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--- a +++ b/4DetectNodules.ipynb @@ -0,0 +1,889 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Detect Nodules from Kaggle Dataset\n", + "\n", + "## Summary\n", + "* load and process kaggle dataset\n", + "* Generate prediction masks with trained unet\n", + "* Reduce false positives with trained CNN" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:64: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:66: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:69: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:71: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:74: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:76: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:79: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:81: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:84: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:86: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(512, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:88: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\legacy\\layers.py:458: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n", + " name=name)\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:90: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:91: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:93: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:95: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:96: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:98: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:100: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:101: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:103: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:105: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:106: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:107: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1, (1, 1), activation=\"sigmoid\")`\n", + "c:\\programdata\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:109: UserWarning: Update your `Model` call to the Keras 2 API: `Model(outputs=Tensor(\"co..., inputs=Tensor(\"in...)`\n" + ] + } + ], + "source": [ + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "### EDIT HERE ###\n", + "\n", + "unetweightspath=\"modelweights/unet-weights-improvement.hdf5\"\n", + "truenoduleweightspath=\"modelweights/truenodule-cnn-weights-improvement.hdf5\"\n", + "INPUT_FOLDER = 'stage1/' #path to kaggle stage1 dataset\n", + "datafolder=\"processeddata/\"\n", + "\n", + "####################\n", + "import numpy as np # linear algebra\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import dicom\n", + "import os\n", + "import scipy.ndimage\n", + "import time\n", + "from keras.callbacks import ModelCheckpoint\n", + "import h5py\n", + "from sklearn.cluster import KMeans\n", + "from skimage import measure, morphology\n", + "import cell_magic_wand as cmw\n", + "import numpy as np\n", + "import csv\n", + "import random\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestClassifier as RF\n", + "from sklearn.metrics import confusion_matrix, classification_report\n", + "from scipy.ndimage.measurements import center_of_mass, label\n", + "from skimage.measure import regionprops\n", + "\n", + "import keras\n", + "from keras.models import Sequential,load_model,Model\n", + "from keras.layers import