Dual attention U-net for liver tumor segmentation in CT images
DOI:
https://doi.org/10.15837/ijccc.2024.2.6226Keywords:
liver tumor, soft attention, U-net, level-set, graph-based segmentation, deep learning (DL), EEDAbstract
Segmenting liver tumors in CT scans plays a vital role in medical analysis planning. The clinicians require a detailed 3D understanding of the tumor’s location and liver anatomy, to decide about the proper surgical resection approach. Manual segmentation requires a lot of efforts and time also it depends on the expertise of clinicians. An automatic U-net based method for liver tumors delineation in CT images is proposed. It relies on employing attention-based processes to enhance the performance of U-net. Hard attention and soft attention are used to orient the U-net in learning the intended features from the target CT scans. Soft attention mechanisms, spatial and channel attentions, are employed to help in extracting the long-range relationships and allow the network to successfully distinguish tumors from the surrounding parenchyma. The paper addressed the use of region based active contour technique as postprocessing step to improve the predicted segmented tumors. The proposed approach is validated using a challenging big LiTS datasets. The achieved Dice score for the segmenting of liver tumors is 0.81. The suggested method was successful in discriminating liver tumors from surrounding tissue in heterogeneous CT scans, demonstrating its generalizability and reliability to be used for automatic analysis of the liver tumors in daily clinical practice.
References
Chen, J.; Konstan, J.A. (2010). Conference paper selectivity and impact, Communications of the ACM, 53(6), 79-83, 2010.
https://doi.org/10.1145/1743546.1743569
Mohammed, FA; Viriri, S. (2017). Liver segmentation: A survey of the state-of-the-art. In: 2017 Sudan Conference on Computer Science and Information Technology (SCCSIT). IEEE pp 1-6
https://doi.org/10.1109/SCCSIT.2017.8293049
Alirr, OI; Rahni, AAAbd. (2019). Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters. J Digit Imaging 1-20.
https://doi.org/10.1007/s10278-019-00262-8
Anter, AM; Elsoud, MA; Hassanien, AE. (2013). Automatic Liver Parenchyma Segmentation from Abdominal CT Images. 32-36
https://doi.org/10.1109/ICENCO.2013.6736472
Alirr,O; Alshatti, R; Altemeemi, S; et al. (2023). Automatic Brain Tumor Segmentation from MRI Scans using U-net Deep Learning. BioSMART 2023 - Proceedings: 5th International Conference on Bio-Engineering for Smart Technologies.
https://doi.org/10.1109/BioSMART58455.2023.10162093
Alirr, OI. (2020). Deep learning and level set approach for liver and tumor segmentation from CT scans. J Appl Clin Med Phys, 21:200-209.
https://doi.org/10.1002/acm2.13003
Hesamian, MH; Jia, W; He, X; Kennedy P. (2019). Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges. J Digit Imaging, 32:582-596.
https://doi.org/10.1007/s10278-019-00227-x
Gong, M; Zhao, B; Soraghan, J; et al. (2022). Hybrid attention mechanism for liver tumor segmentation in CT images. 2022 10th European Workshop on Visual Information Processing (EUVIP), 1-6.
https://doi.org/10.1109/EUVIP53989.2022.9922871
Gruber, N; Antholzer, S; Jaschke, W; et al.(2020). A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation. Institute of Electrical and Electronics Engineers (IEEE), pp 1-5
https://doi.org/10.1109/SampTA45681.2019.9030909
Li, W; Jia, F; Hu, Q (2015). Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks. Journal of Computer and Communications 03:146-151.
https://doi.org/10.4236/jcc.2015.311023
Alirr, OI; Rahni, AAAbd; Golkar, E (2018). An automated liver tumour segmentation from abdominal CT scans for hepatic surgical planning. Int J Comput Assist Radiol Surg 13:1169-1176.
https://doi.org/10.1007/s11548-018-1801-z
Alirr, OI; Rahni, AAAbd (2019). Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters. J Digit Imaging.
https://doi.org/10.1007/s10278-019-00262-8
Alirr, OI; Ashrani, AA (2020). Automatic atlas-based liver segmental anatomy identification for hepatic surgical planning. Int J Comput Assist Radiol Surg 15:239-248.
https://doi.org/10.1007/s11548-019-02078-x
Bilic, P; Christ, PF; Vorontsov, E; et al (2019). The Liver Tumor Segmentation Benchmark (LiTS)
Chlebus, G; Schenk, A; Moltz, JH; et al (2018). Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Sci Rep 8:1-7.
https://doi.org/10.1038/s41598-018-33860-7
Han, X (2017). MR-based synthetic CT generation using a deep convolutional neural network method. Med Phys 44:.
https://doi.org/10.1002/mp.12155
Vorontsov, E; Tang, A; Pal, C; Kadoury, S (2018). Liver lesion segmentation informed by joint liver segmentation. In: Proceedings - International Symposium on Biomedical Imaging. IEEE Computer Society, pp 1332-1335
https://doi.org/10.1109/ISBI.2018.8363817
Alirr OI (2022) Automatic deep learning system for COVID-19 infection quantification in chest CT. Multimed Tools Appl 81:527-541.
https://doi.org/10.1007/s11042-021-11299-9
Ronneberger, O; Fischer, P; Brox, T (2015). U-net: Convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9351:234-241.
