【博文】Medical Image Analysis with Deep Learning

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1. Analyzing images and videos, and using them in various applications such as self driven cars, drones etc. with underlying deep learning techniques has been the new research frontier. The recent research papers such as “A Neural Algorithm of Artistic Style”, show how a styles can be transferred from an artist and applied to an image, to create a new image. Other papers such as “Generative Adversarial Networks” (GAN) and “Wasserstein GAN” have paved the path to develop models that can learn to create data that is similar to data that we give them. Thus opening up the world to semi-supervised learning and paving the path to a future of unsupervised learning.
   
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3. While these research areas are still on the generic images, our goal is to use these research into medical images to help healthcare. We need to start with some basics. In this article, I start with basics of image processing, basics of medical image format data and visualize some medical data. In the next article I will deep dive into some convolutional neural nets and use them with Keras for predicting lung cancer.

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关键词：Analysis Learning Analysi Medical earning

Basic Face Detection

Lets, do something fun such as detecting a face. To detect face we will use an open source xml stump-based 20x20 gentle adaboost frontal face detector originally created by Rainer Lienhart. A good post with details on Haar-cascade detection is here.

Analyze DICOM Images

A very good python package used for analyzing DICOM images is pydicom. In this section, we will see how to render a DICOM image on a Jupyter notebook.

Install OpenCV using: pip install pydicom

After you install pydicom package, go back to the jupyter notebook. In the notebook, import the dicom package and other packages as shown below.

Analyze DICOM Images

A very good python package used for analyzing DICOM images is pydicom. In this section, we will see how to render a DICOM image on a Jupyter notebook.

Install OpenCV using: pip install pydicom

After you install pydicom package, go back to the jupyter notebook. In the notebook, import the dicom package and other packages as shown below.

Now, load the DICOM images into a list.

Step 1 : Basic Viewing of DICOM Image in Jupyter

In the first line we load the 1st DICOM file, which we’re gonna use as a reference named RefDs, to extract metadata and whose filename is first in the lstFilesDCM list.

We then calculate the total dimensions of the 3D NumPy array which are equal to (Number of pixel rows in a slice) x (Number of pixel columns in a slice) x (Number of slices) along the x, y, and z cartesian axes. Lastly, we use the PixelSpacing and SliceThickness attributes to calculate the spacing between pixels in the three axes. We store the array dimensions in ConstPixelDims and the spacing in ConstPixelSpacing [1].

Step 2: Looking into details of DICOM format

1. he unit of measurement in CT scans is the Hounsfield Unit (HU), which is a measure of radiodensity. CT scanners are carefully calibrated to accurately measure this. A detailed understanding on this can be found here.
   
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3. Each pixel is assigned a numerical value (CT number), which is the average of all the attenuation values contained within the corresponding voxel. This number is compared to the attenuation value of water and displayed on a scale of arbitrary units named Hounsfield units (HU) after Sir Godfrey Hounsfield.
   
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5. This scale assigns water as an attenuation value (HU) of zero. The range of CT numbers is 2000 HU wide although some modern scanners have a greater range of HU up to 4000. Each number represents a shade of grey with +1000 (white) and –1000 (black) at either end of the spectrum.
   
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7. Some scanners have cylindrical scanning bounds, but the output image is square. The pixels that fall outside of these bounds get the fixed value -2000.
   
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9. The first step usually is setting these values to 0. Next, let’s go back to HU units, by multiplying with the rescale slope and adding the intercept (which are conveniently stored in the metadata of the scans!).
   
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11. In the next part, we will use Kaggle’s lung cancer data-set and Convolution Neural Nets using Keras. We will build upon the information provided by this article to go to the next one.

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