Dense, Dropout, Activation, Flatten\n", + "from keras.layers import Conv2D, MaxPooling2D, SpatialDropout2D\n", + "from keras.layers import Input, merge, UpSampling2D, BatchNormalization\n", + "from keras.optimizers import Adam\n", + "from keras.callbacks import EarlyStopping, ModelCheckpoint\n", + "from keras import backend as K\n", + "from keras.utils import to_categorical\n", + "from keras.datasets import mnist\n", + "from keras.models import Sequential\n", + "from keras import backend as K\n", + "from keras.optimizers import Adam\n", + "# Some constants \n", + "\n", + "patients = os.listdir(INPUT_FOLDER)\n", + "#patients=patients.sort()\n", + "K.set_image_dim_ordering('th') \n", + "\n", + "#Code sourced from https://www.kaggle.com/c/data-science-bowl-2017#tutorial\n", + "smooth = 1.0\n", + "width = 32\n", + "\n", + "def dice_coef(y_true, y_pred):\n", + " y_true_f = K.flatten(y_true)\n", + " y_pred_f = K.flatten(y_pred)\n", + " intersection = K.sum(y_true_f * y_pred_f)\n", + " return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n", + "def dice_coef_loss(y_true, y_pred):\n", + " return -dice_coef(y_true, y_pred)\n", + "\n", + "def unet_model():\n", + " inputs = Input((1, 512, 512))\n", + " conv1 = Conv2D(width, 3, 3, activation='relu', border_mode='same')(inputs)\n", + " conv1 = BatchNormalization(axis = 1)(conv1)\n", + " conv1 = Conv2D(width, 3, 3, activation='relu', border_mode='same')(conv1)\n", + " pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n", + "\n", + " conv2 = Conv2D(width*2, 3, 3, activation='relu', border_mode='same')(pool1)\n", + " conv2 = BatchNormalization(axis = 1)(conv2)\n", + " conv2 = Conv2D(width*2, 3, 3, activation='relu', border_mode='same')(conv2)\n", + " pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n", + "\n", + " conv3 = Conv2D(width*4, 3, 3, activation='relu', border_mode='same')(pool2)\n", + " conv3 = BatchNormalization(axis = 1)(conv3)\n", + " conv3 = Conv2D(width*4, 3, 3, activation='relu', border_mode='same')(conv3)\n", + " pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n", + "\n", + " conv4 = Conv2D(width*8, 3, 3, activation='relu', border_mode='same')(pool3)\n", + " conv4 = BatchNormalization(axis = 1)(conv4)\n", + " conv4 = Conv2D(width*8, 3, 3, activation='relu', border_mode='same')(conv4)\n", + " pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)\n", + "\n", + " conv5 = Conv2D(width*16, 3, 3, activation='relu', border_mode='same')(pool4)\n", + " conv5 = BatchNormalization(axis = 1)(conv5)\n", + " conv5 = Conv2D(width*16, 3, 3, activation='relu', border_mode='same')(conv5)\n", + "\n", + " up6 = merge([UpSampling2D(size=(2, 2))(conv5), conv4], mode='concat', concat_axis=1)\n", + " conv6 = SpatialDropout2D(0.35)(up6)\n", + " conv6 = Conv2D(width*8, 3, 3, activation='relu', border_mode='same')(conv6)\n", + " conv6 = Conv2D(width*8, 3, 3, activation='relu', border_mode='same')(conv6)\n", + "\n", + " up7 = merge([UpSampling2D(size=(2, 2))(conv6), conv3], mode='concat', concat_axis=1)\n", + " conv7 = SpatialDropout2D(0.35)(up7)\n", + " conv7 = Conv2D(width*4, 3, 3, activation='relu', border_mode='same')(conv7)\n", + " conv7 = Conv2D(width*4, 3, 3, activation='relu', border_mode='same')(conv7)\n", + "\n", + " up8 = merge([UpSampling2D(size=(2, 2))(conv7), conv2], mode='concat', concat_axis=1)\n", + " conv8 = SpatialDropout2D(0.35)(up8)\n", + " conv8 = Conv2D(width*2, 3, 