PCAF‐Net: A liver segmentation network based on deep learning

IET Image ProcessingVolume 16, Issue 1 p. 229-238 ORIGINAL RESEARCH PAPEROpen Access PCAF-Net: A liver segmentation network based on deep learning Yanlin Wu, Yanlin Wu orcid.org/0000-0002-2087-275X College of Electronic and Information Engineering, Hebei University, Hebei, ChinaSearch for more papers by this authorGuanglei Wang, Corresponding Author Guanglei Wang guangleiwang@hbu.edu.cn College of Electronic and Information Engineering, Hebei University, Hebei, China Correspondence Guanglei Wang, College of Electronic and Information Engineering, Hebei University, Hebei 071002, China Email: guangleiwang@hbu.edu.cn.Search for more papers by this authorZhongyang Wang, Zhongyang Wang College of Electronic and Information Engineering, Hebei University, Hebei, ChinaSearch for more papers by this authorHongrui Wang, Hongrui Wang College of Electronic and Information Engineering, Hebei University, Hebei, ChinaSearch for more papers by this author Yanlin Wu, Yanlin Wu orcid.org/0000-0002-2087-275X College of Electronic and Information Engineering, Hebei University, Hebei, ChinaSearch for more papers by this authorGuanglei Wang, Corresponding Author Guanglei Wang guangleiwang@hbu.edu.cn College of Electronic and Information Engineering, Hebei University, Hebei, China Correspondence Guanglei Wang, College of Electronic and Information Engineering, Hebei University, Hebei 071002, China Email: guangleiwang@hbu.edu.cn.Search for more papers by this authorZhongyang Wang, Zhongyang Wang College of Electronic and Information Engineering, Hebei University, Hebei, ChinaSearch for more papers by this authorHongrui Wang, Hongrui Wang College of Electronic and Information Engineering, Hebei University, Hebei, ChinaSearch for more papers by this author First published: 29 September 2021 https://doi.org/10.1049/ipr2.12346AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Liver cancer poses a great threat to people's health. Accurate liver segmentation is crucial to the diagnosis of liver cancer. In recent years, great achievements have been made in liver segmentation using a series of improved networks developed based on U-Net. U-Net uses a skip connection splices the feature map in the encoder and decoder. However, this method ignores the difference between the two feature maps, which limits the ability of the network to extract liver structures of different sizes. This study proposes a new network structure, the pyramid convolutional attention fusion network (PCAF-Net), for 2D liver segmentation. The PCAF mechanism was introduced to fuse the feature maps of different receptive field paths after attention mechanism processing to enhance the semantic expression ability of feature maps in skip connection and to improve the segmentation accuracy. The MICCAI 2017 LiTS dataset and CHAOS were used to validate the proposed method. The results of multi-index evaluation showed that the segmentation performance of the proposed network was superior to those of other networks. PCAF-Net achieved accurate liver segmentation, providing a reference for artificial intelligenceassisted clinical diagnosis of liver cancer. 1 INTRODUCTION The liver is one of the most important organs in the human body, with a complex anatomy and vascular system. Liver cancer is the second-leading cause of cancer-related death in men and the sixth in women, so it poses a serious threat to human health and life. The identification of the liver position and contour plays a crucial role in the segmentation of liver tumours. Therefore, accurate liver segmentation has become the primary task of liver cancer diagnosis and treatment. Due to the differences in size, position, and shape of the liver in different computed tomography (CT) images, as well as the unclear edge contours of the liver and the similar intensity values to those of adjacent organs, accurate liver segmentation is a difficult problem. In clinical diagnosis of liver diseases, several manual and semi-manual segmentation techniques have been applied to CT and magnetic resonance imaging (MRI) images. However, most of these technologies are subjective, experience dependent, and time consuming. To improve the work efficiency of radiologists, computer-aided diagnosis and treatment are gradually applied to the diagnosis of liver diseases. Traditional liver segmentation methods include the graph-cut method [1], level set method [2-5], and machine learning method [6, 7]. However, these methods have poor robustness and low generalizability. In recent years, deep learning has brought profound changes to the field of artificial intelligence. The deep learning algorithm represented by the convolutional neural network has achieved great success in various fields and has attracted widespread attention. With its continuous development, various applications for liver segmentation