Abstract
Medical image segmentation based on deep learning technics has been more and more prevalent in recent years. The primary reasons lead to success of those methods are radical technics in improving deep learning model or global loss functions such as Cross Entropy (CE), Dice loss, … However, it is still a challenging problem because medical images are normally affected by complex noises, intensity inhomogeneity, or occlusion. To effectively solve those issues, utilizing local image information for segmentation is demonstrated as the potential way. Therefore, in this paper, we propose a new region-based loss functional based on level set method and extent to the case of multiphase segmentation. It allows our deep learning model to train end-to-end and also segment multiclass simultaneously with high accuracy instead of binary segmentation. Our proposed method is assessed on 2017 ACDC dataset and 2018 ISIC Challenge dataset. Experiments indicate that the proposed method outperforms the state-of-the-art methods in terms of Dice coefficient and Jaccard indexes. This highlights the efficiency of our approach in multiclass segmentation for medical images.
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References
D.-P. Fan et al., Pranet: parallel reverse attention network for polyp segmentation, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2020), pp. 263–273
A. Sinha, J. Dolz, Multi-scale self-guided attention for medical image segmentation. IEEE J. Biomed. Health Inform. (2020)
Z. Zhou, M.M.R. Siddiquee, N. Tajbakhsh, J. Liang, Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2019)
T. Zhou, S. Ruan, S. Canu, A review: deep learning for medical image segmentation using multi-modality fusion. Array 3, 100004 (2019)
V.T. Pham, T.T. Tran, P.C. Wang, M.T. Lo, Tympanic membrane segmentation in otoscopic images based on fully convolutional network with active contour loss. Signal Image Video Process. (2020). https://doi.org/10.1007/s11760-020-01772-7
O. Ronneberger, P. Fischer, T. Brox, U-net: convolutional networks for biomedical image segmentation, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 234–241
S. Jadon, A survey of loss functions for semantic segmentation (2020). arXiv preprint arXiv:2006.14822
K. Kawaguchi, L. Kaelbling, Elimination of all bad local minima in deep learning, in International Conference on Artificial Intelligence and Statistics. PMLR (2020), pp. 853–863
F. Schutyser, G. Swennen, P. Suetens, Robust visualization of the dental occlusion by a double scan procedure, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2005), pp. 368–374
C. Li, R. Huang, Z. Ding, J.C. Gatenby, D.N. Metaxas, J.C. Gore, A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans. Image Process. 20(7), 2007–2016 (2011)
T.F. Chan, L.A. Vese, Image segmentation using level sets and the piecewise-constant Mumford-Shah model, in Technical Report 0014. Computational Applied Math Group (Citeseer, 2000)
T.T. Tran, V.T. Pham, K.K. Shyu, Zernike moment and local distribution fitting fuzzy energy-based active contours for image segmentation. SIViP 8(1), 11–25 (2014)
V.T. Pham, T.T. Tran, K.K. Shyu, C. Lin, P.C. Wang, M.T. Lo, Shape collaborative representation with fuzzy energy based active contour model. Eng. Appl. Artif. Intell. 56, 60–74 (2016)
T.F. Chan, L.A. Vese, Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
L.A. Vese, T.F. Chan, A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Comput. Vis. 50(3), 271–293 (2002)
A. Vasilevskiy, K. Siddiqi, Flux maximizing geometric flows. IEEE Trans. Pattern Anal. Mach. Intell. 24(12), 1565–1578 (2002)
R. Malladi, J.A. Sethian, B.C. Vemuri, Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17(2), 158–175 (1995)
B. Kim, J.C. Ye, Mumford–Shah loss functional for image segmentation with deep learning. IEEE Trans. Image Process. 29, 1856–1866 (2019)
V.-T. Pham, T.-T. Tran, Active contour model and nonlinear shape priors with application to left ventricle segmentation in cardiac MR images. Optik 127(3), 991–1002 (2016)
M. Tan, Q.V. Le, Efficientnet: rethinking model scaling for convolutional neural networks (2019). arXiv preprint arXiv:1905.11946
B. Xu, N. Wang, T. Chen, M. Li, Empirical evaluation of rectified activations in convolutional network (2015). arXiv preprint arXiv:1505.00853
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778
J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 7132–7141
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A.L. Yuille, Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)
A. Tato, R. Nkambou, Improving Adam optimizer (2018)
N.C. Codella et al., Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC), in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (IEEE, 2018), pp. 168–172
P. Tschandl, C. Rosendahl, H. Kittler, The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018)
M.Z. Alom, M. Hasan, C. Yakopcic, T.M. Taha, V.K. Asari, Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation (2018). arXiv preprint arXiv:1802.06955
C.H. Sudre, W. Li, T. Vercauteren, S. Ourselin, M.J. Cardoso, Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations, in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer, 2017), pp. 240–248
M. Ren, W. Zeng, B. Yang, R. Urtasun, Learning to reweight examples for robust deep learning (2018). arXiv preprint arXiv:1803.09050
S.S. Shekhawat, H. Sharma, S. Kumar, A. Nayyar, B. Qureshi, bSSA: Binary Salp Swarm Algorithm with hybrid data transformation for feature selection. IEEE Access 9, 14867–14882 (2021). https://doi.org/10.1109/ACCESS.2021.3049547
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This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.05-2018.302.
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Trinh, MN., Nguyen, NT., Tran, TT., Pham, VT. (2022). A Deep Learning-Based Approach with Image-Driven Active Contour Loss for Medical Image Segmentation. In: Saraswat, M., Roy, S., Chowdhury, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications . Lecture Notes in Networks and Systems, vol 288. Springer, Singapore. https://doi.org/10.1007/978-981-16-5120-5_1
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