Abstract
Anatomical maps depicted by medical images reflect the health of the human body. Understanding the detailed division of the regions in the anatomical maps is helpful for diagnosis and further treatment. For example, the lung window contains the lung parenchyma, mediastinum, and diseased tumor. Accurate tumor segmentation can be more comprehensive and accurate to determine the disease condition to select the appropriate radiotherapy means to improve the success rate of treatment. However, the traditional manual segmentation task is large, and the segmentation results vary with the experience level of the observer. Therefore, the development of automatic and reliable segmentation methods is a feasible and important task in clinical practice. Therefore, this paper proposes a multichannel convolutional neural network (CNN)-based fuzzy active contour model for medical image segmentation. Medical images are gridded and labeled using the superpixel SLIC method. The gridded images are used as data sets to train the multichannel CNN network to segment superpixels of organ boundaries, and the seed points of these superpixels are connected to form rough segmentation boundaries. The rough segmentation boundary is used as the initial contour, and the organ boundary in the medical image is obtained by fuzzy active contour segmentation. Finally, we conduct comparison experiments with the state-of-the-art segmentation methods, the results of the subjective evaluation and objective evaluation show that the proposed method has a better segmentation effect and robustness.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bai XD, Cao ZG, Wang Y et al (2014) Image segmentation using modified SLIC and Nyström based spectral clustering. Optik 125(16):4302–4307
Bi L, Feng D, Kim J (2018) Dual-path adversarial learning for fully convolutional network (FCN)-based medical image segmentation. Vis Comput 34(6–8):1–10
Brion E, Jean L, Javaid U et al (2019) Using planning CTs to enhance CNN-based bladder segmentation on cone beam CT. In: Image-guided procedures, robotic interventions, and modeling, vol 10951. https://doi.org/10.1117/12.2512791
Chen X, Udupa JK, Bagci U, Zhuge Y, Yao J (2012) Medical image segmentation by combining graph cuts and oriented active appearance models. IEEE Trans Image Process 21(4):2035–2046. https://doi.org/10.1109/TIP.2012.2186306
Chu J, Min H, Liu L et al (2015) A novel computer aided breast mass detection scheme based on morphological enhancement and SLIC superpixel segmentation. Med Phys 42(7):3859–3869
Drozdzal M, Chartrand G, Vorontsov E et al (2017) Learning normalized inputs for iterative estimation in medical image segmentation. Med Image Anal 44:1–13
Efimenko M, Ignatev A, Koshechkin K (2020) Review of medical image recognition technologies to detect melanomas using neural networks. BMC Bioinform 21(Suppl 11):270
Faisal R, Syed IAS, Naveed Riaz M et al (2020) A region-based deep level set formulation for vertebral bone segmentation of osteoporotic fractures. J Dig Imaging 33(1):191–203
Gu X, Angelov P (2019) Self-boosting first-order autonomous learning neuro-fuzzy systems. Appl Soft Comput 77:118–134. https://doi.org/10.1016/j.asoc.2019.01.005
Hedrea E, Precup R, Roman R et al (2021) Tensor product-based model transformation approach to tower crane systems modeling. Asian J Control. https://doi.org/10.1002/asjc.2494
Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5(2):143–156. https://doi.org/10.1016/S1361-8415(01)00036-6
Kang J, Ding J, Wan Y et al (2020) Liver image segmentation algorithm based on watershed correction and U-Net. Comput Eng (in Chinese) 46(1):255–261
Kayalibay B, Jensen G, van der Smagt P (2017) CNN-based segmentation of medical imaging data. arXiv:1701.03056
Pozna C, Precup RE (2014) Applications of signatures to expert systems modelling. Acta Polytech Hung 11(2):21–39
Rcr A, Rep A, Bd A et al (2019) Combined model-free adaptive control with fuzzy component by virtual reference feedback tuning for tower crane systems. Proc Comput Sci 162:267–274. https://doi.org/10.1016/j.procs.2019.11.284
Roth HR, Oda H, Zhou X et al (2018) An application of cascaded 3D fully convolutional networks for medical image segmentation. Comput Med Imaging Graph 66:90–99
Sun W-Y, Dong E-Q, Cao Z-L, Zhang Q (2017) A robust local segmentation method based on fuzzy-energy based active contour. Acta Autom Sin 43(4):611–621. https://doi.org/10.16383/j.aas.2017.c160260
Teng L, Li H, Yin S et al (2020) An active contour model based on hybrid energy and fisher criterion for image segmentation. Int J Image Data Fusion 11(1):97–112
Toennies K, Rak M, Engel K (2014) Deformable part models for object detection in medical images. Biomed Eng Online 13(Suppl 1):S1
Tseng KK, Zhang R, Chen CM et al (2020) DNetUnet: a semi-supervised CNN of medical image segmentation for super-computing AI service. J Supercomput. https://doi.org/10.1007/s11227-020-03407-7
Tustison NJ, Yang Y, Salerno M (2015) Advanced normalization tools for cardiac motion correction. In: Camara O, Mansi T, Pop M, Rhode K, Sermesant M, Young A (eds) Statistical atlases and computational models of the heart—imaging and modelling challenges. STACOM 2014. Lecture notes in computer science, vol 8896. Springer, Cham. https://doi.org/10.1007/978-3-319-14678-2_1
Wang X, Yin S, Sun Ke et al (2020) GKFC-CNN: modified Gaussian kernel fuzzy C-means and convolutional neural network for apple segmentation and recognition. J Appl Sci Eng 23(3):555–561
Xue P, Dong E, Ji H (2020) Lung 4D CT image registration based on high-order markov random field. IEEE Trans Med Imaging 39(4):910–921. https://doi.org/10.1109/TMI.2019.2937458
Yin S, Li H, Liu D, Karim S (2020a) Active contour modal based on density-oriented BIRCH clustering method for medical image segmentation. Multimed Tools Appl 79:31049–31068
Yin S, Li H, Teng L et al (2020b) An optimised multi-scale fusion method for airport detection in large-scale optical remote sensing images. Int J Image Data Fusion 11(2):201–214
Yongpeng TAO, Yu JING, Cong XU (2020) CT image segmentation method combining superpixel and CNN. Comput Eng Appl (in Chinese) 56(5):200–205
Yu Q, Gao Y, Zheng Y et al (2020) Crossover-Net: leveraging vertical-horizontal crossover relation for robust medical image segmentation. Pattern Recognit 113(10):107756
Zall R, Kangavari MR (2019) On the construction of multi-relational classifier based on canonical correlation analysis. Int J Artif Intell 17(2):23–43
Zhang L et al (2020) Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation. IEEE Trans Med Imaging 39(7):2531–2540. https://doi.org/10.1109/TMI.2020.2973595
Zhang Z, Wu C, Coleman S et al (2020) DENSE-INception U-net for medical image segmentation. Comput Methods Progr Biomed 192:105395
Zhou T, Ruan S, Canu S (2020) A review: deep learning for medical image segmentation using multi-modality fusion. arXiv:2004.10664
Acknowledgements
This work is supported by Basic Research Projects Under Basic Scientific Research Operating Expenses (No. 2019-KYYWF-1387). Also, The authors are grateful to Education Department of Liaoning Province for funding this work through Liaoning Province Future School Innovation Plan (Project Number 2020CFS073).
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Shi, Q., Yin, S., Wang, K. et al. Multichannel convolutional neural network-based fuzzy active contour model for medical image segmentation. Evolving Systems 13, 535–549 (2022). https://doi.org/10.1007/s12530-021-09392-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12530-021-09392-3