have been proposed [34-38]. The codec structure network represented by U-Net [8] has played a key role in the development of medical image segmentation. U-Net uses the codec structure to achieve end-to-end training and uses a skip connection method to connect the encoder path to the decoder path. The feature maps in the paths are spliced together to enrich the semantic information contained in the feature maps in the decoder path. However, U-Net has limited ability to process liver structures of different sizes in the sequence image. The direct fusion of the unprocessed feature map in the encoder path with the feature map in the decoder path through skip connection limits the performance of the fusion process in enriching the semantic information of the feature map, such that the network reduces inter-class differences and increases intra-class differences to a certain extent, which lead to over-segmentation at edges and mis-segmentation of internal regions of the liver. To solve the above problems, we propose a pyramid convolutional attention fusion network (PCAF-Net). Based on U-Net, the proposed network structure improves skip connection by introducing the PCAF module. The pyramidal structure compensates for the U-Net's shortcomings in processing multi-scale information in the segmentation task. The attention mechanism is used to filter the features that are beneficial to the segmentation and to enrich the semantic information in the feature maps. In summary, the network can identify more useful features to improve the segmentation performance before feature map fusion in the up-sampling process. 2 BACKGROUND 2.1 Liver segmentation Common methods used in liver segmentation include texture-based methods [9-11], level set methods [2-5], graph-cut methods [1], and machine learning [6, 7]. Although these methods are relatively mature and have achieved promising segmentation results, they still have some shortcomings. For example, they have strict requirements for image pre-processing and often require manual segmentation. These limitations greatly affect their speed and robustness, especially when dealing with CT images with low contrast and wide variations in liver shape. In recent years, convolutional neural networks have become a very useful tool in the field of computer vision. The fully convolutional networks proposed by Long et al. [12] enable end-to-end training. Proposed by Ronneberger et al. [8], U-Net enables the wide application of semantic segmentation in medical imaging segmentation. U-Net consists of an encoder and a decoder, which are used for feature extraction and image reconstruction, respectively. Thanks to its excellent performance in medical image segmentation, it has been used for automatic liver segmentation in CT images on various occasions. Many improved U-Net networks have been developed, such as the H-DenseUNet proposed by Li [13], which included a new hybrid dense connection to combine two-dimensional (2D) DenseUNet and three-dimensional (3D) DenseUNet and achieved excellent segmentation results using the single model. Fan et al. [14] proposed MSN-Net, which introduced a multi-scale information fusion module in the skip connection and used dense connections between each pair of fusion modules. H-DenseUNet [13] and MSN-Net [14] have enhanced feature map reusability and multi-scale information, yet the networks lack the ability to enhance and filter feature maps. Jin et al. [15] proposed RA-Unet, which introduced the attention residual mechanism into U-Net, and applied it to the extraction of liver tissue and liver tumours. Chen et al. [16] proposed Channel-Unet, which added spatial channel convolution in the up-sampling and down-sampling modules to extract the spatial mapping relationship between pixels. Although RA-Unet [15] and Channel-Unet [16] can enhance and filter features, they cannot effectively process images with large liver tissue variations. Since the networks lack feature information, their segmentation performance is impaired. To solve the above problems, this study fused multi-scale information and the attention mechanism in the PCAF module. This allowed the network to strengthen and filter the features of the feature map during the network propagation process while gaining the ability to process different liver sizes on CT images. 2.2 Attention mechanism The visual attention mechanism is a brain signal processing mechanism unique to mammalian vision. With the development of computer vision, the attention mechanism is combined with the codec framework to be applied to various fields of computer vision, which has yielded excellent results. SENet [17], a typical representative of the attention mechanism, has been widely used in various network structures. It uses a feature recalibration strategy to explicitly model the interdependence between feature channels. The importance of different channels in the feature map is automatically obtained via learning and is used as a reference to improve the expression of beneficial features for target segmentation and to suppress features that have less impact on the segmentation. Attention mechanisms such as SENet [17] perform poorly when dealing with objects with excessive shape changes, making them unsuitable for segmentation of CT images when used alone. 