Discriminative features pyramid network for medical image segmentation

• Autorzy: Xie, Xiwang , Xie, Lijie , Li, Guanyu , Guo, Hao , Zhang, Weidong , Shao, Feng , Zhao, Wenyi , Tong, Ling , Pan, Xipeng , An, Jubai
• Języki publikacji: EN

Abstrakty

EN

The diverse shapes and scales, complicated backgrounds, blurred boundaries, and similar appearances challenge the current organ segmentation methods in medical scene images. It is difficult to acquire satisfactory performance to directly extend the object segmentation methods in the natural scene images to the medical scene images. In this paper, we propose a discriminant feature pyramid (DFPNet) network for organ segmentation in the original medical images, which consists of two sub-networks: the feature steered network and the border network. To be specific, the feature steered network takes a top-down step-wise manner to extract abundant context information, which is conducive to suppressing the cluttered background and perceiving the scale variation of objects. The border network utilizes a bottom-up step-wise manner to optimize the boundary feature map, which aims at distinguishing adjacent edge features with similar appearances but diverse labels. A series of experiments were conducted on three publicly available medical datasets ( i.e., LUNA 16, RIM-ONE-R1, and VNC datasets) to evaluate the validity and generalization of the proposed DFPNet. Experimental results indicate that our network achieves superior performance in terms of the receiver operating characteristic (ROC) curve, F-Score, Jaccard index, and Hausdorff distance. The code will be available at: https://github.com/Xie-Xiwang/DFPNet.

Słowa kluczowe

PL

obraz medyczny; cechy dyskryminacyjne; segmentacja narządu

EN

medical image; discriminative features; pyramid network; organ segmentation

• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2024
• Tom: Vol. 44, no. 2
• Strony: 327--340
• Opis fizyczny: Bibliogr. 50 poz., rys., tab., wykr.

Twórcy

autor

Xie, Xiwang

• School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China

autor

Xie, Lijie

• Engineering Department, Huanghe University of Science and Technology, Zhengzhou 450015, China

autor

Li, Guanyu

• School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China

autor

Guo, Hao

• School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China

autor

Zhang, Weidong

• School of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China

autor

Shao, Feng

• School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China

autor

Zhao, Wenyi

• School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China

autor

Tong, Ling

• School of Health Sciences, University of Wisconsin Milwaukee, WI, 53211, USA

autor

Pan, Xipeng

• School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China,
• Department of Radiology, Guangdong Provincial People’s Hospital, Guangzhou, 510080, China