3, activation='relu', border_mode='same')(conv8)\n", + " conv8 = Conv2D(width*2, 3, 3, activation='relu', border_mode='same')(conv8)\n", + "\n", + " up9 = merge([UpSampling2D(size=(2, 2))(conv8), conv1], mode='concat', concat_axis=1)\n", + " conv9 = SpatialDropout2D(0.35)(up9)\n", + " conv9 = Conv2D(width, 3, 3, activation='relu', border_mode='same')(conv9)\n", + " conv9 = Conv2D(width, 3, 3, activation='relu', border_mode='same')(conv9)\n", + " conv10 = Conv2D(1, 1, 1, activation='sigmoid')(conv9)\n", + "\n", + " model = Model(input=inputs, output=conv10)\n", + " model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss, metrics=[dice_coef])\n", + " return model\n", + "\n", + "unet_model=unet_model()\n", + "unet_model.load_weights(unetweightspath)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#classify as nodule or non-nodule\n", + "input_shape=(1,512,512)\n", + "num_classes=2\n", + "model = Sequential()\n", + "model.add(Conv2D(8, kernel_size=(3, 3),\n", + " activation='relu',\n", + " input_shape=input_shape))\n", + "model.add(Conv2D(16, (3, 3), activation='relu'))\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(Dropout(0.25))\n", + "model.add(Flatten())\n", + "model.add(Dense(32, activation='relu'))\n", + "model.add(Dropout(0.5))\n", + "model.add(Dense(num_classes, activation='softmax'))\n", + "\n", + "model.compile(loss=keras.losses.binary_crossentropy,\n", + " optimizer=Adam(lr=1e-5),\n", + " metrics=['accuracy'])\n", + "\n", + "\n", + "model.load_weights(truenoduleweightspath)\n", + "#os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'\n", + "#plot_model(model, to_file=\"CNNdiagram.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Load the scans in given folder path\n", + "def load_scan(path):\n", + " # code sourced from https://www.kaggle.com/gzuidhof/full-preprocessing-tutorial\n", + " slices = [dicom.read_file(path + '/' + s, force=True) for s in os.listdir(path) if s.endswith('.dcm')]\n", + " slices.sort(key = lambda x: float(x.ImagePositionPatient[2]), reverse=True)\n", + " try:\n", + " slice_thickness = np.abs(slices[0].ImagePositionPatient[2] - slices[1].ImagePositionPatient[2])\n", + " except:\n", + " slice_thickness = np.abs(slices[0].SliceLocation - slices[1].SliceLocation)\n", + " \n", + " for s in slices:\n", + " s.SliceThickness = slice_thickness\n", + " \n", + " return slices\n", + "\n", + "def get_pixels_hu(slices):\n", + " #code sourced from https://www.kaggle.com/gzuidhof/full-preprocessing-tutorial\n", + " image = np.stack([s.pixel_array for s in slices])\n", + " # Convert to int16 (from sometimes int16), \n", + " # should be possible as values should always be low enough (<32k)\n", + " image = image.astype(np.int16)\n", + "\n", + " # Set outside-of-scan pixels to 0\n", + " # The intercept is usually -1024, so air is approximately 0\n", + " image[image == -2000] = 0\n", + " \n", + " # Convert to Hounsfield units (HU)\n", + " for slice_number in range(len(slices)):\n", + " \n", + " intercept = slices[slice_number].RescaleIntercept\n", + " slope = slices[slice_number].RescaleSlope\n", + " \n", + " if slope != 1:\n", + " image[slice_number] = slope * image[slice_number].astype(np.float64)\n", + " image[slice_number] = image[slice_number].astype(np.int16)\n", + " \n", + " image[slice_number] += np.int16(intercept)\n", + " \n", + " return np.array(image, dtype=np.int16)\n", + "\n", + "def processimage(img):\n", + " #function