | " <tr>\n", |
||
| 678 | " <th>75%</th>\n", |
||
| 679 | " <td>40.000000</td>\n", |
||
| 680 | " <td>256.0</td>\n", |
||
| 681 | " <td>256.0</td>\n", |
||
| 682 | " <td>37.750000</td>\n", |
||
| 683 | " <td>256.0</td>\n", |
||
| 684 | " <td>256.0</td>\n", |
||
| 685 | " <td>36.00000</td>\n", |
||
| 686 | " <td>256.0</td>\n", |
||
| 687 | " <td>256.0</td>\n", |
||
| 688 | " </tr>\n", |
||
| 689 | " <tr>\n", |
||
| 690 | " <th>max</th>\n", |
||
| 691 | " <td>51.000000</td>\n", |
||
| 692 | " <td>256.0</td>\n", |
||
| 693 | " <td>256.0</td>\n", |
||
| 694 | " <td>46.000000</td>\n", |
||
| 695 | " <td>256.0</td>\n", |
||
| 696 | " <td>256.0</td>\n", |
||
| 697 | " <td>46.00000</td>\n", |
||
| 698 | " <td>256.0</td>\n", |
||
| 699 | " <td>256.0</td>\n", |
||
| 700 | " </tr>\n", |
||
| 701 | " </tbody>\n", |
||
| 702 | "</table>\n", |
||
| 703 | "</div>" |
||
| 704 | ], |
||
| 705 | "text/plain": [ |
||
| 706 | " axial_s axial_w axial_h coronal_s coronal_w coronal_h \\\n", |
||
| 707 | "count 50.000000 50.0 50.0 50.000000 50.0 50.0 \n", |
||
| 708 | "mean 35.860000 256.0 256.0 31.360000 256.0 256.0 \n", |
||
| 709 | "std 7.050865 0.0 0.0 7.899264 0.0 0.0 \n", |
||
| 710 | "min 22.000000 256.0 256.0 18.000000 256.0 256.0 \n", |
||
| 711 | "25% 32.000000 256.0 256.0 24.000000 256.0 256.0 \n", |
||
| 712 | "50% 37.500000 256.0 256.0 32.000000 256.0 256.0 \n", |
||
| 713 | "75% 40.000000 256.0 256.0 37.750000 256.0 256.0 \n", |
||
| 714 | "max 51.000000 256.0 256.0 46.000000 256.0 256.0 \n", |
||
| 715 | "\n", |
||
| 716 | " sagittal_s sagittal_w sagittal_h \n", |
||
| 717 | "count 50.00000 50.0 50.0 \n", |
||
| 718 | "mean 31.72000 256.0 256.0 \n", |
||
| 719 | "std 6.35687 0.0 0.0 \n", |
||
| 720 | "min 19.00000 256.0 256.0 \n", |
||
| 721 | "25% 26.25000 256.0 256.0 \n", |
||
| 722 | "50% 32.00000 256.0 256.0 \n", |
||
| 723 | "75% 36.00000 256.0 256.0 \n", |
||
| 724 | "max 46.00000 256.0 256.0 " |
||
| 725 | ] |
||
| 726 | }, |
||
| 727 | "execution_count": 70, |
||
| 728 | "metadata": {}, |
||
| 729 | "output_type": "execute_result" |
||
| 730 | } |
||
| 731 | ], |
||
| 732 | "source": [ |
||
| 733 | "dimdf.describe()" |
||
| 734 | ] |
||
| 735 | }, |
||
| 736 | { |
||
| 737 | "cell_type": "markdown", |
||
| 738 | "metadata": {}, |
||
| 739 | "source": [ |
||
| 740 | "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." |
||
| 741 | ] |
||
| 742 | }, |
||
| 743 | { |
||
| 744 | "cell_type": "code", |
||
| 745 | "execution_count": 20, |
||
| 746 | "metadata": {}, |
||
| 747 | "outputs": [], |
||
| 748 | "source": [ |
||
| 749 | "def load_one_stack(case, data_path=train_path, plane='coronal'):\n", |
||
| 750 | " fpath = data_path/plane/'{}.npy'.format(case)\n", |
||
| 751 | " return np.load(fpath)\n", |
||
| 752 | "\n", |
||
| 753 | "def load_stacks(case):\n", |
||
