Local image fitting based active contour loss with deep learning for nuclei segmentation
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Từ khóa:
Deep Learning
Nuclei Segmentation
Image Segmentation
The Fully Convolutional Neural Network (FCN)
UNet
TransUnet
Tóm tắt
This paper proposes an approach for segmentation of nuclei images based on deep learning. In particular, the recent TransUnet inspired from transformers’ strong ability in modeling long-range context, is employed and adapted for the nuclei segmentation. For training the neural network, we propose a new loss inspired from active contour models with the guide of local image fitting. The loss when applied for the TransUnet has shown promising results over common Dice and Binary Cross Entropy loss functions. Our approach has been validated on the Data Science Bowl 2018 dataset, which includes 670 data folders for training model and 65 data folders for testing. State of the art models, such as FCN, SegNet, Unet, and DoubleU-Net are also conducted and evaluated. Quantitative assessments with high Dice similarity coefficient and Intersection over Union metrics demonstrate the performances of the proposed approach for nuclei segmentation.
Tài liệu tham khảo
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[1] C. Caicedo, J. Roth, A. Goodman, T. Becker, K. W. Karhohs, C. McQuin, et al., "Evaluation of deep learning strategies for nucleus segmentation in fluorescence images”, Cytometry A, vol. 95, pp. 952-965, 2019.
[2] Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, et al., "U-Net: Deep learning for cell counting detection and morphometry”, Nature Methods, vol. 16, pp. 67-70, 2019.
[3] Liu and F. Long, "Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning”, in in CNMC Challenge: Classification in Cancer Cell Imaging, 2019, pp. 113-121.
[4] Otsu, "A threshold selection method from gray-level histograms”, IEEE Trans Syst Man Cybern, vol. 9, pp. 62-66, 1979.
[5] Wählby, I.-M. Sintorn, F. Erlandsson, G. Borgefors, and E. Bengtsson, "Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections”, Journal of Microscopy, vol. 215, pp. 67-76, 2004.
[6] Rother, V. Kolmogorov, and A. Blake, "Grabcut: Interactive foreground extraction using iterated graph cuts”, ACM Transactions on Graphics (TOG), vol. 23, pp. 309-314, 2004.
[7] Hayakawa, V. B. Surya Prasath, H. Kawanaka, B. J. Aronow, and S. Tsuruoka, "Computational Nuclei Segmentation Methods in Digital Pathology: A Survey”, Archives of Computational Methods in Engineering, vol. 28, pp. 1-13, 2021.
[8] T.J., C. A. D. Durai, and J. D. Peter, "Retinal blood vessel segmentation from diabetic retinopathy images using tandem PCNN model and deep learning based SVM”, Optik- International Journal for Light and Electron Optics, vol. 199, p. 163328, 2019.
[9] Esteva, B. Kuprel, R. A. Novoato, J. Ko, S. M. Swetter, H. M. Blau, et al., "Dermatologist-level classification of skin cancer with deep neural networks”, Nature, vol. 542, pp. 115–118, 2017.
[10] -T. Pham, T.-T. Tran, P.-C. Wang, and M.-T. Lo, "Tympanic membrane segmentation in otoscopic images based on fully convolutional network with active contour loss”, Signal, Image and Video Processing, vol. 15, pp. 519–527, 2021.
[11] Van-Truong Pham, Thi-Thao Tran, Pa-Chun Wang, Po-Yu Chen, and Men-Tzung Lo, "EAR-UNet: A deep learning-based approach for segmentation of tympanic membranes from otoscopic images”, Artificial Intelligence In Medicine, 115, pp. 1-12, 2021.
[12] Sommer, C. Straehle, U. Köthe, and F. A. Hamprecht, "Ilastik: Interactive learning and segmentation toolkit”, In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011, pp. 230-233.
[13] Pan, L. Li, D. Yang, Y. He, Z. Liu, and H. Yang, "An Accurate Nuclei Segmentation Algorithm in Pathological Image Based on Deep Semantic Network”, IEEE Access, vol. 7, pp. 110674 - 110686, 2019.
