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--- a +++ b/.ipynb_checkpoints/Untitled1-checkpoint.ipynb @@ -0,0 +1,283 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "DICOM_FOLDER = '../../data/DeepStrain_Shared_Folder/20210209/20210209_1251_P2018P00292_21_CINE_DEEPSTRAIN_bay7/'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from data import dicom_dataset\n", + "from data.base_dataset import BaseDataset, Transforms\n", + "from data.image_folder import make_dataset\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class Options():\n", + " \n", + " def __init__(self):\n", + " \n", + " self.dataroot = '../../data/DeepStrain_Shared_Folder/20210212'\n", + " self.max_dataset_size = float(\"inf\")\n", + " \n", + "opt = Options()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "filenames = sorted(make_dataset(opt.dataroot, opt.max_dataset_size, 'DICOM'))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import pydicom" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "metadata = {'FileName':[],\n", + " 'PatientName':[], \n", + " 'SeriesInstanceUID':[], \n", + " 'StudyInstanceUID':[], \n", + " 'ProtocolName':[], \n", + " 'TriggerTime':[], \n", + " 'InstanceNumber':[], \n", + " 'ImageOrientationPatient':[], \n", + " 'ImagePositionPatient':[],\n", + " 'SliceLocation':[], \n", + " 'PixelSpacing':[], \n", + " 'SliceThickness':[], \n", + " 'AcquisitionInstanceUID':[], \n", + " 'SliceInstanceUID':[]}\n", + "\n", + "for filename in filenames:\n", + " dicom = pydicom.read_file(filename)\n", + "\n", + " metadata['FileName'] += [filename]\n", + " metadata['PatientName'] += [str(dicom[0x0010, 0x0010].value)]\n", + " metadata['SeriesInstanceUID'] += [dicom.SeriesInstanceUID]\n", + " metadata['StudyInstanceUID'] += [dicom.StudyInstanceUID]\n", + " metadata['ProtocolName'] += [dicom.ProtocolName]\n", + " metadata['TriggerTime'] += [dicom.TriggerTime]\n", + " metadata['InstanceNumber'] += [dicom.InstanceNumber]\n", + " metadata['ImageOrientationPatient'] += [dicom.ImageOrientationPatient]\n", + " metadata['ImagePositionPatient'] += [dicom.ImagePositionPatient]\n", + " metadata['SliceLocation'] += [dicom.SliceLocation]\n", + " metadata['PixelSpacing'] += [dicom.PixelSpacing]\n", + " metadata['SliceThickness'] += [dicom.SliceThickness]\n", + "\n", + " metadata['AcquisitionInstanceUID'] += [dicom.SeriesInstanceUID.split('.')[-4][8:10]]\n", + " metadata['SliceInstanceUID'] += [dicom.SeriesInstanceUID.split('.')[-3]]\n", + "\n", + "metadata = pd.DataFrame(metadata)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metadata" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 patient(s):\n", + "P2018P00292_21_CINE_DEEPSTRAIN\n", + "Found 1 acquisitions(s):\n", + "P2018P00292_21_CINE_DEEPSTRAIN_13\n" + ] + } + ], + "source": [ + "dataset = dicom_dataset.DICOMDataset(opt)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "operands could not be broadcast together with shapes (170,208) (208,170) (170,208) ", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m<ipython-input-31-edb7541a72f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnifti\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mnifti\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_filename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../private_data/REPEATABILITY/20210209_1251_P2018P00292_21_CINE_DEEPSTRAIN_bay7_V1.nii.gz'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/tf/Dropbox (Partners HealthCare)/ubuntu/docker/repos/DeepStrain/data/dicom_dataset.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAcquisitionInstanceUID\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_acquisition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mread_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/tf/Dropbox (Partners HealthCare)/ubuntu/docker/repos/DeepStrain/data/dicom_dataset.py\u001b[0m in \u001b[0;36mload_acquisition\u001b[0;34m(self, df)\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0mdicom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpydicom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m \u001b[0msax_4D\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz_slice\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mphase\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mdicom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpixel_array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0maffine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSliceLocation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (170,208) (208,170) (170,208) " + ] + } + ], + "source": [ + "nifti = dataset.__getitem__(0)\n", + "nifti.to_filename('../private_data/REPEATABILITY/20210209_1251_P2018P00292_21_CINE_DEEPSTRAIN_bay7_V1.nii.gz')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pylab as plt\n", + "import time\n", + "from IPython import display\n", + "\n", + "for t in range(25):\n", + " plt.imshow(nifti.get_fdata()[:,:,8,t], cmap='gray')\n", + " time.sleep(0.1)\n", + " display.clear_output(wait=True)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "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": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}