| 754 | " x = {}\n", |
||
| 755 | " planes = ['axial', 'coronal', 'sagittal']\n", |
||
| 756 | " for i, plane in enumerate(planes):\n", |
||
| 757 | " x[plane] = load_one_stack(case, plane=plane)\n", |
||
| 758 | " return x" |
||
| 759 | ] |
||
| 760 | }, |
||
| 761 | { |
||
| 762 | "cell_type": "code", |
||
| 763 | "execution_count": 29, |
||
| 764 | "metadata": {}, |
||
| 765 | "outputs": [ |
||
| 766 | { |
||
| 767 | "name": "stdout", |
||
| 768 | "output_type": "stream", |
||
| 769 | "text": [ |
||
| 770 | "(36, 256, 256)\n", |
||
| 771 | "255\n" |
||
| 772 | ] |
||
| 773 | } |
||
| 774 | ], |
||
| 775 | "source": [ |
||
| 776 | "case = train_abnl.Case[0]\n", |
||
| 777 | "x = load_one_stack(case, plane='coronal')\n", |
||
| 778 | "print(x.shape)\n", |
||
| 779 | "print(x.max())" |
||
| 780 | ] |
||
| 781 | }, |
||
| 782 | { |
||
| 783 | "cell_type": "code", |
||
| 784 | "execution_count": 9, |
||
| 785 | "metadata": {}, |
||
| 786 | "outputs": [ |
||
| 787 | { |
||
| 788 | "data": { |
||
| 789 | "text/plain": [ |
||
| 790 | "{'axial': array([[[ 0, 0, 0, 0, ..., 4, 5, 4, 3],\n", |
||
| 791 | " [ 0, 0, 0, 0, ..., 8, 8, 6, 8],\n", |
||
| 792 | " [ 0, 0, 0, 0, ..., 14, 14, 11, 11],\n", |
||
| 793 | " [ 0, 0, 0, 0, ..., 16, 16, 14, 15],\n", |
||
| 794 | " ...,\n", |
||
| 795 | " [ 0, 0, 0, 0, ..., 14, 15, 18, 16],\n", |
||
| 796 | " [ 0, 0, 0, 0, ..., 15, 16, 15, 12],\n", |
||
| 797 | " [ 0, 0, 0, 0, ..., 11, 12, 13, 12],\n", |
||
| 798 | " [ 0, 0, 0, 0, ..., 8, 11, 7, 9]],\n", |
||
| 799 | " \n", |
||
| 800 | " [[ 0, 0, 0, 0, ..., 4, 3, 2, 2],\n", |
||
| 801 | " [ 0, 0, 0, 0, ..., 5, 9, 7, 7],\n", |
||
| 802 | " [ 0, 0, 0, 0, ..., 10, 13, 10, 10],\n", |
||
| 803 | " [ 0, 0, 0, 0, ..., 14, 14, 19, 17],\n", |
||
| 804 | " ...,\n", |
||
| 805 | " [ 0, 0, 0, 0, ..., 18, 16, 16, 17],\n", |
||
| 806 | " [ 0, 0, 0, 0, ..., 13, 12, 15, 13],\n", |
||
| 807 | " [ 0, 0, 0, 0, ..., 16, 14, 12, 12],\n", |
||
| 808 | " [ 0, 0, 0, 0, ..., 8, 6, 5, 7]],\n", |
||
| 809 | " \n", |
||
| 810 | " [[ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", |
||
| 811 | " [ 0, 0, 0, 0, ..., 7, 8, 6, 6],\n", |
||
| 812 | " [ 0, 0, 0, 0, ..., 12, 11, 13, 10],\n", |
||
| 813 | " [ 0, 0, 0, 0, ..., 12, 18, 18, 16],\n", |
||
| 814 | " ...,\n", |
||
| 815 | " [ 0, 0, 0, 0, ..., 16, 18, 16, 17],\n", |
||
| 816 | " [ 0, 0, 0, 0, ..., 15, 13, 13, 16],\n", |
||
| 817 | " [ 0, 0, 0, 0, ..., 10, 10, 10, 12],\n", |
||
| 818 | " [ 0, 0, 0, 0, ..., 6, 6, 6, 5]],\n", |
||
| 819 | " \n", |
||
| 820 | " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", |
||
| 821 | " [ 0, 0, 0, 0, ..., 5, 7, 5, 4],\n", |
||
| 822 | " [ 0, 0, 0, 0, ..., 11, 10, 11, 12],\n", |
||
| 823 | " [ 0, 0, 0, 0, ..., 16, 16, 15, 14],\n", |
||
| 824 | " ...,\n", |
||
| 825 | " [ 0, 0, 0, 0, ..., 17, 21, 20, 18],\n", |
||