[14] Zhou, O. F. Onder, Q. Dou, E. Tsougenis, H. Chen, and P. A. Heng, "CIA-net: Robust nuclei instance segmentation with contour-aware information aggregation”, in In: International Conference on Information Processing in Medical Imaging, 2019, pp. 682-693.
[15] O. Vuola, S. U. Akram, and J. Kannala, in IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019, pp. 208-212.
[16] A. Van Valen, T. Kudo, K. M. Lane, D. N. Macklin, N. T. Quach, M. M. DeFelice, et al., "Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments”, PLoS Comput. Biol., vol. 12, p. e1005177, 2016.
[17] Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation”, in International Conference on Medical image computing and computer-assisted intervention, 2015, pp. 234-241.
[18] Zeng, W. Xie, Y. Zhang, and Y. Lu, "RIC-Unet: An improved neural network based on Unet for nuclei segmentation in histology images”, IEEE Access, vol. 7, pp. 21420-21428, 2019.
[19] V. Tran, "A fully convolutional neural network for cardiac segmentation in short-axis MRI”, arXiv preprint arXiv:1604.00494, 2016.
[20] Badrinarayanan, A. Kendall, and R. Cipolla, "Segnet: A deep convolutional encoder-decoder architecture for image segmentation”, IEEE transactions on pattern analysis and machine intelligence, vol. 39, pp. 2481-2495, 2017.
[21] Jha, M. Riegler, D. Johansen, P. Halvorsen, and H. Johansen, "Doubleu-net: A deep convolutional neural network for medical image segmentation”, in IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 2020, pp. 558-564
[22] Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440, 2015.
[23] Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., "Attention is all you need”, in Advances in neural information processing systems, 2017, pp. 5998–6008.
[24] Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, et al., "An image is worth 16x16 words: Transformers for image recognition at scale”, arXiv:2010.11929, 2020.
[25] Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, et al., "TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation”, arXiv:2102.04306, 2021.
[26] Chan and L. Vese, "Active contours without edges”, IEEE Trans. Image Process., vol. 10 (2), pp. 266-277, 2001.
[27] Chen, B. M. Williams, S. R. Vallabhaneni, G. Czanner, R. Williams, and Y. Zheng, "Learning Active Contour Models for Medical Image Segmentation”, Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11623-11640, 2019.
[28] Chen, B. M. Williams, S. R. Vallabhaneni, G. Czanner, R. Williams, and Y. Zheng, "Learning active contour models for medical image segmentation”, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2019, pp. 11632-11640.
[29] -N. Trinh, N.-T. Nguyen, T.-T. Tran, and V.-T. Pham, "A Semi-supervised Deep Learning-Based Approach with Multiphase Active Contour Loss for Left Ventricle Segmentation from CMR Images”, in The third International Conference on Sustainable Computing, 2021, pp. 13-23.
[30] Zhang, H. Song, and L. Zhang, "Active contours driven by local image fitting energy " Pattern Recognition, vol. 43, pp. 1199-1206, 2010.
[31] Li, C. Kao, J. Gore, and Z. Ding, "Minimization of region-scalable fitting energy for image segmentation”, IEEE Trans. Image Process., vol. 17, pp. 1940-1949, 2008.
[32] J. C. Caicedo, A. Goodman, K. W. Karhohs, B. A. Cimini, J. Ackerman, M. Haghighi, et al., "Nucleus segmentation across imaging experiments: the 2018 data science bowl”, Nature Methods, vol. 16, pp. 1247–125, 2019
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Jun 30, 2022
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Thi-Thao Tran, Van-Truong Pham. “Local Image Fitting Based Active Contour Loss With Deep Learning for Nuclei Segmentation”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 20, số p.h 6.1, Tháng Sáu 2022, tr 19-23, doi:10.31130/ud-jst.2022.100E.