sourced from https://www.kaggle.com/c/data-science-bowl-2017#tutorial\n", + " #Standardize the pixel values\n", + " mean = np.mean(img)\n", + " std = np.std(img)\n", + " img = img-mean\n", + " img = img/std\n", + " #plt.hist(img.flatten(),bins=200)\n", + " #plt.show()\n", + " #print(thresh_img[366][280:450])\n", + " middle = img[100:400,100:400] \n", + " mean = np.mean(middle) \n", + " max = np.max(img)\n", + " min = np.min(img)\n", + " #move the underflow bins\n", + " img[img==max]=mean\n", + " img[img==min]=mean\n", + " kmeans = KMeans(n_clusters=2).fit(np.reshape(middle,[np.prod(middle.shape),1]))\n", + " centers = sorted(kmeans.cluster_centers_.flatten())\n", + " threshold = np.mean(centers)\n", + " thresh_img = np.where(img<threshold,1.0,0.0) # threshold the image\n", + " eroded = morphology.erosion(thresh_img,np.ones([4,4]))\n", + " dilation = morphology.dilation(eroded,np.ones([10,10]))\n", + " labels = measure.label(dilation)\n", + " label_vals = np.unique(labels)\n", + " #plt.imshow(labels)\n", + " #plt.show()\n", + " labels = measure.label(dilation)\n", + " label_vals = np.unique(labels)\n", + " regions = measure.regionprops(labels)\n", + " good_labels = []\n", + " for prop in regions:\n", + " B = prop.bbox\n", + " if B[2]-B[0]<475 and B[3]-B[1]<475 and B[0]>40 and B[2]<472:\n", + " good_labels.append(prop.label)\n", + " mask = np.ndarray([512,512],dtype=np.int8)\n", + " mask[:] = 0\n", + " #\n", + " # The mask here is the mask for the lungs--not the nodes\n", + " # After just the lungs are left, we do another large dilation\n", + " # in order to fill in and out the lung mask \n", + " #\n", + " for N in good_labels:\n", + " mask = mask + np.where(labels==N,1,0)\n", + " mask = morphology.dilation(mask,np.ones([10,10])) # one last dilation\n", + " return mask*img\n", + "\n", + "def processimagenomask(img):\n", + " #Standardize the pixel values\n", + " mean = np.mean(img)\n", + " std = np.std(img)\n", + " img = img-mean\n", + " img = img/std\n", + " #plt.hist(img.flatten(),bins=200)\n", + " #plt.show()\n", + " #print(thresh_img[366][280:450])\n", + " middle = img[100:400,100:400] \n", + " mean = np.mean(middle) \n", + " max = np.max(img)\n", + " min = np.min(img)\n", + " #move the underflow bins\n", + " img[img==max]=mean\n", + " img[img==min]=mean\n", + " return img\n", + "\n", + "def processimagefromfile(ppix):\n", + " processpix=np.ndarray([ppix.shape[0],1,512,512])\n", + " for i in range(ppix.shape[0]):\n", + " processpix[i,0]=processimage(ppix[i])\n", + " return processpix\n", + "\n", + "#predict mask from images\n", + "def predictmask(images):\n", + " num_test=images.shape[0]\n", + " imgs_mask_test = np.ndarray([num_test,1,512,512],dtype=np.float32)\n", + " for i in range(num_test):\n", + " imgs_mask_test[i] = unet_model.predict([images[i:i+1]], verbose=0)[0]\n", + " return imgs_mask_test\n", + "\n", + "#find number of slices where a nodule is detected\n", + "def getnoduleindex(imgs_mask_test):\n", + " masksum=[np.sum(maskslice[0]) for maskslice in imgs_mask_test]\n", + " return [i for i in range(len(masksum)) if masksum[i]>5]\n", + "\n", + "def trueindicies(processed_pix, noduleindex):\n", + " noduleimgs=[processed_pix[ind] for ind in noduleindex]\n", + " noduleimgs=np.array(noduleimgs)\n", + " predictions=model.predict(noduleimgs)\n", + " predictions=predictions[:len(predictions),1]\n", + " predictions[predictions>0.5]=True\n", + " predictions[predictions<0.5]=False\n", + " trueindicies=[ind