| 826 | " [ 0, 0, 0, 0, ..., 14, 15, 18, 14],\n", |
||
| 827 | " [ 0, 0, 0, 0, ..., 11, 9, 8, 10],\n", |
||
| 828 | " [ 0, 0, 0, 0, ..., 5, 5, 5, 5]],\n", |
||
| 829 | " \n", |
||
| 830 | " ...,\n", |
||
| 831 | " \n", |
||
| 832 | " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", |
||
| 833 | " [ 0, 0, 0, 0, ..., 4, 4, 4, 4],\n", |
||
| 834 | " [ 0, 0, 0, 0, ..., 10, 9, 9, 10],\n", |
||
| 835 | " [ 0, 0, 0, 0, ..., 12, 13, 16, 14],\n", |
||
| 836 | " ...,\n", |
||
| 837 | " [ 0, 0, 0, 0, ..., 16, 12, 14, 15],\n", |
||
| 838 | " [ 0, 0, 0, 0, ..., 11, 10, 13, 10],\n", |
||
| 839 | " [ 0, 0, 0, 0, ..., 9, 12, 9, 9],\n", |
||
| 840 | " [ 0, 0, 0, 0, ..., 6, 6, 4, 5]],\n", |
||
| 841 | " \n", |
||
| 842 | " [[ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", |
||
| 843 | " [ 0, 0, 0, 0, ..., 5, 5, 4, 4],\n", |
||
| 844 | " [ 0, 0, 0, 0, ..., 12, 8, 9, 11],\n", |
||
| 845 | " [ 0, 0, 0, 0, ..., 16, 17, 12, 13],\n", |
||
| 846 | " ...,\n", |
||
| 847 | " [ 0, 0, 0, 0, ..., 13, 14, 14, 17],\n", |
||
| 848 | " [ 0, 0, 0, 0, ..., 12, 14, 16, 13],\n", |
||
| 849 | " [ 0, 0, 0, 0, ..., 9, 12, 12, 8],\n", |
||
| 850 | " [ 0, 0, 0, 0, ..., 6, 5, 4, 5]],\n", |
||
| 851 | " \n", |
||
| 852 | " [[ 0, 0, 0, 0, ..., 3, 2, 3, 2],\n", |
||
| 853 | " [ 0, 0, 0, 0, ..., 5, 4, 5, 6],\n", |
||
| 854 | " [ 0, 0, 0, 0, ..., 13, 11, 11, 9],\n", |
||
| 855 | " [ 0, 0, 0, 0, ..., 13, 13, 16, 16],\n", |
||
| 856 | " ...,\n", |
||
| 857 | " [ 0, 0, 0, 0, ..., 17, 17, 14, 16],\n", |
||
| 858 | " [ 0, 0, 0, 0, ..., 16, 14, 16, 15],\n", |
||
| 859 | " [ 0, 0, 0, 0, ..., 9, 8, 10, 8],\n", |
||
| 860 | " [ 0, 0, 0, 0, ..., 6, 6, 6, 6]],\n", |
||
| 861 | " \n", |
||
| 862 | " [[ 0, 0, 0, 0, ..., 5, 4, 4, 4],\n", |
||
| 863 | " [ 0, 0, 0, 0, ..., 9, 8, 7, 6],\n", |
||
| 864 | " [ 0, 0, 0, 0, ..., 11, 9, 10, 11],\n", |
||
| 865 | " [ 0, 0, 0, 0, ..., 17, 15, 12, 13],\n", |
||
| 866 | " ...,\n", |
||
| 867 | " [ 0, 0, 0, 0, ..., 12, 16, 16, 15],\n", |
||
| 868 | " [ 0, 0, 0, 0, ..., 16, 12, 15, 16],\n", |
||
| 869 | " [ 0, 0, 0, 0, ..., 10, 14, 12, 12],\n", |
||
| 870 | " [ 0, 0, 0, 0, ..., 6, 8, 7, 7]]], dtype=uint8),\n", |
||
| 871 | " 'coronal': array([[[ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", |
||
| 872 | " [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", |
||
| 873 | " [ 0, 0, 0, 0, ..., 2, 2, 1, 2],\n", |
||
| 874 | " [ 0, 0, 0, 0, ..., 3, 3, 1, 2],\n", |
||
| 875 | " ...,\n", |
||
| 876 | " [ 0, 0, 0, 0, ..., 2, 3, 2, 2],\n", |
||
| 877 | " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", |
||
| 878 | " [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", |
||
| 879 | " [ 0, 0, 0, 0, ..., 0, 0, 1, 0]],\n", |
||
| 880 | " \n", |
||
| 881 | " [[ 0, 0, 0, 0, ..., 1, 1, 1, 0],\n", |
||
| 882 | " [ 0, 0, 0, 0, ..., 2, 1, 1, 1],\n", |
||
| 883 | " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", |
