Overview of segmentation networks and implementation of various algorithms

Segmentation Network

A segmentation network can be a type of neural network for identifying different objects or regions in an image on a pixel-by-pixel basis and dividing them into segments (regions). It is mainly used in computer vision tasks and plays an important role in many applications because it can associate each pixel in an image to a different class or category.

There are two main types of segmentation networks

• Semantic Segmentation: Semantic segmentation is the task of mapping each pixel in an image to a different object or class. In semantic segmentation, the entire image is transformed into a segmentation map that is color-coded by class and used, for example, to identify different areas such as roads, cars, or buildings.
• Instance Segmentation: Instance segmentation is the task of identifying individual instances of different objects and dividing them into segments. Each object is represented by an independent segmentation map, which also distinguishes between multiple objects belonging to the same class. This is used, for example, to identify individual instances of cars or people.

Common architectures for segmentation networks include U-Net, Mask R-CNN, and FCN (Fully Convolutional Network). These architectures use elements such as encoders and decoders, skip connections, etc., to combine the overall image context with local information to produce segmentation results.

Segmentation networks are used in many application areas such as medical image analysis, automated driving, video editing, and object tracking, making them an important technology in computer vision research and practice.

For more information on U-net, see also “Overview of U-net and examples of algorithms and implementations“.

Application examples of segmentation networks

Segmentation networks have been widely applied in various domains. The following are some of the major applications of segmentation networks.

• Medical image analysis: Segmentation networks are used in medical image analysis, such as MRI and CT scans, for example, to detect and classify brain tumors and to segment organs.
• Automated Driving: In automated driving technology, segmentation networks are used to segment objects, vehicles, and pedestrians on the road to help understand their surroundings.
• Agriculture: In agriculture, segmentation is used to monitor the condition of crops and the presence of pests. This allows for optimization of agricultural operations and early detection of pests and diseases.
• Video editing: In video editing, segmentation networks are used to segment specific objects or backgrounds and edit each element, e.g., to add or remove specific objects.
• Environmental Monitoring: Segmentation of vegetation and landforms is important for monitoring the natural environment. This information can be used to assess forest health and monitor topographic changes.
• Satellite image analysis: Segmentation networks are used to extract specific areas or elements of the earth’s surface from satellite images. This has applications in urban planning and geographic information systems (GIS).
• Biological Research: Segmentation of cells and tissues is important in biological research. This is used to detect cell nuclei and analyze protein localization.
  Security surveillance: Segmentation of people and vehicles from security camera footage is used to detect suspicious behavior and intrusions.

These examples illustrate the diverse application areas of segmentation networks. Segmentation networks are effectively used in a variety of tasks due to their ability to identify and segment specific objects and regions pixel by pixel.

The following sections describe the architectures of various segmentation networks.

U-Net

<Overview>

U-Net will be a network architecture designed for semantic segmentation. Semantic segmentation is the task of assigning each pixel in an image to a different class (category), and U-Net is known to show particularly useful performance in segmentation tasks.

The U-Net architecture consists of an encoder and a decoder. The encoder is the part that downsamples the image to extract features, and the decoder is the part that upsamples the features to generate a segmentation map. the shape of the U-Net architecture is derived from its “U”-like appearance, hence its name.

U-Net is widely used in fields such as medical image segmentation and is an excellent network architecture for achieving high accuracy with relatively small amounts of data. Furthermore, the U-Net architecture can be extended and improved, and many variations exist.

<Algorithms>

The following is an overview of the U-Net algorithm.

• Encoder (downsampling path):
  Input images are fed into the network, and feature maps are downsampled through convolutional and pooling layers.
  At each stage of the encoder, the resolution of the feature map is reduced as the number of feature channels increases. This allows extensive context information to be captured.
• Decoder (up-sampling path):
  The feature map downsampled by the encoder is restored to its original resolution through an upsampling operation.
  At each stage of the decoder, the up-sampled feature maps are combined with the feature maps from the corresponding stage of the encoder. This integrates the local features with the broader context information.
• Coupling Layer (Skip Connections):
  Feature maps from the corresponding stages of the encoder and decoder are combined through the coupling layer. This combines low-level detail with high-level semantics information.Skip connections help the network to accurately capture object boundaries and small detailed features in particular.
• Segmentation Layer:
  At the final stage of the decoder, a segmentation map is generated by the segmentation layer.
  At the segmentation layer, the feature map is passed through a 1×1 convolution layer to predict the segmentation class for each pixel.

The overall algorithm is a basic process of passing the input image through an encoder to extract features, followed by up-sampling and feature merging at the decoder to generate the segmentation map. features are merged, improving the accuracy of semantic segmentation.

