Medical Image Analysis with Deep Learning Techniques

Author: Zen and the Art of Computer Programming

1 Introduction

2D Medical Image Analysis

When performing two-dimensional analysis of medical images, it is necessary to perform processes such as image preprocessing, feature extraction, model training, and classifier generation. With the rapid development of computer vision technology in recent years, technology based on deep learning has attracted more and more widespread attention. Deep learning has achieved good results in the field of image recognition, but the structural information in medical images is difficult to directly analyze using traditional machine learning methods. Therefore, how to combine deep learning technology and its medical image characteristics to achieve more efficient, accurate, and precise medical image analysis has become one of the research hotspots for a long time.

With the popularization of medical imaging applications, it is necessary to collect, organize, store and manage various types of data that are often involved in medical image analysis. Currently, the collection of medical imaging data is carried out by professional medical records, but this method still has certain shortcomings. In addition, there are also difficulties in the storage of medical imaging data, including data backup, classification, archiving, etc., which require professional participation. Therefore, in this regard, you can consider building an all-in-one platform that can integrate, communicate and manage different types of data sources to facilitate the collection, storage, sharing and analysis of medical images.

This article first discusses some basic principles of medical image data, and then combines deep learning technology to introduce in detail the technologies commonly used in medical image analysis, such as image preprocessing, feature extraction, model training, classifier generation, etc. Finally, the future development direction is prospected and a more healthy and effective medical image analysis model is envisioned.

2. Overview of 2D medical image data

2.1 Medical imaging data types

Generally speaking, medical imaging data includes the following types:

1. Slice medical image: It is a set of continuous slice images in which the slicing device is moved in the same direction;
2. Contrast (CT) image: CT image is composed of tissue meteorological scans of various tissues and organs, and is composed of tissue sections at different time points;
3. X-ray images: X-ray photos are made from radioactive material rays. The more pixels an X-ray image has, the more information it contains;
4. Ultrasound ultrasound image: generally used for analysis of ultrasonic drying, adhesion, and intracystic permeability samples;
5. MR magnetic resonance imaging images: used for monitoring the morphology, physiological activity and disease changes of tissues outside the body or surrounding tissue structures of the body.

The 2D medical image is shown in the figure above and contains multiple regions of interest and non-interest regions. Each region of interest usually corresponds to a different tissue or structure, and its location and size depend on factors such as the scanning device and the patient's movement. The distribution of different tissues in the image shows complex shapes, and there are often similarities between various tissues and structures. The non-interest area usually contains information such as edges and background.

2.2 Characteristics of medical image data

In addition to having two-dimensional and three-dimensional structures, medical image data also has the following significant characteristics:

1. Rich spatial information: Due to the existence of three-dimensional structure, medical image data contains a large amount of spatial information, including front, back, left, right, up and down, hierarchical organization and environmental information;
2. Large resolution differences: Medical image data usually adopts higher resolution, so that it can reflect the details and texture information of microstructure;
3. Pixel diversity: Medical image data contains a variety of pixels, including masses, tissue walls, abnormal physiological markers, and other in vitro signals collected during the image production process, etc.;
4. Data imbalance: Medical image data often has extreme imbalance distribution, and the positive and negative cases can even reach the order of thousands of times.

2.3 Challenges of medical image data

The challenges of medical image data mainly include three aspects:

1. Diversity: including various light sources, stimulation conditions, organizational structures, movement modes, natural scenes and other diverse signals mixed together;
2. Model complexity: For complex models, lack of training data, imbalance of positive and negative examples, and excessive and non-sparse model parameters will cause computational difficulties;
3. Low information density: Images displayed on computer screens often have very little pixel information, so some information processing mechanism is needed to extract important features and convert them into readable image representations.

3. Preprocessing of medical image data

3.1 Channel information extraction

During the original signal collection process of medical image data, mixed information between different bands will be generated. Therefore, in order to extract signals from different tissues, the data needs to be channel divided first to separate signals in different spectrum ranges. Medical image data usually consists of red, green, blue, indigo and other color spectra mixed together, so the channel division method is also relatively flexible.

3.2 Contrast adjustment

Contrast refers to the relationship between the brightness value of the pixels in the image and the black background. If the contrast of the image is too low, it may cause distortion in tasks such as image feature detection and classification. The contrast adjustment can solve this problem. Contrast can be adjusted in two ways:

1. Histogram Equalization: The method of histogram equalization is to describe the distribution of each pixel of the image with a certain probability distribution function, and then reset the gray value of the image according to this function to make the distribution of all pixels Things become smooth and even. Histogram equalization can eliminate the inconsistency in light and dark contrast caused by the influence of light sources, and can also enhance the robustness of the image.
2. Local Histogram Equalization: Local histogram equalization is a technique for equalizing contrast within a region of interest. It does not require a global histogram, but only the histogram of a certain window (region) in the image. The graph can be equalized.

3.3 Noise reduction

Noise refers to signals where errors occur in the propagation path of the analog signal, such as defocus, debris, infiltration, reverberation, etc. Denoising image data can start from the following two aspects:

1. Filtering: Filtering is a common noise reduction method, including Gaussian filtering, median filtering, mean filtering, bidirectional filtering, etc. Through filtering, the noise in the analog signal can be eliminated or reduced and the image quality can be improved.
2. De-fogging: De-fogging is a noise reduction method based on the statistical characteristics of the image. It eliminates the influence of background, clouds, haze and other objects by modeling the statistical rules of the image gray value. De-fogging has a good effect, but it has certain requirements for the image. For example, the image must have a good structure and no obvious gradients, and it must be able to automatically determine whether the image is suitable for de-fogging.