||
| 884 | " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", |
||
| 885 | " ...,\n", |
||
| 886 | " [ 0, 0, 0, 0, ..., 3, 3, 2, 2],\n", |
||
| 887 | " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", |
||
| 888 | " [ 0, 0, 0, 0, ..., 1, 1, 1, 2],\n", |
||
| 889 | " [ 0, 0, 0, 0, ..., 0, 1, 1, 1]],\n", |
||
| 890 | " \n", |
||
| 891 | " [[ 0, 0, 0, 0, ..., 1, 1, 0, 1],\n", |
||
| 892 | " [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", |
||
| 893 | " [ 0, 0, 0, 0, ..., 2, 2, 1, 2],\n", |
||
| 894 | " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", |
||
| 895 | " ...,\n", |
||
| 896 | " [ 0, 0, 0, 0, ..., 3, 2, 1, 2],\n", |
||
| 897 | " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", |
||
| 898 | " [ 0, 0, 0, 0, ..., 2, 1, 1, 2],\n", |
||
| 899 | " [ 0, 0, 0, 0, ..., 1, 1, 1, 1]],\n", |
||
| 900 | " \n", |
||
| 901 | " [[ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", |
||
| 902 | " [ 0, 0, 0, 0, ..., 2, 1, 1, 1],\n", |
||
| 903 | " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", |
||
| 904 | " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", |
||
| 905 | " ...,\n", |
||
| 906 | " [ 0, 0, 0, 0, ..., 2, 3, 2, 2],\n", |
||
| 907 | " [ 0, 0, 0, 0, ..., 3, 2, 1, 2],\n", |
||
| 908 | " [ 0, 0, 0, 0, ..., 2, 1, 1, 1],\n", |
||
| 909 | " [ 0, 0, 0, 0, ..., 1, 1, 1, 1]],\n", |
||
| 910 | " \n", |
||
| 911 | " ...,\n", |
||
| 912 | " \n", |
||
| 913 | " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", |
||
| 914 | " [ 0, 0, 0, 0, ..., 1, 1, 2, 2],\n", |
||
| 915 | " [ 0, 0, 0, 0, ..., 3, 3, 3, 3],\n", |
||
| 916 | " [ 0, 0, 0, 0, ..., 5, 4, 4, 5],\n", |
||
| 917 | " ...,\n", |
||
| 918 | " [ 0, 0, 0, 0, ..., 5, 5, 4, 5],\n", |
||
| 919 | " [ 0, 0, 0, 0, ..., 3, 3, 3, 3],\n", |
||
| 920 | " [ 0, 0, 0, 0, ..., 0, 0, 0, 1],\n", |
||
| 921 | " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n", |
||
| 922 | " \n", |
||
| 923 | " [[ 0, 0, 0, 0, ..., 1, 0, 0, 1],\n", |
||
| 924 | " [ 0, 0, 0, 0, ..., 2, 2, 1, 2],\n", |
||
| 925 | " [ 0, 0, 0, 0, ..., 4, 5, 4, 4],\n", |
||
| 926 | " [ 0, 0, 0, 0, ..., 7, 8, 8, 7],\n", |
||
| 927 | " ...,\n", |
||
| 928 | " [ 0, 0, 0, 0, ..., 7, 7, 6, 6],\n", |
||
| 929 | " [ 0, 0, 0, 0, ..., 5, 3, 3, 5],\n", |
||
| 930 | " [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", |
||
| 931 | " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n", |
||
| 932 | " \n", |
||
| 933 | " [[ 0, 0, 0, 0, ..., 0, 0, 1, 1],\n", |
||
| 934 | " [ 0, 0, 0, 0, ..., 2, 3, 3, 3],\n", |
||
| 935 | " [ 0, 0, 0, 0, ..., 4, 4, 5, 4],\n", |
||
| 936 | " [ 0, 0, 0, 0, ..., 9, 6, 8, 10],\n", |
||
| 937 | " ...,\n", |
||
| 938 | " [ 0, 0, 0, 0, ..., 8, 9, 8, 11],\n", |
||
| 939 | " [ 0, 0, 0, 0, ..., 5, 6, 3, 4],\n", |
||
| 940 | " [ 0, 0, 0, 0, ..., 0, 0, 1, 0],\n", |
||