for i,ind in enumerate(noduleindex) if predictions[i]==1]\n", + " return trueindicies\n", + "\n", + "def thresholdnodules(noduleindices,mask):\n", + " nodulearea=[]\n", + " for ind in trueindicies:\n", + " nodulearea.append(np.sum(mask[ind]))\n", + " return nodulearea\n", + "\n", + "def largestnodulecoordinates(mask):\n", + " #mask=nodulemasks[indx,0][0]\n", + " mask[mask>0.5]=1\n", + " mask[mask<0.5]=0\n", + " labeled_array,nf=label(mask)\n", + " areasinslice=[]\n", + " if nf>1:\n", + " for n in range(nf):\n", + " lab=np.array(labeled_array)\n", + " lab[lab!=(n+1)]=0\n", + " lab[lab==(n+1)]=1\n", + " areasinslice.append(np.sum(lab))\n", + " nlargest=areasinslice.index(max(areasinslice))\n", + " labeled_array[labeled_array!=(nlargest+1)]=0\n", + " com=center_of_mass(labeled_array)\n", + " else:\n", + " com=center_of_mass(mask)\n", + " return [int(com[0]),int(com[1])]\n", + "\n", + "def largestnodulearea(mask,table,i):\n", + " #mask=nodulemasks[indx,0][0]\n", + " mask[mask>0.5]=1\n", + " mask[mask<0.5]=0\n", + " labeled_array,nf=label(mask)\n", + " areasinslice=[]\n", + " if nf>1:\n", + " for n in range(nf):\n", + " lab=np.array(labeled_array)\n", + " lab[lab!=(n+1)]=0\n", + " lab[lab==(n+1)]=1\n", + " areasinslice.append(np.sum(lab))\n", + " #nlargest=areasinslice.index(max(areasinslice))\n", + " #labeled_array[labeled_array!=(nlargest+1)]=0\n", + " return max(areasinslice)\n", + " else:\n", + " return table[\"Area\"][i]\n", + "\n", + "def crop_nodule(coord,image):\n", + " dim=32\n", + " return image[coord[0]-dim:coord[0]+dim,coord[1]-dim:coord[1]+dim]\n", + "#output: 64x64 images of the nodules with malignancy labels from the patient\n", + "\n", + "def largestnoduleproperties(mask):\n", + " mask[mask>0.5]=1\n", + " mask[mask<0.5]=0\n", + " mask=mask.astype(np.int8)\n", + " labeled_array,nf=label(mask)\n", + " areasinslice=[]\n", + " if nf>1:\n", + " for n in range(nf):\n", + " lab=np.array(labeled_array)\n", + " lab[lab!=(n+1)]=0\n", + " lab[lab==(n+1)]=1\n", + " areasinslice.append(np.sum(lab))\n", + " nlargest=areasinslice.index(max(areasinslice))\n", + " labeled_array[labeled_array!=(nlargest+1)]=0\n", + " noduleprops=regionprops(labeled_array)\n", + " else:\n", + " noduleprops=regionprops(mask)\n", + " area=noduleprops[0].area\n", + " eccentricity=noduleprops[0].eccentricity\n", + " diam=noduleprops[0].equivalent_diameter\n", + " diammajor=noduleprops[0].major_axis_length\n", + " spiculation=noduleprops[0].solidity\n", + " return area, eccentricity, diam, diammajor, spiculation\n", + "\n", + "def generatefeaturetable(nodulemasks):\n", + " meannoduleHU=[]\n", + " nodulecount=[]\n", + " largestarealist=[]\n", + " eccentricitylist=[]\n", + " diamlist=[]\n", + " diammajorlist=[]\n", + " spiculationlist=[]\n", + "\n", + " for i in range(nodulemasks.shape[0]):\n", + " mask=nodulemasks[i,0]\n", + " mask[mask>0.5]=1\n", + " mask[mask<0.5]=0\n", + " meannoduleHU.append(np.sum(noduleimages[i,0]*mask)/np.sum(mask))\n", + " labeled_array,features=label(mask)\n", + " nodulecount.append(features)\n", + " area, eccentricity, diam, diammajor, spiculation = largestnoduleproperties(nodulemasks[i,0])\n", + " largestarealist.append(area)\n", + " eccentricitylist.append(eccentricity)\n", + " diamlist.append(diam)\n", + " diammajorlist.append(diammajor)\n", + " spiculationlist.append(spiculation)\n", + " table=pd.DataFrame({\"Patient\":sample,\"NoduleIndex\":noduleindicies,\"Area\":area,\"MeanHU\":meannoduleHU, \"LargestNoduleArea\":largestarealist,\n", + " \"Eccentricity\":eccentricitylist, \"Diameter\":diamlist, \"DiameterMajor\":diammajorlist, \"Spiculation\":spiculationlist})\n", + " return table" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing patient# 1200 ETA: 13.88888888888889 hrs\n", + "Processing patient# 1201 ETA: 12.623715912898382 hrs\n", + "Processing patient# 1202 ETA: 10.018525106410186 hrs\n", + "Processing patient# 1203 ETA: 18.164322644339666 hrs\n", + "Processing 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"Processing patient# 1225 ETA: 8.727355263285615 hrs\n", + "Processing patient# 1226 ETA: 8.455341982405919 hrs\n", + "Processing patient# 1227 ETA: 8.351120784155135 hrs\n", + "Processing patient# 1228 ETA: 8.316320655887562 hrs\n", + "Processing patient# 1229 ETA: 8.488186760167965 hrs\n", + "Processing patient# 1230 ETA: 8.393290068418892 hrs\n", + "Processing patient# 1231 ETA: 8.380940078535387 hrs\n", + "Processing patient# 1232 ETA: 8.325024080611765 hrs\n", + "Processing patient# 1233 ETA: 8.342425880022724 hrs\n", + "Processing patient# 1234 ETA: 8.32099210145232 hrs\n", + "Processing patient# 1235 ETA: 8.283116624014719 hrs\n", + "Processing patient# 1236 ETA: 8.308585265549246 hrs\n", + "Processing patient# 1237 ETA: 8.243522517545086 hrs\n", + "Processing patient# 1238 ETA: 8.686181035109781 hrs\n", + "Processing patient# 1239 ETA: 8.866050753437216 hrs\n", + "Processing patient# 1240 ETA: 8.860589005996784 hrs\n", + "Processing patient# 1241 ETA: 8.904041662632933 hrs\n", 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"Processing patient# 1365 ETA: 4.928263342958509 hrs\n", + "Processing patient# 1366 ETA: 4.906512433510309 hrs\n", + "Processing patient# 1368 ETA: 4.884942292143961 hrs\n", + "Processing patient# 1369 ETA: 4.82783218746433 hrs\n", + "Processing patient# 1370 ETA: 4.8466363209135395 hrs\n", + "Processing patient# 1371 ETA: 4.8318471228541915 hrs\n", + "Processing patient# 1372 ETA: 4.821795012823449 hrs\n", + "Processing patient# 1373 ETA: 4.803408985386005 hrs\n", + "Processing patient# 1374 ETA: 4.777427026608132 hrs\n", + "Processing patient# 1375 ETA: 4.763656514637054 hrs\n", + "Processing patient# 1376 ETA: 4.743936037966472 hrs\n", + "Processing patient# 1377 ETA: 4.719618169200248 hrs\n", + "Processing patient# 1378 ETA: 4.690835314464851 hrs\n", + "Processing patient# 1379 ETA: 4.666089715291667 hrs\n", + "Processing patient# 1380 ETA: 4.675125468286835 hrs\n", + "Processing patient# 1381 ETA: 4.658687751179754 hrs\n", + "Processing patient# 1382 ETA: 4.635416509547295 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"Processing patient# 1439 ETA: 3.3569018378051583 hrs\n", + "Processing patient# 1440 ETA: 3.3322680721583744 hrs\n", + "Processing patient# 1441 ETA: 3.3100857617839687 hrs\n", + "Processing patient# 1443 ETA: 3.291559807825679 hrs\n", + "Processing patient# 1444 ETA: 3.2368162911495224 hrs\n", + "Processing patient# 1445 ETA: 3.219285455204192 hrs\n", + "Processing patient# 1446 ETA: 3.1963537533862594 hrs\n", + "Processing patient# 1447 ETA: 3.1745961232535005 hrs\n", + "Processing patient# 1448 ETA: 3.161650590983931 hrs\n", + "Processing patient# 1449 ETA: 3.1433429676488482 hrs\n", + "Processing patient# 1450 ETA: 3.1198210242682034 