基于U-Net的递归残差卷积神经网络（R2U-Net）在医学图像分割中的应用.pdf

需积分: 5 23 浏览量 2023-11-22 10:58:56 上传评论收藏 1.73MB PDF 举报

基于U-Net的递归残差卷积神经网络（R2U-Net）在医学图像分割中的应用 U-Net是深度学习领域中一个非常流行的神经网络架构，经常应用于图像分割任务。然而，传统的U-Net架构存在一些缺陷，例如训练深度架构时容易出现梯度消失的问题。为了解决这些问题，本文提出了一种基于U-Net的递归残差卷积神经网络（R2U-Net），该网络架构结合了U-Net、残差网络和递归卷积神经网络的优点，实现了图像分割任务的高性能。 U-Net是一种Encoder-Decoder结构的神经网络，能够有效地提取图像特征，并将其应用于图像分割任务。然而，U-Net架构存在一些缺陷，例如训练深度架构时容易出现梯度消失的问题。为了解决这些问题，研究人员提出了残差网络（Residual Network），该网络架构能够有效地解决梯度消失的问题，并提高神经网络的训练效率。在本文中，我们提出了一种基于U-Net的递归残差卷积神经网络（R2U-Net），该网络架构结合了U-Net、残差网络和递归卷积神经网络的优点。该网络架构能够有效地提取图像特征，并实现图像分割任务的高性能。实验结果表明，R2U-Net模型在血管分割、皮肤癌分割和肺部损伤分割等图像分割任务中表现优于传统的U-Net和残差U-Net（ResU-Net）模型。 R2U-Net模型的架构可以分为两个部分：Encoder和Decoder。Encoder部分负责提取图像特征，Decoder部分负责将提取的特征进行反卷积，生成最终的图像分割结果。在R2U-Net模型中，我们使用了递归卷积神经网络（RCNN）来代替传统的卷积神经网络（CNN），该方法可以有效地提高图像特征的提取效率。实验结果表明，R2U-Net模型在血管分割、皮肤癌分割和肺部损伤分割等图像分割任务中表现优于传统的U-Net和残差U-Net（ResU-Net）模型。这些结果验证了R2U-Net模型的优越性，并表明其在图像分割任务中的应用前景广阔。本文提出了一种基于U-Net的递归残差卷积神经网络（R2U-Net），该网络架构结合了U-Net、残差网络和递归卷积神经网络的优点，实现了图像分割任务的高性能。实验结果表明，R2U-Net模型在图像分割任务中表现优于传统的U-Net和残差U-Net（ResU-Net）模型，验证了其在图像分割任务中的应用前景广阔。

资源推荐

资源详情

资源评论



Abstract—Deep learning (DL) based semantic segmentation

methods have been providing state-of-the-art performance in the

last few years. More specifically, these techniques have been

successfully applied to medical image classification, segmentation,

and detection tasks. One deep learning technique, U-Net, has

become one of the most popular for these applications. In this

paper, we propose a Recurrent Convolutional Neural Network

(RCNN) based on U-Net as well as a Recurrent Residual

Convolutional Neural Network (RRCNN) based on U-Net models,

which are named RU-Net and R2U-Net respectively. The proposed

models utilize the power of U-Net, Residual Network, as well as

RCNN. There are several advantages of these proposed

architectures for segmentation tasks. First, a residual unit helps

when training deep architecture. Second, feature accumulation

with recurrent residual convolutional layers ensures better feature

representation for segmentation tasks. Third, it allows us to design

better U-Net architecture with same number of network

parameters with better performance for medical image

segmentation. The proposed models are tested on three

benchmark datasets such as blood vessel segmentation in retina

images, skin cancer segmentation, and lung lesion segmentation.

The experimental results show superior performance on

segmentation tasks compared to equivalent models including U-

Net and residual U-Net (ResU-Net).

Index Terms—Medical imaging, Semantic segmentation,

Convolutional Neural Networks, U-Net, Residual U-Net, RU-Net,

and R2U-Net.

I. INTRODUCTION

OWADAYS DL provides state-of-the-art performance for

image classification [1], segmentation [2], detection and

tracking [3], and captioning [4]. Since 2012, several Deep

Convolutional Neural Network (DCNN) models have been

proposed such as AlexNet [1], VGG [5], GoogleNet [6],

Residual Net [7], DenseNet [8], and CapsuleNet [9][65]. A DL

based approach (CNN in particular) provides state-of-the-art

performance for classification and segmentation tasks for

several reasons: first, activation functions resolve training

problems in DL approaches. Second, dropout helps regularize

the networks. Third, several efficient optimization techniques

Md Zahangir Alom

, Chris Yakopcic

, Tarek M. Taha

, and Vijayan K.

Asari

are with the University of Dayton, 300 College Park, Dayton, OH,

45469, USA. (e-mail: {alomm1, cyakopcic1, ttaha1, vasari1}@udayton.edu).

are available for training CNN models [1]. However, in most

cases, models are explored and evaluated using classification

tasks on very large-scale datasets like ImageNet [1], where the

outputs of the classification tasks are single label or probability

values. Alternatively, small architecturally variant models are

used for semantic image segmentation tasks. For example, a

fully-connected convolutional neural network (FCN) also

provides state-of-the-art results for image segmentation tasks in

computer vision [2]. Another variant of FCN was also proposed

which is called SegNet [10].