<Implementation>

An example implementation of U-Net is shown using Python and PyTorch. The following is a basic example implementation of semantic segmentation using U-Net.

import torch
import torch.nn as nn

class UNet(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(UNet, self).__init__()
        
        # encoder
        self.encoder = nn.Sequential(
            nn.Conv2d(in_channels, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        
        # decoder
        self.decoder = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        )
        
    def forward(self, x):
        # encoder
        x1 = self.encoder(x)
        
        # decoder
        x2 = self.decoder(x1)
        
        return x2

# Create a U-Net model by specifying the number of input and output channels
model = UNet(in_channels=3, out_channels=1)

In this example, the U-Net model is defined by a convolution layer, a pooling layer, and an up-sampling layer for each part of the encoder and decoder. The model is converting from 3 input channels (RGB images) to 1 output channel (segmentation map).

Mask R-CNN

<Overview>

Mask R-CNN is a model that combines object detection and semantic segmentation and is based on the architecture of Faster R-CNN (Object Detection), a neural network to provide per-pixel segmentation of object interior as well as object bounding boxes.

Mask R-CNN is a model that combines object detection and semantic segmentation and is a neural network architecture for providing segmentation not only at the bounding box of an object, but also at the pixel level inside the object. By performing both object detection and segmentation tasks simultaneously, this model allows for accurate estimation of not only object location and class information, but also object shape and region Mask R-CNN can be very useful for a task called instance segmentation The Mask R-CNN is used to estimate the shape of objects and regions.

The main components of Mask R-CNN are described below.

• Encoder (object detection part): The encoder part of the object detection network, called Faster R-CNN, forms the basic structure of Mask R-CNN. This encoder is used to detect the bounding boxes of objects in the image.
• Decoder (segmentation part): After the encoder, a decoder for segmentation is added. This decoder performs a pixel-by-pixel segmentation of the interior of the object for each object region detected by the object detection section. The decoder is used to upsample the feature map to estimate the shape of the object interior.
• Mask Prediction Layer: The decoder includes a mask prediction layer to generate a segmentation mask for each object. This layer predicts for each pixel in the bounding box of each object the probability that it belongs to the object interior. This allows the shape and area of the object to be estimated.

<Algorithm>

This section outlines the main steps and algorithms of Mask R-CNN.

1. Backbone Network Feature Extraction: In the first step, the input image is passed to the backbone (usually a convolutional neural network) and feature maps at different levels are extracted. This captures information at different resolutions of the image.
2. Generation of Object Candidates (Object Detection): In the Object Detection step, the bounding boxes of candidate objects and object classes are predicted. Usually, a module called Region Proposal Network (RPN) is used to propose potential object regions in the image. This identifies candidate object regions in the image.
3. RoI Align: For each candidate object region (bounding box), a RoI Align (Region of Interest Align) operation is performed to extract a feature map from the backbone. This extracts the features corresponding to each object region.
4. Class prediction and bounding box fine-tuning: Using the feature maps obtained by RoI Align as input, object class prediction and bounding box fine-tuning are performed. The class and location of each object candidate is then estimated.
5. Segmentation mask generation: A decoder is used to generate a segmentation mask for the object regions detected by the object detection section. The decoder upsamples the backbone feature map to generate segmentation information inside the object.
6. Segmentation mask prediction: The segmentation mask generated by the segmentation decoder predicts, for each pixel corresponding to each candidate object region, the probability of belonging to the object interior. This allows the shape and region of the object to be estimated as a segmentation.

In this way, Mask R-CNN combines object detection and semantic segmentation to simultaneously estimate the position, class, shape, and region of an object in an image. The integrated approach of the algorithm helps to achieve high accuracy in both object detection and segmentation tasks.

<Implementation>

An example implementation using Python for Mask R-CNN and PyTorch, a deep learning framework, is shown below. The following code is a basic implementation example.

import torch
import torchvision
from torchvision.models.detection import maskrcnn_resnet50_fpn
from torchvision.transforms import functional as F
from PIL import Image

# Load pre-trained Mask R-CNN model
model = maskrcnn_resnet50_fpn(pretrained=True)
model.eval()

# Load and preprocess an input image
image_path = 'path_to_your_image.jpg'
image = Image.open(image_path)
image_tensor = F.to_tensor(image).unsqueeze(0)

# Make predictions
with torch.no_grad():
    predictions = model(image_tensor)

# Get bounding boxes, labels, masks from predictions
boxes = predictions[0]['boxes'].cpu().numpy()
labels = predictions[0]['labels'].cpu().numpy()
masks = predictions[0]['masks'].cpu().numpy()

# Visualize the results
import matplotlib.pyplot as plt
import numpy as np

plt.imshow(image)
ax = plt.gca()

for box, label, mask in zip(boxes, labels, masks):
    x, y, x_max, y_max = box
    rect = plt.Rectangle((x, y), x_max - x, y_max - y, fill=False, color='r')
    ax.add_patch(rect)
    
    mask = np.squeeze(mask)
    mask = np.where(mask, 1, 0)  # Convert mask to binary
    plt.imshow(mask, alpha=0.3, cmap='gray')

plt.show()

The above code loads a pre-trained Mask R-CNN model, performs object detection and semantic segmentation on the input image, and visualizes the results.

