Pneumothorax Segmentation Method Based on Improved U-net Network

doi:10.3778/j.issn.1002-8331.2008-0214

Abstract

Abstract: Pneumothorax is one of the common lung diseases. The existing X-ray pneumothorax detection methods mainly have two problems：first, pneumothorax is usually overlapped with the ribs, clavicle and other tissues, which is clinically missed. Second, the existing mainstream segmentation algorithms adopt single or double threshold strategies, resulting in inaccurate results. To solve the above problems, a novel pneumothorax segmentation method is proposed. The contrast limited adaptive histogram equalization is applied to chest radiographs to remove noise points and restore image details. Using the convolutional neural network layer with MBConvBlock as the encoder module to extract the abstract deep features in the image. Then the feature map is interpolated and reconstructed by the decoder to obtain the binary classification result of each pixel. The improved triple threshold strategy is adopted to better meet the results of actual medical scenarios. Compared with DeepLabV3+ and U-net, the Dice similarity coefficient value, accuracy rate and recall rate obtained by this method on the Siim-ACR Pneumothorax data set are 87.21%, 94.81% and 88.96%, respectively. The experimental results show that this method can make the segmentation of X-ray pneumothorax with high precision and fill the shortage of the present pneumothorax image segmentation.

Key words: pneumothorax segmentation, triple threshold strategy, MBConvBlock, SIIM-ACR Pneumothorax

摘要： 气胸是肺部常见疾病之一，目前已有的X线气胸检测方法主要存在两个问题：一是气胸通常与肋骨、锁骨等组织重叠，在临床上存在较大的漏诊情况；二是现有的主流分割算法采用单一或双重阈值策略，导致结果不准确。针对上述问题，提出了一种新颖的气胸分割方法。该方法对胸片进行对比度限制自适应直方图均衡化，去除噪点并还原图像细节；通过以MBConvBlock为编码器模块的卷积神经网络层提取图像中抽象的深层特征；通过解码器对提取到的特征映射进行插值重构得到每个像素的二分类结果；采取改进的三重阈值策略输出更满足实际医用场景的结果。该方法在SIIM-ACR Pneumothorax数据集上得到的Dice相似系数值、精确率和召回率分别为87.21%、94.81%和88.96%，相比DeepLabV3+和U-net等网络，在气胸分割取得了更好的性能。实验结果表明该方法能够使X线气胸分割具有较高的精度，填补了目前气胸X线图像分割领域的不足。

关键词: 气胸分割, 三重阈值策略, MBConvBlock, SIIM-ACR Pneumothorax

YU Sheng, WANG Kangjian, HE Lingmin, XU Zhijie, WANG Xiuhui. Pneumothorax Segmentation Method Based on Improved U-net Network[J]. Computer Engineering and Applications, 2022, 58(3): 207-214.

余昇, 王康健, 何灵敏, 胥智杰, 王修晖. 基于改进U-net网络的气胸分割方法[J]. 计算机工程与应用, 2022, 58(3): 207-214.

References

[1] 王朝，邹卫.原发性自发性气胸病因研究进展[J].临床肺科杂志，2015，20（6）：1120-1122.
WANG C，ZOU W.Research progress on the etiology of primary spontaneous pneumothorax[J].Journal of Clinical Pulmonary Medicine，2015，20（6）：1120-1122.
[2] BINTCLIFFE O，MASKELL N.Spontaneous pneumothorax[J].British Medical Journal，2014，348：2928．
[3] SUTHAR M，MAHJOUBFAR A，SEALS K，et al.Diagnostic tool for pneumothorax[C]//Proceedings of the 2016 IEEE Photonics Society Summer Topical Meeting Series.Piscataway：IEEE，2016：218-219．
[4] 穆魁津.谈谈气胸的诊断方法[J].中国实用内科杂志，1995（4）：205-206.
MU K J.Talk about the diagnosis method of pneumothorax[J].Chinese Journal of Practical Internal Medicine，1995（4）：205-206.
[5] GEVA O，ZIMMERMAN-MORENO G，LIEBERMAN S，et al.Pneumothorax detection in chest radiographs using local and global texture signatures[C]//Proceedings of the Medical Imaging 2015：Computer-Aided Diagnosis，SPIE 9414.Bellingham：SPIE，2015．
[6] SANADA S，DOI K，MACMAHON H.Image feature analysis and computer-aided diagnosis in digital radiography：automated detection of pneumothorax in chest images[J].Medical Physics，1992，19（5）.
[7] CHAN Y H，ZENG Y Z，WU H C，et al.Effective pneumothorax detection for chest X-ray images using local binary pattern and support vector machine[J].Journal of Healthcare Engineering，2018：2908517．
[8] 何雪英，韩忠义，魏本征.基于深度卷积神经网络的色素性皮肤病识别分类[J].计算机应用，2018，38（11）：3236-3240.
HE X Y，HAN Z Y，WEI B Z.Pigmented skin lesion recognition and classification based on deep convolutional neural network[J].Journal of Computer Applications，2018，38（11）：3236-3240.
[9] WANG X，PENG Y，LU L，et al.Chest X-ray8：hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：3462-3471.
[10] LONG J，SHELHAMER E，DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2015：3431-3440.
[11] RONNEBERGER O，FISCHER P，BROX T.U-net：convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Cham：Springer，2015：234-241.
[12] OKTAY O，SCHLEMPER J，FOLGOC L L，et al.Attention U-Net：learning where to look for the pancreas[J].arXiv：1804.03999，2018.
[13] XIAO X，LIAN S，LUO Z，et al.Weighted Res-UNet for high-quality retina vessel segmentation[C]//2018 9th International Conference on Information Technology in Medicine and Education（ITME），2018：327-331.
[14] CHEN L C，ZHU Y K，PAPANDREOU G，et al.Encoder decoder with atrous separable convolution for semantic image segmentation[C]//The European Conference on Computer Vision，2018：801-818.
[15] TAN M，LE Q V.EfficientNet：rethinking model scaling for convolutional neural networks[C]//International Conference on Machine Learning，2019：6105-6114.
[16] RAMACHANDRAN P，ZOPH B，LE Q V，et al.Searching for activation functions[C]//International Conference on Learning Representations，2018.
[17] ROY A G，NAVAB N，WACHINGER C，et al.Concurrent spatial and channel squeeze & excitation in fully convolutional networks[C]//Medical Image Computing and Computer Assisted Intervention，2018：421-429.
[18] LIN T Y，GOYAL P，GIRSHICK R，et al.Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：2980-2988.
[19] 李炼，李维嘉，吴耀中.基于红色暗通道先验理论与CLAHE算法的水下图像增强算法[J].中国舰船研究，2019，14（S1）：175-182.
LI L，LI W J，WU Y Z.An underwater image enhancement algorithm based on RDCP and CLAHE[J].Chinese Journal of Ship Research，2019，14（S1）：175-182.
[20] DENG J，DONG W，SOCHER R，et al.Imagenet：a large-scale hierarchical image database[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition，2009：248-255.