Visual Geometry Group based on U-Shaped Model for Liver/Liver Tumor Segmentation
Keywords:
Convolutional Neural Network, Segmentation, Datasets, LiverAbstract
Liver cancer is the primary reason of death around the globe. Manually detecting the infected tissues is a challenging and time-consuming task. The computerized methods help make accurate decisions and therapy processes. The segmentation accuracy might be increased to reduce the loss rate. Semantic segmentation performs a vital role in infected liver region segmentation. This article proposes a method that consists of two major steps; first, the local Laplacian filter is applied to improve the image quality. The second is the proposed semantic segmentation model in which features are extracted to the pre-trained VGG16 model and passed to the U-shaped network. This model consists of 51 layers: input, 23 convolutional, 4 max pooling, 4 transpose convolutional, 4 concatenated, 8 activation, and 7 batch-normalization. The proposed segmentation framework is trained on the selected hyperparameters that reduce the loss rate and increase the segmentation accuracy. The proposed approach more precisely segments the infected liver region. The proposed approach performance is accessed on two datasets such as 3DIRCADB and LiTS17. The proposed framework provides an average dice score of 0.98, which is far better compared to the existing works.
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References
Understanding Liver Cancer, https://www.webmd.com/cancer/understanding-liver-cancer-basic-information, Accessed by 15/8/2022.
J. Amin, M. A. Anjum, M. Sharif, S. Kadry, A. Nadeem, and S. F. Ahmad, "Liver Tumor Localization Based on YOLOv3 and 3D-Semantic Segmentation Using Deep Neural Networks," Diagnostics, vol. 12, p. 823, 2022.
C. Mattiuzzi and G. Lippi, "Cancer statistics: a comparison between world health organization (WHO) and global burden of disease (GBD)," European journal of public health, vol. 30, pp. 1026-1027, 2020.
A. Krishan and D. Mittal, "Effective segmentation and classification of tumor on liver MRI and CT images using multi-kernel K-means clustering," Biomedical Engineering/Biomedizinische Technik, vol. 65, pp. 301-313, 2020.
C. Zhang, J. Lu, Q. Hua, C. Li, and P. Wang, "SAA-Net: U-shaped network with Scale-Axis-Attention for liver tumor segmentation," Biomedical Signal Processing and Control, vol. 73, p. 103460, 2022.
A. Hänsch, G. Chlebus, H. Meine, F. Thielke, F. Kock, T. Paulus, et al., "Improving automatic liver tumor segmentation in late-phase MRI using multi-model training and 3D convolutional neural networks," Scientific Reports, vol. 12, pp. 1-10, 2022.
S. Di, Y. Zhao, M. Liao, Z. Yang, and Y. Zeng, "Automatic liver tumor segmentation from CT images using hierarchical iterative superpixels and local statistical features," Expert Systems with Applications, vol. 203, p. 117347, 2022.
D. S. Uplaonkar and N. Patil, "Modified Otsu thresholding based level set and local directional ternary pattern technique for liver tumor segmentation," International Journal of System Assurance Engineering and Management, pp. 1-11, 2022.
H. Rahman, T. F. N. Bukht, A. Imran, J. Tariq, S. Tu, and A. Alzahrani, "A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet," Bioengineering, vol. 9, p. 368, 2022.
R. Zheng, Q. Wang, S. Lv, C. Li, C. Wang, W. Chen, et al., "Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM," IEEE Transactions on Medical Imaging, 2022.
J. Zhang, S. Luo, Y. Qiang, Y. Tian, X. Xiao, K. Li, et al., "Edge Constraint and Location Mapping for Liver Tumor Segmentation from Nonenhanced Images," Computational and Mathematical Methods in Medicine, vol. 2022, 2022.
S. Gul, M. S. Khan, A. Bibi, A. Khandakar, M. A. Ayari, and M. E. Chowdhury, "Deep learning techniques for liver and liver tumor segmentation: A review," Computers in Biology and Medicine, p. 105620, 2022.