https://doi.org/10.1007/978-3-319-24574-4_28
Çiçek, Ö; Abdulkadir, A; Lienkamp, SS, et al (2016). 3D U-net: Learning dense volumetric segmentation from sparse annotation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
https://doi.org/10.1007/978-3-319-46723-8_49
Ronneberger, O; Fischer, P; Brox, T (2015). U-net: Convolutional networks for biomedical image segmentation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, pp 234-241
https://doi.org/10.1007/978-3-319-24574-4_28
Milletari, F; Navab, N; Ahmadi SA (2016). V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016. Institute of Electrical and Electronics Engineers Inc., pp 565-571
https://doi.org/10.1109/3DV.2016.79
Zhou, Z; Rahman, Siddiquee MM; Tajbakhsh, N; Liang J (2018). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, S. 11045:3-11.
https://doi.org/10.1007/978-3-030-00889-5_1
Mehta R, Sivaswamy J (2017). M-net: A Convolutional Neural Network for deep brain structure segmentation. Proceedings - International Symposium on Biomedical Imaging 437-440.
https://doi.org/10.1109/ISBI.2017.7950555
Alirr O (2023). Severity Quantification of COVID-19 Infection using ResDense U-net in Chest Xray. BioSMART 2023 - Proceedings: 5th International Conference on Bio-Engineering for Smart Technologies.
https://doi.org/10.1109/BioSMART58455.2023.10162077
Alirr OI, Aizzuddin A, Rahni A (2023). Hepatic vessels segmentation using deep learning and preprocessing enhancement. J Appl Clin Med Phys e13966.
https://doi.org/10.1002/acm2.13966
Alirr OI, Rahni AAA (2021). An Automated Liver Vasculature Segmentation from CT Scans for Hepatic Surgical Planning. International Journal of Integrated Engineering 13:188-200.
Liu X, Song L, Liu S, Zhang Y (2021). A Review of Deep-Learning-Based Medical Image Segmentation Methods. Sustainability 2021, Vol 13, Page 1224 13:1224.
https://doi.org/10.3390/su13031224
Oktay O, Schlemper J, Folgoc L Le, et al (2022). Attention U-Net: Learning Where to Look for the Pancreas
Khanh TLB, Dao DP, Ho NH, et al (2020). Enhancing U-Net with Spatial-Channel Attention Gate for Abnormal Tissue Segmentation in Medical Imaging. Applied Sciences 2020, Vol 10, Page 5729 10:5729.
https://doi.org/10.3390/app10175729
Chen J, Lu Y, Yu Q, et al (2021). TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.
https://doi.org/10.1109/IGARSS46834.2022.9883628
Alirr OI, Rahni AAA, Golkar E (2018). An automated liver tumour segmentation from abdominal CT scans for hepatic surgical planning. Int J Comput Assist Radiol Surg 13:1169-1176.
https://doi.org/10.1007/s11548-018-1801-z
Altarawneh NM, Luo S, Regan B, Sun C (2015). A MODIFIED DISTANCE REGULARIZED LEVEL SET MODEL FOR LIVER SEGMENTATION. 6:1-11
https://doi.org/10.5121/sipij.2015.6101
Alirr OI, Rahni AAA (2018) Automatic liver segmentation from ct scans using intensity analysis and level-set active contours. Journal of Engineering Science and Technology 13:
Jumakulyyev I, Schultz T (2021). Fourth-Order Anisotropic Diffusion for Inpainting and Image Compression. Math Vis 99-124.
https://doi.org/10.1007/978-3-030-56215-1_5
Woo S, Park J, Lee JY, Kweon IS (2018). CBAM: Convolutional block attention module. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11211 LNCS:3-19.
https://doi.org/10.1007/978-3-030-01234-2_1
Mendrik AM, Vonken EJ, Rutten A, et al (2009). Noise reduction in computed tomography scans using 3-D anisotropic hybrid diffusion with continuous switch. IEEE Trans Med Imaging 28:1585-1594.
https://doi.org/10.1109/TMI.2009.2022368
Schlemper J, Oktay O, Schaap M, et al (2019). Attention gated networks: Learning to leverage salient regions in medical images. Med Image Anal 53:197-207.
https://doi.org/10.1016/j.media.2019.01.012
Chan T, Vese L (2001) Active contours without edges. Image processing, IEEE transactions on image processing, 10(2), 266-277.
https://doi.org/10.1109/83.902291
Osher S, Fedkiw R (2001) Level set methods: an overview and some recent results. Journal of Computational physics, 169(2), 463-502.
https://doi.org/10.1006/jcph.2000.6636
Lankton S, Tannenbaum A (2008) Localizing Region-Based Active Contours. IEEE Transactions on Image Processing 17:2029-2039.
https://doi.org/10.1109/TIP.2008.2004611
Liu X, Guo S, Yang B, et al (2018) Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks. Journal of digital imaging, 31, 748-760.
https://doi.org/10.1007/s10278-018-0052-4
Bai Z, Jiang H, Li S, Yao YD (2019) Liver Tumor Segmentation Based on Multi-Scale Candidate Generation and Fractal Residual Network. IEEE Access 7:82122-82133.
https://doi.org/10.1109/ACCESS.2019.2923218
Jiang H, Shi T, Bai Z, Huang L (2019) AHCNet: An Application of Attention Mechanism and Hybrid Connection for Liver Tumor Segmentation in CT Volumes. IEEE Access 7:24898-24909.
Additional Files
Published
Issue
Section
License
Copyright (c) 2024 Omar
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