2.3 Pyramidal structure The idea of the pyramid [18-20] has always played an important role in the field of deep learning and is a common computer vision processing method. The multi-scale idea is in line with human observation of surrounding objects, it processes objects of different sizes differently and then fuses the results such that the network can obtain objects of different sizes in the image. Duta et al. recently proposed pyramidal convolution (PyConv) [21]. Compared with ordinary convolution operations, the pyramidal structure of PyConv can process multi-scale information to provide an effective solution to the problem of large differences in liver contours. While improving the accuracy of segmentation, it also lowers the number of parameters and thereby saves computing resources. However, it lacks the ability to filter features. The same processing is used for the feature maps obtained in the channel dimension, while ignoring that the feature maps in different channels contribute differently to the segmentation task. Thus, the performance of PyConv is limited. In view of the above analysis, this study combined the channel attention mechanism [17] and PyConv [21], and then the position attention mechanism [22] was introduced to form PCAF-Net for liver segmentation. The main contributions of this study are as follows: The PCAF module. This module introduces both multi-scale processing and the channel attention mechanism. The channel dimensions of the feature maps were recalibrated in each processing path such that the feature map channels in different processing paths had different weights, which made up for the shortcomings of the pyramidal structure that cannot filter features when processing information at different scales. The position attention mechanism. It was added to the transition layer of U-Net, which provides rich contextual connections in local features such that broader contextual information was encoded in the local features, thereby enhancing feature representation capabilities, aggregating global semantic information, and improving segmentation performance. 3 METHODS 3.1 Flow chart of the algorithm The flow chart of the algorithm is shown in Figure 1. The dataset was divided into two groups: a training dataset and a testing dataset. The training dataset was further divided into a training set and a validation set during the training stage. The original images in the training set were first subjected to pre-processing such as centre-cutting and normalization. After preprocessing, the images were input into the network, and after processing by the codec path and PCAF module, the results were output, and the cross-entropy loss was calculated, the result of which was used for back-propagation. The validation set was used to screen the optimal network model for testing. The data in the testing dataset was loaded into the optimal network model after normalization and other preprocessing steps, and the loss, mean intersection over union (MIoU), and accuracy were then calculated. FIGURE 1Open in figure viewerPowerPoint PCAF-Net flow chart 3.2 PCAF-Net The PCAF-Net structure proposed is shown in Figure 2. The PCAF module, an innovation of this study, was added to the skip connection to strengthen the semantic expression ability of the feature map. Moreover, the position attention mechanism was added to the transition layer to aggregate global semantic information, thereby improving the performance of the network. FIGURE 2Open in figure viewerPowerPoint PCAF-Net structure 3.2.1 PCAF module The structure of the PCAF module is shown in Figure 3. It consists of two layers, the PyConv layer and the attention layer. The input feature maps in the PyConv layer were processed in three paths. The sizes of the convolutional kernels in the convolution layer from top to bottom were 5, 3, and 1. As the convolutional kernel size increased, the number of channels decreased, forming a positive pyramidal structure in terms of the number of channels and an inverted pyramidal structure in terms of the convolutional kernel size. FIGURE 3Open in figure viewerPowerPoint Pyramid convolutional attention mechanism The structure of the PyConv layer is shown in Figure 4. It had three levels, and the sizes of the convolution kernels were 5, 3, and 1 from top to bottom. The size-5 kernel was replaced with two kernels of size 3, which increased the depth of the network under the same receptive field and strengthened the