Y. Wu, H. Shen, Y. Tan, and Y. Shi, "Automatic liver tumor segmentation used the cascade multi-scale attention architecture method based on 3D U-Net," International Journal of Computer Assisted Radiology and Surgery, pp. 1-8, 2022.
J. Amin, M. Sharif, G. A. Mallah, and S. Fernandes, "An Optimized Features Selection Approach based on Manta Ray Foraging Optimization (MRFO) Method for Parasite Malaria Classification," Frontiers in Public Health, p. 2846.
J. Amin, M. Sharif, M. A. Anjum, A. Siddiqa, S. Kadry, Y. Nam, et al., "3d semantic deep learning networks for leukemia detection," 2021.
J. Amin, M. Sharif, M. A. Anjum, Y. Nam, S. Kadry, and D. Taniar, "Diagnosis of COVID-19 infection using three-dimensional semantic segmentation and classification of computed tomography images," Computers, Materials and Continua, vol. 68, pp. 2451-2467, 2021.
Y. Zhang, J. Yang, Y. Liu, J. Tian, S. Wang, C. Zhong, et al., "Decoupled pyramid correlation network for liver tumor segmentation from CT images," Medical Physics, 2022.
J. Amin, M. Sharif, and M. Almas Anjum, "Skin Lesion Detection Using Recent Machine Learning Approaches," in Prognostic Models in Healthcare: AI and Statistical Approaches, ed: Springer, 2022, pp. 193-211.
U. Yunus, J. Amin, M. Sharif, M. Yasmin, S. Kadry, and S. Krishnamoorthy, "Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network," Life, vol. 12, p. 1126, 2022.
D. T. Kushnure and S. N. Talbar, "HFRU-Net: High-level feature fusion and recalibration unet for automatic liver and tumor segmentation in CT images," Computer Methods and Programs in Biomedicine, vol. 213, p. 106501, 2022.
H. Seo, C. Huang, M. Bassenne, R. Xiao, and L. Xing, "Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images," IEEE transactions on medical imaging, vol. 39, pp. 1316-1325, 2019.
C. Sun, S. Guo, H. Zhang, J. Li, M. Chen, S. Ma, et al., "Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs," Artificial intelligence in medicine, vol. 83, pp. 58-66, 2017.
X. Li, H. Chen, X. Qi, Q. Dou, C.-W. Fu, and P.-A. Heng, "H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes," IEEE transactions on medical imaging, vol. 37, pp. 2663-2674, 2018.
K. Xia, H. Yin, P. Qian, Y. Jiang, and S. Wang, "Liver semantic segmentation algorithm based on improved deep adversarial networks in combination of weighted loss function on abdominal CT images," IEEE Access, vol. 7, pp. 96349-96358, 2019.
J. Chi, X. Han, C. Wu, H. Wang, and P. Ji, "X-Net: Multi-branch UNet-like network for liver and tumor segmentation from 3D abdominal CT scans," Neurocomputing, vol. 459, pp. 81-96, 2021.
Y. Tang, Y. Tang, Y. Zhu, J. Xiao, and R. M. Summers, "E $$^ $$ Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans," in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020, pp. 512-522.
W. Li, "Automatic segmentation of liver tumor in CT images with deep convolutional neural networks," Journal of Computer and Communications, vol. 3, p. 146, 2015.
J. Zhang, Y. Xie, P. Zhang, H. Chen, Y. Xia, and C. Shen, "Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation," in IJCAI, 2019, pp. 4271-4277.
H. Jiang, T. Shi, Z. Bai, and L. Huang, "Ahcnet: An application of attention mechanism and hybrid connection for liver tumor segmentation in ct volumes," Ieee Access, vol. 7, pp. 24898-24909, 2019.