hrs\n", + "Processing patient# 1451 ETA: 3.0976603277366 hrs\n", + "Processing patient# 1452 ETA: 3.0779698065120384 hrs\n", + "Processing patient# 1453 ETA: 3.0560144474137805 hrs\n", + "Processing patient# 1454 ETA: 3.035682783060343 hrs\n", + "Processing patient# 1455 ETA: 3.01512198534926 hrs\n", + "Processing patient# 1457 ETA: 3.001986470494109 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"Processing patient# 1515 ETA: 1.7381065940352345 hrs\n", + "Processing patient# 1516 ETA: 1.7198453679184118 hrs\n", + "Processing patient# 1517 ETA: 1.6968657202587767 hrs\n", + "Processing patient# 1518 ETA: 1.6766352182999515 hrs\n", + "Processing patient# 1519 ETA: 1.6640672508136443 hrs\n", + "Processing patient# 1520 ETA: 1.6443906538188457 hrs\n", + "Processing patient# 1521 ETA: 1.6265579485526016 hrs\n", + "Processing patient# 1522 ETA: 1.6064969870503643 hrs\n", + "Processing patient# 1523 ETA: 1.5878849256592276 hrs\n", + "Processing patient# 1524 ETA: 1.5670396502469308 hrs\n", + "Processing patient# 1526 ETA: 1.5448135244235013 hrs\n", + "Processing patient# 1527 ETA: 1.5007179911218453 hrs\n", + "Processing patient# 1528 ETA: 1.4779989854163593 hrs\n", + "Processing patient# 1529 ETA: 1.455040394503777 hrs\n", + "Processing patient# 1530 ETA: 1.4348017759756608 hrs\n", + "Processing patient# 1531 ETA: 1.412959918495631 hrs\n", + "Processing patient# 1532 ETA: 1.3895584875255462 hrs\n", + "Processing patient# 1534 ETA: 1.3681563406241992 hrs\n", + "Processing patient# 1535 ETA: 1.32147174191119 hrs\n", + "Processing patient# 1536 ETA: 1.2993491290633876 hrs\n", + "Processing patient# 1537 ETA: 1.2765257389907825 hrs\n", + "Processing patient# 1538 ETA: 1.2545430914596223 hrs\n", + "Processing patient# 1539 ETA: 1.2330312886358439 hrs\n", + "Processing patient# 1540 ETA: 1.2134439415684322 hrs\n", + "Processing patient# 1541 ETA: 1.1903008280926082 hrs\n", + "Processing patient# 1542 ETA: 1.167653547794189 hrs\n", + "Processing patient# 1543 ETA: 1.1457156837554199 hrs\n", + "Processing patient# 1544 ETA: 1.1241135936051372 hrs\n", + "Processing patient# 1545 ETA: 1.102846443816275 hrs\n", + "Processing patient# 1546 ETA: 1.080637407885313 hrs\n", + "Processing patient# 1547 ETA: 1.0583609296631968 hrs\n", + "Processing patient# 1549 ETA: 1.0358784646273498 hrs\n", + "Processing patient# 1550 ETA: 0.9894958379524097 hrs\n", + "Processing patient# 1551 ETA: 0.9702932011284904 hrs\n", + "Processing patient# 1552 ETA: 0.9487030035562142 hrs\n", + "Processing patient# 1553 ETA: 0.927032014874493 hrs\n", + "Processing patient# 1555 ETA: 0.9052574532595503 hrs\n", + "Processing patient# 1556 ETA: 0.8583338576951499 hrs\n", + "Processing patient# 1557 ETA: 0.8364724836388824 hrs\n", + "Processing patient# 1558 ETA: 0.8143605039141544 hrs\n", + "Processing patient# 1559 ETA: 0.7939183002461303 hrs\n", + "Processing patient# 1560 ETA: 0.7714008516864275 hrs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing patient# 1561 ETA: 0.7496390868227617 hrs\n", + "Processing patient# 1562 ETA: 0.7277910622262829 hrs\n", + "Processing patient# 1563 ETA: 0.70534063768343 hrs\n", + "Processing patient# 1564 ETA: 0.6836017272867927 hrs\n", + "Processing patient# 1566 ETA: 0.6618160449642033 hrs\n", + "Processing patient# 1568 ETA: 0.6160384095808674 hrs\n", + "Processing patient# 1570 ETA: 0.5702290712717382 hrs\n", + "Processing patient# 1571 