autor

An, Jubai

• School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China,

Bibliografia

• [1] Asgari TS, Abhishek K, Cohen JP, Cohen-Adad J, Hamarneh G. Deep semantic segmentation of natural and medical images: A review. Artif Intell Rev 2021;54:137-78.
• [2] Nie R, Cao J, Zhou D, Dian W. Multi-source information exchange encoding with PCNN for medical image fusion. IEEE Trans Circuits Syst Video Technol 2020;31(3):986-1000.
• [3] Hu B, Zhou S, Xiong Z, Wu F. Cross-resolution distillation for efficient 3D medical image registration. IEEE Trans Circuits Syst Video Technol 2022;32(10):7269-83.
• [4] Wang S, Dong Z, Zhang Z, Huo Y, Celebi Y, Shan C. Guest editorial emerging challenges for deep learning. IEEE J Biomed Health Inf 2022;26(11):5287-8.
• [5] Zhao S, Wang P, Heidari AA, Chen H, Turabieh H, Mafarja M, et al. Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi’s entropy for chronic obstructive pulmonary disease. Comput Biol Med 2021;134:104427.
• [6] Liu X, Zhang G, Jia L, Wang X, Zhao Z. An iterative threshold algorithm of log-sum regularization for sparse problem. 2023.
• [7] Li J, Han Y, Gao Y, Li Q, Wang S. An enhance relative total variation with BF model for edge-preserving image smoothing. IEEE Trans Circuits Syst Video Technol 2023.
• [8] Ge P, Chen Y, Wang G, Weng G. A hybrid active contour model based on pre-fitting energy and adaptive functions for fast image segmentation. Pattern Recognit Lett 2022;158:71-9.
• [9] Xie X, Zhang W, Pan X, Shao F, Zhao W, An J. Canet: Context aware network with dual-stream pyramid for medical image segmentation. Biomed Signal Process Control 2023;81:104437.
• [10] Liang C, Wang W, Miao J, Yang Y. Gmmseg: Gaussian mixture based generative semantic segmentation models. Adv Neural Inf Process Syst 2022;35:31360-75.
• [11] Ge X, Qu Y, Shang C, Yang L, Shen Q. A self-adaptive discriminative autoencoder for medical applications. IEEE Trans Circuits Syst Video Technol 2022;32(12):8875-86.
• [12] Han C, Lin J, Mai J, Wang Y, Zhang Q, Zhao B, et al. Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels. Med Image Anal 2022;80:102487.
• [13] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015, p. 3431-40.
• [14] Badrinarayanan V, Kendall A, Cipolla R. Segnet: A deep convolutional encoder- decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 2017;39(12):2481-95.
• [15] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention-MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. Springer International Publishing; 2015, p. 234-41.
• [16] Alom MZ, Yakopcic C, Hasan M, Taha TM, Asari VK. Recurrent residual U-Net for medical image segmentation. J Med Imaging 2019;6(1):014006-014006.
• [17] Qin X, Zhang Z, Huang C, Gao C, Dehghan M, Jagers M. Basnet: Boundary-aware salient object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019, p. 7479-89.
• [18] Tang P, Yang P, Nie D, Wu X, Zhou J, Wang Y. Unified medical image segmentation by learning from uncertainty in an end-to-end manner. Knowl-Based Syst 2022;241:108215.
• [19] Lin A, Chen B, Xu J, Zhang Z, Lu G, Zhang D. Ds-transunet: Dual swin transformer u-net for medical image segmentation. IEEE Trans Instrum Meas 2022;71:1-15.
• [20] Yang S, Zhang X, Chen Y, Jiang Y, Feng Q, Pu L, et al. UcUNet: A lightweight and precise medical image segmentation network based on efficient large kernel U-shaped convolutional module design. Knowl-Based Syst 2023;278:110868.
• [21] Yu C, Wang J, Peng C, Gao C, Yu G, Sang N. Learning a discriminative feature network for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, p. 1857-66.
• [22] Zhao H, Shi J, Qi X, Wang X, Jia J. Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, p. 2881-90.
• [23] Feng S, Zhao H, Shi F, et al. CPFNet: Context pyramid fusion network for medical image segmentation. IEEE Trans Med Imaging 2020;39(10):3008-18.
• [24] Wang W, Zhou T, Yu F, Dai J, Konukoglu E. Exploring cross-image pixel contrast for semantic segmentation. In: Proceedings of the IEEE/CVF international conference on computer vision. 2021, p. 7303-13.
• [25] Lv J, Hu Y, Fu Q, Hu Y, Lv L, Yang G, et al. Local feature matters: Cascade multi-scale MLP for edge segmentation of medical images. IEEE Trans NanoBiosci 2023.
• [26] Zhou T, Wang W, Konukoglu E, Van Gool L. Rethinking semantic segmentation: A prototype view. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022, p. 2582-93.
• [27] Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, et al. Ce-net: Context encoder network for 2d medical image segmentation. IEEE Trans Med Imaging 2019;38(10):2281-92.
• [28] Tran M, Vo-Ho VK, Le NTH. 3DConvCaps: 3Dunet with convolutional capsule encoder for medical image segmentation. In: 2022 26th international conference on pattern recognition. IEEE; 2022, p. 4392-8.