J. Stawiaski, E. Decenciere, and F. Bidault, "Interactive liver tumor segmentation using graph-cuts and watershed," in Workshop on 3D segmentation in the clinic: a grand challenge II. Liver tumor segmentation challenge. MICCAI, New York, USA, 2008.
G. Chlebus, H. Meine, J. H. Moltz, and A. Schenk, "Neural network-based automatic liver tumor segmentation with random forest-based candidate filtering," arXiv preprint arXiv:1706.00842, 2017.
S.-T. Tran, C.-H. Cheng, and D.-G. Liu, "A multiple layer U-Net, U n-Net, for liver and liver tumor segmentation in CT," IEEE Access, vol. 9, pp. 3752-3764, 2020.
M. Aubry, S. Paris, S. W. Hasinoff, J. Kautz, and F. Durand, "Fast local laplacian filters: Theory and applications," ACM Transactions on Graphics (TOG), vol. 33, pp. 1-14, 2014.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, et al., "Imagenet large scale visual recognition challenge," International journal of computer vision, vol. 115, pp. 211-252, 2015.
O. 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.
P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, et al., "Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields," in International conference on medical image computing and computer-assisted intervention, 2016, pp. 415-423.
C. Zhang, Q. Hua, Y. Chu, and P. Wang, "Liver tumor segmentation using 2.5 D UV-Net with multi-scale convolution," Computers in Biology and Medicine, vol. 133, p. 104424, 2021.
J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3431-3440.
K. He, X. Zhang, S. Ren, and J. Sun, "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification," in Proceedings of the IEEE international conference on computer vision, 2015, pp. 1026-1034.
B. M. Tummala and S. S. Barpanda, "Liver tumor segmentation from computed tomography images using multiscale residual dilated encoder‐decoder network," International Journal of Imaging Systems and Technology, vol. 32, pp. 600-613, 2022.
Y. Liu, F. Yang, and Y. Yang, "Free-form Lesion Synthesis Using a Partial Convolution Generative Adversarial Network for Enhanced Deep Learning Liver Tumor Segmentation," arXiv preprint arXiv:2206.09065, 2022.
R. Bi, C. Ji, Z. Yang, M. Qiao, P. Lv, and H. Wang, "Residual based attention-Unet combing DAC and RMP modules for automatic liver tumor segmentation in CT," Mathematical Biosciences and Engineering, vol. 19, pp. 4703-4718, 2022.
Z. Yang, Y.-q. Zhao, M. Liao, S.-h. Di, and Y.-z. Zeng, "Semi-automatic liver tumor segmentation with adaptive region growing and graph cuts," Biomedical Signal Processing and Control, vol. 68, p. 102670, 2021G. O. Young, “Synthetic structure of industrial plastics,” in Plastics, 2nd ed., vol. 3, J. Peters, Ed. New York, NY, USA: McGraw-Hill, 1964, pp. 15–64.
Nie, Yali, Paolo Sommella, Marco Carratù, Mattias O’Nils, and Jan Lundgren. "A Deep CNN Transformer Hybrid Model for Skin Lesion Classification of Dermoscopic Images Using Focal Loss." Diagnostics 13, no. 1 (2022): 72.
Hassan, Loay, Adel Saleh, Mohamed Abdel-Nasser, Osama A. Omer, and Domenec Puig. "Promising deep semantic nuclei segmentation models for multi-institutional histopathology images of different organs." (2021).
A Laishram, K Thongam, Automatic Classification of Oral Pathologies Using Orthopantomogram Radiography Images Based on Convolutional Neural Network, International Journal of Interactive Multimedia and Artificial Intelligence 7(4), pp.69-77, 2022
Khattak, Muhammad Irfan, Mu’ath Al-Hasan, Atif Jan, Nasir Saleem, Elena Verdu, and Numan Khurshid. "Automated detection of COVID-19 using chest x-ray images and CT scans through multilayer-spatial convolutional neural networks." (2021).