| 941 | " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n", |
||
| 942 | " \n", |
||
| 943 | " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", |
||
| 944 | " [ 0, 0, 0, 0, ..., 2, 2, 2, 2],\n", |
||
| 945 | " [ 0, 0, 0, 0, ..., 3, 3, 5, 5],\n", |
||
| 946 | " [ 0, 0, 0, 0, ..., 9, 10, 7, 6],\n", |
||
| 947 | " ...,\n", |
||
| 948 | " [ 0, 0, 0, 0, ..., 9, 9, 9, 8],\n", |
||
| 949 | " [ 0, 0, 0, 0, ..., 5, 5, 4, 4],\n", |
||
| 950 | " [ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", |
||
| 951 | " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]]], dtype=uint8),\n", |
||
| 952 | " 'sagittal': array([[[ 0, 0, 0, 0, ..., 2, 1, 1, 2],\n", |
||
| 953 | " [ 0, 0, 0, 0, ..., 8, 8, 8, 7],\n", |
||
| 954 | " [ 0, 0, 0, 0, ..., 11, 15, 16, 14],\n", |
||
| 955 | " [ 7, 5, 5, 7, ..., 15, 14, 19, 16],\n", |
||
| 956 | " ...,\n", |
||
| 957 | " [ 7, 5, 7, 5, ..., 15, 17, 15, 14],\n", |
||
| 958 | " [ 0, 1, 1, 1, ..., 10, 15, 12, 11],\n", |
||
| 959 | " [ 0, 0, 0, 0, ..., 7, 7, 6, 5],\n", |
||
| 960 | " [ 0, 0, 0, 0, ..., 3, 2, 2, 1]],\n", |
||
| 961 | " \n", |
||
| 962 | " [[ 0, 0, 0, 0, ..., 4, 3, 1, 1],\n", |
||
| 963 | " [ 0, 0, 0, 0, ..., 10, 9, 7, 8],\n", |
||
| 964 | " [ 0, 0, 0, 0, ..., 17, 17, 15, 17],\n", |
||
| 965 | " [ 7, 5, 5, 6, ..., 20, 22, 21, 17],\n", |
||
| 966 | " ...,\n", |
||
| 967 | " [ 6, 7, 6, 4, ..., 18, 14, 19, 19],\n", |
||
| 968 | " [ 0, 1, 0, 2, ..., 16, 13, 15, 16],\n", |
||
| 969 | " [ 0, 0, 0, 0, ..., 10, 8, 5, 6],\n", |
||
| 970 | " [ 0, 0, 0, 0, ..., 2, 1, 1, 4]],\n", |
||
| 971 | " \n", |
||
| 972 | " [[ 0, 0, 0, 0, ..., 3, 4, 3, 4],\n", |
||
| 973 | " [ 0, 0, 0, 0, ..., 9, 12, 9, 10],\n", |
||
| 974 | " [ 0, 0, 0, 0, ..., 19, 20, 12, 11],\n", |
||
| 975 | " [ 7, 6, 7, 6, ..., 19, 17, 13, 16],\n", |
||
| 976 | " ...,\n", |
||
| 977 | " [ 3, 6, 6, 4, ..., 27, 27, 14, 19],\n", |
||
| 978 | " [ 2, 3, 1, 1, ..., 23, 19, 14, 15],\n", |
||
| 979 | " [ 0, 0, 0, 0, ..., 9, 4, 11, 9],\n", |
||
| 980 | " [ 0, 0, 0, 0, ..., 2, 1, 2, 3]],\n", |
||
| 981 | " \n", |
||
| 982 | " [[ 0, 0, 0, 0, ..., 3, 6, 4, 3],\n", |
||
| 983 | " [ 0, 0, 0, 0, ..., 8, 10, 8, 10],\n", |
||
| 984 | " [ 0, 0, 0, 0, ..., 21, 18, 14, 18],\n", |
||
| 985 | " [ 5, 9, 6, 6, ..., 26, 19, 18, 22],\n", |
||
| 986 | " ...,\n", |
||
| 987 | " [ 6, 4, 6, 9, ..., 22, 23, 24, 25],\n", |
||
| 988 | " [ 1, 1, 2, 1, ..., 14, 17, 19, 17],\n", |
||
| 989 | " [ 0, 0, 0, 0, ..., 10, 8, 7, 9],\n", |
||
| 990 | " [ 0, 0, 0, 0, ..., 2, 2, 2, 3]],\n", |
||
| 991 | " \n", |
||
| 992 | " ...,\n", |
||
| 993 | " \n", |
||
| 994 | " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", |
||
| 995 | " [ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", |
||
| 996 | " [ 0, 0, 0, 0, ..., 3, 3, 3, 2],\n", |
||
| 997 | " [ 0, 0, 0, 0, ..., 10, 8, 10, 10],\n", |
||
| 998 | " ...,\n", |
||