4. Feature extraction of medical image data

Feature extraction refers to extracting meaningful and representative features from medical image data, which can be used for medical image analysis. Usually, feature extraction includes three stages: local feature extraction, global feature extraction and multi-scale feature extraction.

4.1 Local feature extraction

Local feature extraction refers to extracting image features from the neighborhood of a single pixel or geometric area. Although this method is simple and easy to understand, it often counts all local features in the entire image, so the expressive ability of the features is relatively weak.

Features in 2D images include edges, corners, patches, etc., which can be obtained through operations such as erosion and dilation. Convolutional Neural Network (CNN) can be used for feature extraction, in which weight sharing and pooling operations can effectively reduce the amount of parameters and memory usage.

4.2 Global feature extraction

Global feature extraction refers to feature extraction from the statistical features of the overall image. Global features can often capture contextual information of the entire image and provide an overall feature expression of the image.

There are many global features of images, such as edge length, width, direction, curvature, morphological features, etc. Global features can be extracted from angles such as color, spatial position, and texture.

4.3 Multi-scale feature extraction

Multi-scale feature extraction refers to feature extraction from images of different scales and fusion through different levels of image information. Such as multi-scale Fourier transform, morphological pyramid, radial transform, etc. This method can improve the performance of feature extraction, retain different features at different scales, and reduce spatial positioning errors.

5. Model training of medical image data

5.1 CNN model structure

In CNN, convolutional layers, pooling layers, and fully connected layers are usually connected in series, and then trained through backpropagation. Commonly used convolutional layers are:

1. 3 x 3 convolution kernel: performs convolution operation on adjacent areas in the image, which is equivalent to image filtering;
2. 5 x 5 convolution kernel: perform convolution operation on adjacent areas in the image;
3. 7 x 7 convolution kernel: perform convolution operation on adjacent areas in the image;

Commonly used pooling layers are:

1. Max pooling: Take the maximum value in the pooling window as the output value;
2. Average pooling: Take the average value in the pooling window as the output value;

5.2 Data expansion and overfitting

Due to the imbalance of the data set and over-fitting problems, model training is often very difficult. To solve this problem, data augmentation can be used. Data expansion methods include:

1. Probability distribution sampling: randomly select samples from the data set, and then copy, rotate, crop, change brightness, etc.;
2. Natural image generation: Generate new images designed by the model itself and add them to the training set;
3. Semi-supervised learning: Use labeled data to train the model, while training unlabeled data, and use unlabeled data to help the model improve its generalization ability;
4. Ensemble learning: Improve the robustness and robustness of the model by building multiple models and averaging or voting on their output results.

5.3 Validation set and test set

When training the model, it is necessary to divide the validation set and the test set. The validation set is used to evaluate the performance of the model and select optimal model parameters during the iteration process, and the test set is used to demonstrate the generalization ability of the model. The division of the verification set needs to be determined based on actual business conditions.

6. Classifier generation for medical image data

A classifier is an algorithm that predicts which class an image belongs to based on features. Currently, classifiers can be divided into supervised classifiers and unsupervised classifiers.

6.1 Supervised classifiers

A supervised classifier is given a label for an image and then uses a classification model to classify the image. Typical models of supervised classifiers are:

1. Support Vector Machine (SVM): SVM performs well in classification tasks, and it supports the form of kernel functions and can model nonlinear problems;
2. Naive Bayes: Naive Bayes is a classification method based on probability theory, which assumes that each category obeys a multinomial distribution and is independent of each other. Therefore, NB can effectively solve high-dimensional, multi-label problems.
3. Decision Tree: Decision tree is a classification method that divides the feature space into disjoint regions and predicts the target variable based on the attributes of the region. The training process of the decision tree is a recursive process until it can no longer be divided;

6.2 Unsupervised classifier

An unsupervised classifier does not give the label of the image, but only uses a clustering algorithm or a density inference algorithm to cluster the images and then classify them. Typical models of unsupervised classifiers are:

1. K-Means algorithm: K-Means is an unsupervised learning algorithm that determines the final clustering result based on the number k of sample clustering centers. The advantage of K-Means is that it is fast and easy to implement, but it cannot find the global optimal solution.
2. DBSCAN algorithm: DBSCAN is an unsupervised learning algorithm based on density clustering. DBSCAN aims to discover potential clustering structures by scanning the data set and looking for core objects and boundary objects to construct clusters.

7. Future development direction

Medical image data will also encounter many challenges in the development process, such as:

1. Scalability: The availability of medical image data continues to increase, but related tools require further development;
2. Lack of standards: Medical imaging data comes in a variety of formats and attributes, and often lacks unified standards;
3. Privacy protection: Personal privacy of medical imaging data has become a new sensitive issue;
4. Spatiotemporal heterogeneity: People in different countries and regions use the same equipment to take medical images, and the spatiotemporal distribution of data is also very inconsistent;
5. Computing resource limitations: The magnitude of medical imaging data is very large, so targeted analysis and processing requires a large amount of computing resources;

For better medical image data analysis, deep learning technology can play a role in effectively integrating the characteristics and challenges of medical image data. In this regard, the latest technologies in machine learning, computer vision, pattern recognition, database management and other fields can be used to help break through bottlenecks in the medical imaging field.