non-linearity of the network. FIGURE 4Open in figure viewerPowerPoint Pyramidal structure The corresponding number of channels was Xi/4, Xi/4, and Xi/2 (Xi is the number of input channels), and the parameters can be calculated using Equation (1): P = ∑ i n K i 2 × I i × O i (1)where i = 1, 2, 3, is the number of layers in the pyramid, K = 1, 3 is the convolutional kernel size, I is the number of input channels in the feature map, and O is the number of output channels. Under multi-scale conditions, the parameters of the model were not increased much. The number of parameters was compared with those of the normal 3×3 convolution kernel (Table 1). As the number of channels increased, the advantages of PyConv became more and more obvious. When the input channel is 512, the number of PyConv parameters was 19.44% less than that of traditional convolution. TABLE 1. Number of parameters in pyramidal convolution and traditional convolution Input channel Output channel Pyramidal convolution Traditional convolution 64 64 29696 36864 128 128 118784 147456 256 256 475136 589824 512 512 1900544 2359296 TABLE 2. Ablation experiment Method IOU DICE RVD U-Net 0.87155 0.91549 −0.03816 U-Net+PAM 0.87704 0.92001 −0.02109 U-Net+PCAF 0.87854 0.92394 −0.01164 PCAF-Net 0.88621 0.92782 −0.01432 The feature map processed by the PyConv layer entered the attention layer, and the channels of the feature maps were recalibrated through the attention mechanism to strengthen the features useful for segmentation and suppress irrelevant features. The attention mechanism was composed of two operations: Squeeze and Excitation. The specific structure of each operation is shown in Figure 5. FIGURE 5Open in figure viewerPowerPoint Squeeze and Excitation module In the convolutional process (X ∈ RH'W'C' → U ∈ RHWC), the convolution kernel is V = {v1, v2, v3, …, vc}, where vc represents the cth convolution kernel, and the output U = {u1, u2, …, uc}: u n = v n X , n = 1 , … , c (2)X represents the input feature map, v n represents a convolution kernel, u n represents the output feature map. Because convolutional operation is performed in a local space, it is difficult for U to obtain enough information to extract the relationship between the channels. In a shallow network, due to the small receptive field, this impact is more obvious. Therefore, it used global average pooling in the Squeeze operation to compress the spatial information on a channel into a global feature: z c = F s q ( u c ) = 1 H × W ∑ i = 1 H ∑ j = 1 W u c ( i , j ) (3) The essence of Equation (3) is the average of all the values of the matrix represented by the feature map. After the Squeeze operation, a 1×1×C feature vector was obtained. Then, the fully connected (FC), rectified linear unit (ReLU), FC (again), and sigmoid operations were performed to ultimately achieve the purpose of controlling the network traffic: S = F e x ( Z , W ) = σ ( W 2 δ ( W 1 Z ) ) (4) where δ is the ReLU activation function, σ is the sigmoid activation function. The W1 and W2 represents full connection operation. S represents attention vector of channel dimension. W 1 ∈ R C r × C , and W 2 ∈ R C × C r , r represents the compression ratio, which is used to reduce the model parameters. The first FC layer was used to reduce the model complexity and the number of calculations, and the original dimension was restored in the second FC layer, and then the activation value of each channel was multiplied by U: X ∼ = F scale ( U , S ) (5) X ∼ is the feature map processed by the attention mechanism. After processing using the three paths, the channel dimensions were then fused with the input feature map to output the final feature map. 3.2.2 Position attention module The structure of the position attention module is shown in Figure 6. For the input feature map A ∈ R C × H × W , after a convolutional operation with a batch normalization (BN) layer and a ReLU layer, two new feature maps B and C were obtained, where { B , C } ∈ R C × H × W , which could be transformed to { B ′ , C ′ } ∈ R C × N , where N = H × W . Then, the transpose of B ′ was multiplied by C ′ , and the position attention feature map was obtained after the softmax activation layer: S ∈ R N × N : S i j = exp ( B i · C j ) ∑ i , j = 1 N exp ( B i · C j ) (6)where Sij represents the influence of the ith position on the jth position, and the more similar the features of the two positions are, the greater they influence Sij. FIGURE 6Open in figure viewerPowerPoint PAM The feature map A was then processed with a convolution layer with a BN layer and a ReLU layer to generate a feature map D ∈ R C × H × W , which was transformed into D ′ ∈ R C × N . The output was R C × H × W after applying matrix multiplication to D ′ and S. By multiply the output by a factor α and then adding it to A, the final output was obtained: E ∈ R C × H × W . E j = α ∑ i = 1 N ( S j i D i + A j ) (7)where α starts at 0 and is gradually learned during training. It can be seen from Equation (7) that each position of E is the weighted sum of the features of all positions and the original features, so it aggregates the global semantic information. 