| 999 | " [ 0, 0, 0, 3, ..., 7, 10, 8, 7],\n", |
||
| 1000 | " [ 0, 0, 0, 0, ..., 4, 4, 5, 4],\n", |
||
| 1001 | " [ 0, 0, 0, 0, ..., 2, 2, 1, 1],\n", |
||
| 1002 | " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n", |
||
| 1003 | " \n", |
||
| 1004 | " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", |
||
| 1005 | " [ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", |
||
| 1006 | " [ 0, 0, 0, 0, ..., 3, 3, 3, 2],\n", |
||
| 1007 | " [ 0, 0, 0, 0, ..., 10, 11, 9, 11],\n", |
||
| 1008 | " ...,\n", |
||
| 1009 | " [ 0, 0, 0, 4, ..., 10, 9, 10, 9],\n", |
||
| 1010 | " [ 0, 0, 0, 1, ..., 5, 6, 5, 6],\n", |
||
| 1011 | " [ 0, 0, 0, 0, ..., 3, 2, 1, 2],\n", |
||
| 1012 | " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n", |
||
| 1013 | " \n", |
||
| 1014 | " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", |
||
| 1015 | " [ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", |
||
| 1016 | " [ 0, 0, 0, 0, ..., 3, 2, 2, 2],\n", |
||
| 1017 | " [ 0, 0, 0, 1, ..., 11, 12, 11, 9],\n", |
||
| 1018 | " ...,\n", |
||
| 1019 | " [ 0, 0, 0, 3, ..., 9, 11, 10, 9],\n", |
||
| 1020 | " [ 0, 0, 0, 1, ..., 5, 6, 6, 5],\n", |
||
| 1021 | " [ 0, 0, 0, 0, ..., 2, 3, 2, 1],\n", |
||
| 1022 | " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]],\n", |
||
| 1023 | " \n", |
||
| 1024 | " [[ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", |
||
| 1025 | " [ 0, 0, 0, 0, ..., 0, 0, 0, 0],\n", |
||
| 1026 | " [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", |
||
| 1027 | " [ 0, 0, 0, 0, ..., 8, 10, 8, 9],\n", |
||
| 1028 | " ...,\n", |
||
| 1029 | " [ 0, 0, 0, 2, ..., 7, 9, 8, 8],\n", |
||
| 1030 | " [ 0, 0, 0, 0, ..., 4, 4, 4, 5],\n", |
||
| 1031 | " [ 0, 0, 0, 0, ..., 1, 1, 1, 1],\n", |
||
| 1032 | " [ 0, 0, 0, 0, ..., 0, 0, 0, 0]]], dtype=uint8)}" |
||
| 1033 | ] |
||
| 1034 | }, |
||
| 1035 | "execution_count": 9, |
||
| 1036 | "metadata": {}, |
||
| 1037 | "output_type": "execute_result" |
||
| 1038 | } |
||
| 1039 | ], |
||
| 1040 | "source": [ |
||
| 1041 | "x_multi = load_stacks(case)\n", |
||
| 1042 | "x_multi" |
||
| 1043 | ] |
||
| 1044 | }, |
||
| 1045 | { |
||
| 1046 | "cell_type": "code", |
||
| 1047 | "execution_count": 30, |
||
| 1048 | "metadata": {}, |
||
| 1049 | "outputs": [], |
||
| 1050 | "source": [ |
||
| 1051 | "from ipywidgets import interactive\n", |
||
| 1052 | "from IPython.display import display\n", |
||
| 1053 | "\n", |
||
| 1054 | "plt.style.use('grayscale')\n", |
||
| 1055 | "\n", |
||
| 1056 | "class KneePlot():\n", |
||
| 1057 | " def __init__(self, x, figsize=(10, 10)):\n", |
||
| 1058 | " self.x = x\n", |
||
| 1059 | " self.slice_range = (0, self.x.shape[0] - 1)\n", |
||
| 1060 | " self.resize(figsize)\n", |
||
| 1061 | " \n", |
||
| 1062 | " def _plot_slice(self, im_slice):\n", |
||
| 1063 | " fig, ax = plt.subplots(1, 1, figsize=self.figsize)\n", |