ETA: 0.5249562931103638 hrs\n", + "Processing patient# 1572 ETA: 0.5033148116076286 hrs\n", + "Processing patient# 1573 ETA: 0.48112341017214527 hrs\n", + "Processing patient# 1574 ETA: 0.4594317513380257 hrs\n", + "Processing patient# 1575 ETA: 0.43778636997364123 hrs\n", + "Processing patient# 1576 ETA: 0.415779529424036 hrs\n", + "Processing patient# 1577 ETA: 0.3949588738670405 hrs\n", + "Processing patient# 1578 ETA: 0.37282882327230255 hrs\n", + "Processing patient# 1580 ETA: 0.3515907668564129 hrs\n", + "Processing patient# 1581 ETA: 0.30684781963013846 hrs\n", + "Processing patient# 1582 ETA: 0.28561606805205886 hrs\n", + "Processing patient# 1583 ETA: 0.26367890904528596 hrs\n", + "Processing patient# 1584 ETA: 0.241728710744323 hrs\n", + "Processing patient# 1585 ETA: 0.2196190606033997 hrs\n", + "Processing patient# 1587 ETA: 0.19766932272849366 hrs\n", + "Processing patient# 1588 ETA: 0.1533620524758064 hrs\n", + "Processing patient# 1589 ETA: 0.1314311540085944 hrs\n", + "Processing patient# 1590 ETA: 0.10954693088589212 hrs\n", + "Processing patient# 1591 ETA: 0.08776483735769842 hrs\n", + "Processing patient# 1592 ETA: 0.06588565785057654 hrs\n", + "Processing patient# 1594 ETA: 0.04392167075191941 hrs\n", + "31161.49111223221\n" + ] + } + ], + "source": [ + "start_time=time.time()\n", + "\n", + "elapsed_time=0\n", + "totaltime=94000\n", + "thresh=-500 #lower HU threshold for nodule segmentation\n", + "noduleimages=np.ndarray([5000,1,512,512],dtype=np.float32)\n", + "nodulemasks=np.ndarray([5000,1,512,512],dtype=np.float32)\n", + "sample=[]\n", + "area=[]\n", + "noduleindicies=[]\n", + "index=0\n", + "start=1\n", + "end=400\n", + "for i in range(len(patients)):\n", + " print(\"Processing patient#\",i,\"ETA:\",(totaltime-elapsed_time)/3600,\"hrs\")\n", + " if (i-1)/400-np.floor((i-1)/400)==0:\n", + " noduleimages=noduleimages[:index]\n", + " nodulemasks=nodulemasks[:index]\n", + " table=generatefeaturetable(nodulemasks)\n", + " print(\"Saving data for patients\"+str(start)+\"-\"+str(end))\n", + " np.save(datafolder+\"DSBNoduleImages\"+str(start)+\"-\"+str(end)+\".npy\",noduleimages)\n", + " np.save(datafolder+\"DSBNoduleMasks\"+str(start)+\"-\"+str(end)+\".npy\",nodulemasks)\n", + " table.to_csv(datafolder+\"DSBNoduleFeatures\"+str(start)+\"-\"+str(end)+\".csv\")\n", + " del noduleimages, nodulemasks\n", + " noduleimages=np.ndarray([5000,1,512,512],dtype=np.float32)\n", + " nodulemasks=np.ndarray([5000,1,512,512],dtype=np.float32)\n", + " sample=[]\n", + " area=[]\n", + " noduleindicies=[]\n", + " index=0 \n", + " patient_scan=load_scan(INPUT_FOLDER+patients[i])\n", + " patient_pix=get_pixels_hu(patient_scan)\n", + " processed_pix = processimagefromfile(patient_pix)\n", + " mask = predictmask(processed_pix)\n", + " noduleindex = getnoduleindex(mask)\n", + " trueinds=trueindicies(processed_pix,noduleindex)\n", + "\n", + " for ind in trueinds:\n", + " noduleimages[index,0]=patient_pix[ind]\n", + " nodulemasks[index,0]=mask[ind]\n", + " sample.append(patients[i])\n", + " area.append(np.sum(mask[ind]))\n", + " noduleindicies.append(ind)\n", + " index+=1\n", + "\n", + " elapsed_time=time.time()-start_time\n", + " totaltime=elapsed_time/(i-start+1)*(end-start)\n", + "\n", + "\n", + "\n", + "print(elapsed_time)" + ] + } + ], + "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": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}