See also “Overview of Search Algorithms and Various Algorithms and Implementations” for more details.

FCN(Fully Convolutional Network)

<Overview>

Fully Convolutional Network (FCN) is a neural network architecture that performs feature extraction and segmentation using only the convolutional layer in image segmentation tasks such as semantic segmentation and instance segmentation. FCNs derive their name from the fact that they do not contain a full concatenation layer.

The main idea of FCNs is to achieve per-pixel segmentation by using the convolutional layer to predict the class for each pixel in the image, which is not suitable for segmentation tasks because conventional convolutional networks are limited by the fixed input size provided by the full-combining layer. However, FCNs are designed to overcome this limitation and allow flexible segmentation of input images of arbitrary size.

FCN is widely used as an architecture that provides high performance and flexibility in segmentation tasks such as semantic segmentation. Subsequently, derivative models such as U-Net and Mask R-CNN have been developed to further improve various aspects of segmentation.

<Algorithm>

The basic steps and overview of the FCN algorithm are described below.

1. Encoder (feature extraction part):
   • A pre-trained convolutional neural network (usually VGG or ResNet as described in “About ResNet (Residual Network)”) is used as the encoder. This network is used to extract features from the image.
   • Feature maps from the middle layer of the encoder are extracted through convolutional operations. This allows low-level to high-level information in the image to be captured.
2. Decoder (up-sampling section):
   • The feature maps from the encoder are upsampled back to the original image resolution. This allows for pixel-by-pixel class prediction.
   • Upsampling involves multiple convolution and upsampling layers. The up-sampling operation is performed to restore the feature map to the original resolution and generate a segmentation map.
3. Skip Connections:
   • The feature maps of the corresponding layers of the encoder and decoder are combined via skip connections. This combines high-resolution decoder features with low-resolution encoder features.
   • Skip connections help integrate local details and wide-area context information, which contributes to improved segmentation accuracy.

The overall algorithm is a basic process of passing the input image through the encoder to extract features, and then generating a segmentation map with up-sampling and feature combination at the decoder. The advantage of this architecture is that it allows segmentation prediction for each pixel in the image and can accommodate flexible input sizes.

FCN has shown excellent results in semantic segmentation and has greatly influenced the subsequent development of the model, making FCN an important tool in image analysis as an example of an architecture dedicated to the segmentation task.

<Implementation>

An example implementation of FCN is shown below. The following code is an example implementation of an FCN model for semantic segmentation using Python and PyTorch.

import torch
import torch.nn as nn
import torchvision.models as models

class FCN(nn.Module):
    def __init__(self, num_classes):
        super(FCN, self).__init__()
        # Loaded pre-trained VGG16 models
        vgg16 = models.vgg16(pretrained=True)
        
        # Set to not use the last layer of VGG16
        features = list(vgg16.features.children())
        self.encoder = nn.Sequential(*features)
        
        # Decoder part of FCN
        self.decoder = nn.Sequential(
            nn.Conv2d(512, 4096, kernel_size=7),  # Convert all coupled layers to convolutional layers
            nn.ReLU(inplace=True),
            nn.Dropout2d(),
            nn.Conv2d(4096, 4096, kernel_size=1),
            nn.ReLU(inplace=True),
            nn.Dropout2d(),
            nn.Conv2d(4096, num_classes, kernel_size=1)  # Convolution layer corresponding to the number of classes
        )
        
    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

# Create FCN model with number of classes
num_classes = 21  # Number of classes of semantic segmentation tasks in PASCAL VOC
model = FCN(num_classes)

In this example, a pre-trained VGG16 model is used as the encoder, followed by a defined decoder part. The encoder part is responsible for feature extraction, while the decoder part performs up-sampling and segmentation prediction from the feature map.

Reference Information and Reference Books

For details on image information processing, see “Image Information Processing Techniques.

Reference book is “Image Processing and Data Analysis with ERDAS IMAGINE“

“Hands-On Image Processing with Python: Expert techniques for advanced image analysis and effective interpretation of image data“

“Introduction to Image Processing Using R: Learning by Examples“

“Deep Learning for Vision Systems“