||
| 1064 | " ax.imshow(self.x[im_slice, :, :])\n", |
||
| 1065 | " plt.show()\n", |
||
| 1066 | "\n", |
||
| 1067 | " def resize(self, figsize):\n", |
||
| 1068 | " self.figsize = figsize\n", |
||
| 1069 | " self.interactive_plot = interactive(self._plot_slice, im_slice=self.slice_range)\n", |
||
| 1070 | " self.output = self.interactive_plot.children[-1]\n", |
||
| 1071 | " self.output.layout.height = '{}px'.format(60 * self.figsize[1])\n", |
||
| 1072 | "\n", |
||
| 1073 | " def show(self):\n", |
||
| 1074 | " display(self.interactive_plot)\n" |
||
| 1075 | ] |
||
| 1076 | }, |
||
| 1077 | { |
||
| 1078 | "cell_type": "code", |
||
| 1079 | "execution_count": 31, |
||
| 1080 | "metadata": {}, |
||
| 1081 | "outputs": [ |
||
| 1082 | { |
||
| 1083 | "data": { |
||
| 1084 | "application/vnd.jupyter.widget-view+json": { |
||
| 1085 | "model_id": "bb94b9e5a31b44c5abd287a8cdb12fe9", |
||
| 1086 | "version_major": 2, |
||
| 1087 | "version_minor": 0 |
||
| 1088 | }, |
||
| 1089 | "text/plain": [ |
||
| 1090 | "interactive(children=(IntSlider(value=17, description='im_slice', max=35), Output(layout=Layout(height='600px'…" |
||
| 1091 | ] |
||
| 1092 | }, |
||
| 1093 | "metadata": {}, |
||
| 1094 | "output_type": "display_data" |
||
| 1095 | } |
||
| 1096 | ], |
||
| 1097 | "source": [ |
||
| 1098 | "plot = KneePlot(x)\n", |
||
| 1099 | "plot.show()\n" |
||
| 1100 | ] |
||
| 1101 | }, |
||
| 1102 | { |
||
| 1103 | "cell_type": "code", |
||
| 1104 | "execution_count": 12, |
||
| 1105 | "metadata": {}, |
||
| 1106 | "outputs": [ |
||
| 1107 | { |
||
| 1108 | "data": { |
||
| 1109 | "application/vnd.jupyter.widget-view+json": { |
||
| 1110 | "model_id": "4d55829c45294dbd90f82665f799a8ca", |
||
| 1111 | "version_major": 2, |
||
| 1112 | "version_minor": 0 |
||
| 1113 | }, |
||
| 1114 | "text/plain": [ |
||
| 1115 | "interactive(children=(IntSlider(value=17, description='im_slice', max=35), Output(layout=Layout(height='720px'…" |
||
| 1116 | ] |
||
| 1117 | }, |
||
| 1118 | "metadata": {}, |
||
| 1119 | "output_type": "display_data" |
||
| 1120 | } |
||
| 1121 | ], |
||
| 1122 | "source": [ |
||
| 1123 | "plot.resize(figsize=(12, 12))\n", |
||
| 1124 | "plot.show()\n" |
||
| 1125 | ] |
||
| 1126 | }, |
||
| 1127 | { |
||
| 1128 | "cell_type": "code", |
||
| 1129 | "execution_count": 15, |
||
| 1130 | "metadata": {}, |
||
| 1131 | "outputs": [], |
||
| 1132 | "source": [ |
||
| 1133 | "from ipywidgets import interact, Dropdown, IntSlider\n", |
||
| 1134 | "\n", |
||
| 1135 | "class MultiKneePlot():\n", |
||
| 1136 | " def __init__(self, x_multi, figsize=(10, 10)):\n", |
||
| 1137 | " self.x = x_multi\n", |
||
| 1138 | " self.planes = ['coronal', 'sagittal', 'axial']\n", |
||
| 1139 | " self.slice_nums = {plane: self.x[plane].shape[0] for plane in self.planes}\n", |
||