Fig. 1. Medical image segmentation: retina blood vessel segmentation in the

left, skin cancer lesion segmentation, and lung segmentation in the right.

Due to the great success of DCNNs in the field of computer

vision, different variants of this approach are applied in

different modalities of medical imaging including

segmentation, classification, detection, registration, and

medical information processing. The medical imaging comes

from different imaging techniques such as Computer

Tomography (CT), ultrasound, X-ray, and Magnetic Resonance

Imaging (MRI). The goal of Computer-Aided Diagnosis (CAD)

is to obtain a faster and better diagnosis to ensure better

treatment of a large number of people at the same time.

Additionally, efficient automatic processing without human

involvement to reduce human error and also reduces overall

time and cost. Due to the slow process and tedious nature of

Mahmudul Hasan

, is with Comcast Labs, Washington, DC, USA. (e-mail:

mahmud.ucr@gmail.com).

Recurrent Residual Convolutional Neural

Network based on U-Net (R2U-Net) for

Medical Image Segmentation

Md Zahangir Alom

, Student Member, IEEE, Mahmudul Hasan

, Chris Yakopcic

, Member, IEEE,

Tarek M. Taha

, Member, IEEE, and Vijayan K. Asari

, Senior Member, IEEE

manual segmentation approaches, there is a significant demand

for computer algorithms that can do segmentation quickly and

accurately without human interaction. However, there are some

limitations of medical image segmentation including data

scarcity and class imbalance. Most of the time the large number

of labels (often in the thousands) for training is not available for

several reasons [11]. Labeling the dataset requires an expert in

this field which is expensive, and it requires a lot of effort and

time. Sometimes, different data transformation or augmentation

techniques (data whitening, rotation, translation, and scaling)

are applied for increasing the number of labeled samples

available [12, 13, and 14]. In addition, patch based approaches

are used for solving class imbalance problems. In this work, we

have evaluated the proposed approaches on both patch-based

and entire image-based approaches. However, to switch from

the patch-based approach to the pixel-based approach that

works with the entire image, we must be aware of the class

imbalance problem. In the case of semantic segmentation, the

image backgrounds are assigned a label and the foreground

regions are assigned a target class. Therefore, the class

imbalance problem is resolved without any trouble. Two

advanced techniques including cross-entropy loss and dice

similarity are introduced for efficient training of classification

and segmentation tasks in [13, 14].

Furthermore, in medical image processing, global

localization and context modulation is very often applied for

localization tasks. Each pixel is assigned a class label with a

desired boundary that is related to the contour of the target

lesion in identification tasks. To define these target lesion

boundaries, we must emphasize the related pixels. Landmark

detection in medical imaging [15, 16] is one example of this.

There were several traditional machine learning and image

processing techniques available for medical image

segmentation tasks before the DL revolution, including

amplitude segmentation based on histogram features [17], the

region based segmentation method [18], and the graph-cut

approach [19]. However, semantic segmentation approaches

that utilize DL have become very popular in recent years in the

field of medical image segmentation, lesion detection, and

localization [20]. In addition, DL based approaches are known

as universal learning approaches, where a single model can be

utilized efficiently in different modalities of medical imaging

such as MRI, CT, and X-ray.

According to a recent survey, DL approaches are applied to

almost all modalities of medical imagining [20, 21].

Furthermore, the highest number of papers have been published

on segmentation tasks in different modalities of medical

imaging [20, 21]. A DCNN based brain tumor segmentation and

detection method was proposed in [22].

From an architectural point of view, the CNN model for

classification tasks requires an encoding unit and provides class

probability as an output. In classification tasks, we have

performed convolution operations with activation functions

followed by sub-sampling layers which reduces the

dimensionality of the feature maps. As the input samples

traverse through the layers of the network, the number of

feature maps increases but the dimensionality of the feature

maps decreases. This is shown in the first part of the model (in

green) in Fig. 2. Since, the number of feature maps increase in

the deeper layers, the number of network parameters increases

respectively. Eventually, the Softmax operations are applied at

the end of the network to compute the probability of the target

classes.

As opposed to classification tasks, the architecture of

segmentation tasks requires both convolutional encoding and

decoding units. The encoding unit is used to encode input

images into a larger number of maps with lower dimensionality.

The decoding unit is used to perform up-convolution (de-

convolution) operations to produce segmentation maps with the

same dimensionality as the original input image. Therefore, the

architecture for segmentation tasks generally requires almost

double the number of network parameters when compared to

the architecture of the classification tasks. Thus, it is important

to design efficient DCNN architectures for segmentation tasks

which can ensure better performance with less number of

network parameters.

This research demonstrates two modified and improved

segmentation models, one using recurrent convolution

networks, and another using recurrent residual convolutional

networks. To accomplish our goals, the proposed models are

Fig. 2. U-Net architecture consisted with convolutional encoding and decoding units that take image as input and produce the segmentation feature maps with

respective pixel classes.

剩余11页未读，继续阅读

评论收藏

内容反馈