3.3 Feature map extraction To verify the effectiveness of the PCAF and PAB modules, we extracted the feature maps in the original U-Net and PCAF-Net, as shown in Figure 7. The transfer of feature maps in the original U-Net skip connection was oversimplified, and the feature maps transferred by skip connection in PCAF-Net were processed by the PCAF module, which were then divided into three channels for separate processing and fusion. Therefore, the latter method provided feature maps with richer semantic information for the up-sampling process. In addition, the feature maps of the two networks after the first convolution layer in the last up-sampling were extracted. The red area in the figure indicates the liver on the CT image, the red arrow points to the internal area of the liver, and the green arrow points to the critical area at the liver edge adjacent to other surrounding tissues. From the two feature maps, it can be seen that the area pointed at by the green arrow in the PCAF-Net feature map showed high contrast, indicating high contrast between the liver and surrounding tissue because of enhanced inter-class inconsistency. The intensities of the pixel values in the red-arrow-pointed area were similar, indicating low contrast between intrahepatic tissues because of enhanced intra-class consistency. Because of the greater inter-class inconsistency and intra-class consistency, the number of mis-segmented pixels in the result was lower, thus improving the segmentation accuracy of the network. FIGURE 7Open in figure viewerPowerPoint Feature map extraction FIGURE 8Open in figure viewerPowerPoint Comparison of various networks 4 EXPERIMENT AND RESULTS 4.1 Data The MICCAI 2017 LiTS dataset was used and all experimental data were 2D images. It included 10,478 images in the training set, 4279 images in the verification set, and 4406 images in the testing dataset. To save on computational costs, the input images were cropped to 352×352 pixels. Moreover, in order to verify the generalization of our proposed model, experiments are also carried out on CT images of CHAOS dataset. During the experiment, we deleted some pictures that did not contain liver region in the CHAOS dataset, and finally got 2140 pictures. The experiment frame was PyTorch, and the maximum number of training cycles was set to 40. After the model converges, in order to prevent over fitting, the strategy of early stopping was adopted. The Adam optimization method was used with an initial learning rate of 0.0001, and the learning rate was reduced by half every three training cycles. The experiments were all carried out on the Quadro RTX 8000 platform. 4.2 Evaluation indices To evaluate the performance of the network proposed, the following indicators were used as the evaluation criteria: R V D = A − B B (8) D I C E = 2 × A ∩ B A + B (9) I O U = A ∩ B A ∪ B (10)where A and B represent the number of pixels with a value of 1 in the predicted label image and that in the true label image, respectively. DICE ∈ (0, 1), intersection over union (IoU) ∈ (0, 1), the closer the DICE and IoU are to 1, the better the segmentation performance, and the closer the relative volume difference (RVD) is to 0, the better the segmentation performance. 4.3 Ablation experiment Under the same experimental environment, we set U-Net as the reference network, and the Position Attention Module (PAM) module and PCAF module were gradually added for the ablation experiment. From Table 2, it can be seen that compared with the reference network, the addition of PAM modules resulted in an increase of 0.549% and 0.452% in MIoU and mean DICE, respectively. After adding the PCAF module, MIoU and mean DICE increased by 0.699% and 0.845%, respectively. These results prove the effectiveness of PAB and PCAF modules. The MIoU and mean DICE of the PCAF-Net reached 88.621% and 92.782%, respectively, which were 1.466% and 1.233% higher than the values of the reference network, indicating that the PCAF-Net can effectively improve the segmentation accuracy. 