• [29] Gu R, Wang G, Lu J, Zhang J, Lei W, Chen Y, et al. CDDSA: Contrastive domain disentanglement and style augmentation for generalizable medical image segmentation. Med Image Anal 2023;89:102904.
• [30] Qin X, Zhang Z, Huang C, Dehghan M, Zaiane OR, Jagers M. U2-Net: Going deeper with nested U-structure for salient object detection. Pattern Recognit 2020;106:107404.
• [31] Krähenbühl P, Koltun V. Efficient inference in fully connected crfs with Gaussian edge potentials. In: Advances in neural information processing systems, 2011, p. 24.
• [32] Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 2017;40(4):834-48.
• [33] Zheng S, Jayasumana S, Romera-Paredes B, Vineet V, Su Z, Du D, et al. Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE international conference on computer vision. 2015, p. 1529-37.
• [34] Chen S, Gamechi ZS, Dubost F, Tulder G, Bruijne M. An end-to-end approach to segmentation in medical images with CNN and posterior-CRF. Med Image Anal 2022;76:102311.
• [35] Qiu C, Li K, Zhou X, He S, Li B. A novel method for signal labeling and precise location in a variable parameter milling process based on the stacked-BiLSTM-CRF and FLOSS. Adv Eng Inform 2023;55:101850.
• [36] Zhou L, Kong X, Gong C, Zhang F, Zhang X. FC-RCCN: Fully convolutional residual continuous CRF network for semantic segmentation. Pattern Recognit Lett 2020;130:54-63.
• [37] Zhao H, Shi J, Qi X, Wang X, Jia J. Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, p. 2881-90.
• [38] Zhou Y, Onder OF, Dou Q, Tsougenis E, Chen H, Heng PA. Cia-net: Robust nuclei instance segmentation with contour-aware information aggregation. In: Information processing in medical imaging: 26th international conference, IPMI 2019, Hong Kong, China, June 2-7, 2019, proceedings 26. Springer International Publishing; 2019, p. 682-93.
• [39] Nguyen DK, Ju J, Booij O, Oswald MR, Snoek CG. Boxer: Box-attention for 2d and 3d transformers. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022, p. 4773-82.
• [40] Zhang T, Wei S, Ji S. E2ec: An end-to-end contour-based method for high-quality high-speed instance segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022, p. 4443-52.
• [41] Salehi SSM, Erdogmus D, Gholipour A. Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: International workshop on machine learning in medical imaging. Cham: Springer International Publishing; 2017, p. 379-87.
• [42] Xie X, Pan X, Shao F, Zhang W, An J. Mci-net: Multi-scale context integrated network for liver ct image segmentation. Comput Electr Eng 2022;101:108085.
• [43] Xi YJ, Li KW, Xu YH, Zhu JB. A SPD-UNet model for seismic fault image identification. Comput Eng 2021;47:249-55.
• [44] Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P. SegFormer: Simple and efficient design for semantic segmentation with transformers. Adv Neural Inf Process Syst 2021;34:12077-90.
• [45] Li G, Liu Z, Zeng D, Lin W, Ling H. Adjacent context coordination network for salient object detection in optical remote sensing images. IEEE Trans Cybern 2022;53(1):526-38.
• [46] Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, et al. Unet 3+: A fullscale connected unet for medical image segmentation. In: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing. IEEE; 2020, p. 1055-9.
• [47] Ruan J, Xiang S, Xie M, Liu T, Fu Y. MALUNet: A multi-attention and lightweight unet for skin lesion segmentation. In: 2022 IEEE international conference on bioinformatics and biomedicine. IEEE; 2022, p. 1150-6.
• [48] Valanarasu JMJ, Sindagi VA, Hacihaliloglu I, Patel VM. Kiu-net: Overcomplete convolutional architectures for biomedical image and volumetric segmentation. IEEE Trans Med Imaging 2021;41(4):965-76.
• [49] Paluru N, Dayal A, Jenssen HB, Sakinis T, Cenkeramaddi LR, Prakash J, et al. Anam-Net: Anamorphic depth embedding-based lightweight CNN for segmentation of anomalies in COVID-19 chest CT images. IEEE Trans Neural Netw Learn Syst 2021;32(3):932-46.
• [50] Valanarasu JMJ, Oza P, Hacihaliloglu I, Patel VM. Medical transformer: Gated axial-attention for medical image segmentation. In: Medical image computing and computer assisted intervention-MICCAI 2021: 24th international conference, Strasbourg, France, September 27-October 1 2021, proceedings, Part I 24. Springer International Publishing; 2021, p. 36-46.

Uwagi

EN

This work was supported in part by the National Natural Science Foundation of China under Grants (Grant Nos. 61471079, 62002082), the Postdoctoral Science Foundation of China under Grant (Grant No. 2021M690753), Guangxi Natural Science Foundation, China (Grant No. 2020GXNSFBA238014), Middle-aged and Young Teachers’ Basic Ability Promotion Project of Guangxi, China (Grant No. 2020KY05034).

Identyfikatory

DOI

10.1016/j.bbe.2024.04.001