| 1140 | " self.figsize = figsize\n", |
||
| 1141 | " \n", |
||
| 1142 | " def _plot_slices(self, plane, im_slice): \n", |
||
| 1143 | " fig, ax = plt.subplots(1, 1, figsize=self.figsize)\n", |
||
| 1144 | " ax.imshow(self.x[plane][im_slice, :, :])\n", |
||
| 1145 | " plt.show()\n", |
||
| 1146 | " \n", |
||
| 1147 | " def draw(self):\n", |
||
| 1148 | " planes_widget = Dropdown(options=self.planes)\n", |
||
| 1149 | " plane_init = self.planes[0]\n", |
||
| 1150 | " slice_init = self.slice_nums[plane_init] - 1\n", |
||
| 1151 | " slices_widget = IntSlider(min=0, max=slice_init, value=slice_init//2)\n", |
||
| 1152 | " def update_slices_widget(*args):\n", |
||
| 1153 | " slices_widget.max = self.slice_nums[planes_widget.value] - 1\n", |
||
| 1154 | " slices_widget.value = slices_widget.max // 2\n", |
||
| 1155 | " planes_widget.observe(update_slices_widget, 'value')\n", |
||
| 1156 | " interact(self._plot_slices, plane=planes_widget, im_slice=slices_widget)\n", |
||
| 1157 | " \n", |
||
| 1158 | " def resize(self, figsize): self.figsize = figsize\n" |
||
| 1159 | ] |
||
| 1160 | }, |
||
| 1161 | { |
||
| 1162 | "cell_type": "code", |
||
| 1163 | "execution_count": 16, |
||
| 1164 | "metadata": {}, |
||
| 1165 | "outputs": [ |
||
| 1166 | { |
||
| 1167 | "data": { |
||
| 1168 | "application/vnd.jupyter.widget-view+json": { |
||
| 1169 | "model_id": "0d7a1c151c3440c59bfa0a2b55e6180e", |
||
| 1170 | "version_major": 2, |
||
| 1171 | "version_minor": 0 |
||
| 1172 | }, |
||
| 1173 | "text/plain": [ |
||
| 1174 | "interactive(children=(Dropdown(description='plane', options=('coronal', 'sagittal', 'axial'), value='coronal')…" |
||
| 1175 | ] |
||
| 1176 | }, |
||
| 1177 | "metadata": {}, |
||
| 1178 | "output_type": "display_data" |
||
| 1179 | } |
||
| 1180 | ], |
||
| 1181 | "source": [ |
||
| 1182 | "plot_multi = MultiKneePlot(x_multi)\n", |
||
| 1183 | "plot_multi.draw()" |
||
| 1184 | ] |
||
| 1185 | }, |
||
| 1186 | { |
||
| 1187 | "cell_type": "code", |
||
| 1188 | "execution_count": null, |
||
| 1189 | "metadata": {}, |
||
| 1190 | "outputs": [], |
||
| 1191 | "source": [] |
||
| 1192 | } |
||
| 1193 | ], |
||
| 1194 | "metadata": { |
||
| 1195 | "kernelspec": { |
||
| 1196 | "display_name": "Python 3", |
||
| 1197 | "language": "python", |
||
| 1198 | "name": "python3" |
||
| 1199 | }, |
||
| 1200 | "language_info": { |
||
| 1201 | "codemirror_mode": { |
||
| 1202 | "name": "ipython", |
||
| 1203 | "version": 3 |
||
| 1204 | }, |
||
| 1205 | "file_extension": ".py", |
||
| 1206 | "mimetype": "text/x-python", |
||
| 1207 | "name": "python", |
||
| 1208 | "nbconvert_exporter": "python", |
||
| 1209 | "pygments_lexer": "ipython3", |
||
| 1210 | "version": "3.7.2" |
||
| 1211 | } |
||
| 1212 | }, |
||
| 1213 | "nbformat": 4, |
||
| 1214 | "nbformat_minor": 2 |
||
| 1215 | } |