4.4 Comparative experiment To verify the superiority of the network proposed, we compared the PCAF-Net with commonly used networks on LiTS and CHAOS datasets, including U-Net [8], SegNet [23], ResUnet [24], and Unet++[25]. The results are shown in the Table 3. TABLE 3. Comparison experiment LiTS CHAOS Method IOU DICE RVD IOU DICE RVD ExfuseNet [26] 0.73837 0.81028 −0.10675 0.80243 0.87446 −0.11877 Deeplabv3+ [27] 0.74519 0.82244 −0.13744 0.81128 0.89110 −0.01682 ResUnet [24] 0.78824 0.86010 −0.06641 0.85894 0.92201 0.03978 SegNet [23] 0.85636 0.90473 −0.06816 0.90224 0.94673 0.00379 U-Net [8] 0.87155 0.91549 −0.03816 0.86544 0.92241 −0.06961 Att-Unet [32] 0.87179 0.91658 −0.01450 0.92603 0.96084 −0.00500 U-Net++ [25] 0.87372 0.92073 0.02202 0.91162 0.95264 −0.01261 Unet+++ [33] 0.88075 0.92558 −0.02011 0.90423 0.94715 −0.03094 PCAF-Net 0.88621 0.92782 −0.01432 0.92676 0.96115 −0.01698 Figure 8 shows a histogram of three indicators of each network on LiTS. PCAF-Net was superior to other networks in all three indicators . U-Net has been considered the most classic network in medical image segmentation. In this experiment, the number of U-Net parameters was 17.27 million, while that of PCAF-Net, Unet++, Unet+++, Att-Unet, and SegNet was 24.97 million, 28.80 million, 26.97 million, 34.88 million and 29.44 million, respectively. Thus, PCAF-Net achieved excellent segmentation results without introducing excessive parameters, which proves the superiority of PCAF-Net for liver segmentation tasks. 5 DISCUSSION Based on the basic framework of the traditional U-Net, innovative modules were added to the skip connection. In the initial design, the attention mechanism used in skip connection was the channel attention module (CAM) in DANet [22]. An advantage of CAM is that it retains 2D information of the feature maps rather than compressing the feature maps into one dimension of information. However, the experimental performance of CAM is not ideal. Thus, retaining 2D information is not always effective when processing 2D images with U-Net [8]. We introduced high- and low-dimensional feature map information into the skip connection structure, but this did not increase the accuracy. When the introduction of high- and low-dimensional information failed, we shifted our attention to multi-scale information. After analysing the GoogLeNet [28-31] network structures and PyConv [21], we proposed the PCAF module, in which the PyConv structure fused the feature maps after multi-channel processing, which overcomes the network's shortcomings in multi-scale information processing. The attention mechanism provides more effective features for the network and gives the network the ability to screen features. The experimental results showed that the addition of multi-scale and attention mechanisms to the skip connection effectively enhances the inter-class inconsistency and intra-class consistency, thereby clearly distinguishing the edge of the liver from other tissues, avoiding over-segmentation while reducing the contrast of intrahepatic tissues, reducing mis-segmentation inside the liver, and ultimately improving the segmentation performance of network. Figure 9 compares the segmentation results of PCAF-Net, ExfuseNet [26], and DeeplabV3+ [27]. The red areas are mis-segmented pixels. PCAF-Net significantly reduces edge and internal adhesions, resulting in smoother edges, and significantly reduces the number of mis-segmented pixels. Automatic segmentation of the liver and tumours plays an important role in clinical diagnosis. PCAF-Net provides automatic and accurate segmentation and avoids the subjectivity and variability of manual segmentation, which not only can save radiologists time but also can enable doctors to diagnose and treat diseases more accurately. FIGURE 9Open in figure viewerPowerPoint Segmentation results 6 CONCLUSION This study proposed the PCAF-Net for liver segmentation. The PCAF module was added to the skip connection of U-Net to reduce the difference in the feature maps in the codec path before feature fusion. A position attention mechanism was added to the transition layer of U-Net to enhance the ability of the network to extract features. The effectiveness of PCAF and PAB modules was verified by extracting the feature maps of the intermediate process of the network. The experimental results showed that the MIoU and mean DICE of PCAF-Net reached 88.621% and 92.782%, respectively. The segmentation results proved the superiority of PCAF-Net over other networks. PCAF-Net can help surgeons diagnose and treat liver diseases while reducing their workload so they can focus more on treatment. In the next step, we will focus on 3D liver segmentation, test the performance of PCAF-Net in 3D segmentation, and attempt to simplify the structure and parameters of the network to reduce computational costs and achieve a more lightweight network. AUTHOR CONTRIBUTIONS Yanlin Wu: Conceptualization; formal analysis; methodology; project administration; software. Guanglei Wang: Funding acquisition; project administration; resources; writing – original draft; writing – review and editing. Zhongyang Wang: Data curation; investigation; resources; supervision. Hongrui Wang: Funding acquisition; project administration. ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (61473112), the Hebei Provincial Natural Science Fund Key Project (F2017201222). CONFLICT OF INTEREST The authors have declared no conflict of interest. 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