Abstract
A multimedia-based medical decision-making system is an ultimate requirement in the medical imaging domain. In the healthcare sector, achieving quick and efficient results is one of the primary needs as well as a necessity for both doctors and patients. Liver cancer is increasingly spreading disease across the globe, and its timely diagnosis requires an accurate and precise tumor segmentation. However, it is very challenging to segment the tumors because of the variability in appearance, hazy borders, diverse densities, and sizes of tumors. Currently, deep learning-based approaches are being applied in a variety of domains which results in better performances. In this paper, a deep learning method based on modified U-Net referred to as Residual-Atrous U-Net (RA-Net) is proposed to segment the liver tumors. The suggested model is implemented by employing U-Net as a base model and extracts the tumor’s features utilizing a parallel structure-based Atrous convolution block embedded in the original U-Net. This addresses the heterogeneity of tumors in terms of sizes as well as retaining the wider context without introducing any parameters to the model due to the various scales dilated kernels. Moreover, a strong, smooth, and non-monoatomic Mish activation function is deployed in this parallel Atrous block to bring nonlinearity. Besides this, the features of tumors are also extracted from Res Block simultaneously which learns the residual between the input and output feature maps via a skip link that jumps directly from input to output. This identity mapping in this block overcomes network degradation and increases performance. Later on, all these extracted features are fused to be sent to the network and hence improve the feature learning of the original U-Net. Furthermore, we have used the strategy of segmenting the tumors directly from the CT-scan instead of the two-stage process used by the majority of existing methods. We have performed experimental analysis over the 3DIRCADb dataset and according to the results shown by the proposed RA-Net, the Jaccard score which is a performance indicator stands out at 72%.
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References
Organization WH (1983) Prevention of liver cancer: report of a WHO meeting [held in Geneva from 30 January to 4 February 1983]. World Health Organization
Davis GL, Dempster J, Meler JD, Orr DW, Walberg MW, Brown B, Berger BD, O'Connor JK, Goldstein RM (2008) Hepatocellular carcinoma: management of an increasingly common problem. In: Baylor University Medical Center Proceedings, vol 3. Taylor & Francis, pp 266–280
Li W (2015) Automatic segmentation of liver tumor in CT images with deep convolutional neural networks. J Comput Commun 3(11):146
Li BN, Chui CK, Chang S, Ong SH (2012) A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images. Expert Syst Appl 39(10):9661–9668
Moghbel M, Mashohor S, Mahmud R, Saripan MIB (2016) Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring. EXCLI J 15:406
Chlebus G, Meine H, Moltz JH, Schenk A (2017) Neural network-based automatic liver tumor segmentation with random forest-based candidate filtering. arXiv preprint arXiv:1706.00842
Kumar S, Moni R, Rajeesh J (2011) Automatic segmentation of liver and tumor for CAD of liver. J Adv Inf Technol 2(1):63–70
Moltz JH, Bornemann L, Dicken V, Peitgen H (2008) Segmentation of liver metastases in CT scans by adaptive thresholding and morphological processing. In: MICCAI Workshop, vol 43, p 195
Zhao J, Li D, Kassam Z, Howey J, Chong J, Chen B, Li S (2020) Tripartite-GAN: synthesizing liver contrast-enhanced MRI to improve tumor detection. Med Image Anal 63:101667
Kumar S, Devapal D (2014) Survey on recent CAD system for liver disease diagnosis. In: 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). IEEE, pp 763–766
Gruber N, Antholzer S, Jaschke W, Kremser C, Haltmeier M (2019) A joint deep learning approach for automated liver and tumor segmentation. In: 2019 13th International Conference on Sampling Theory and Applications (SampTA). IEEE, pp 1–5
Trivizakis E, Manikis GC, Nikiforaki K, Drevelegas K, Constantinides M, Drevelegas A, Marias K (2018) Extending 2-D convolutional neural networks to 3-D for advancing deep learning cancer classification with application to MRI liver tumor differentiation. IEEE J Biomed Health Inform 23(3):923–930
Wang P, Chen P, Yuan Y, Liu D, Huang Z, Hou X, Cottrell G (2018) Understanding convolution for semantic segmentation. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, pp 1451–1460
Nawaz H, Maqsood M, Afzal S, Aadil F, Mehmood I, Rho S (2020) A deep feature-based real-time system for Alzheimer disease stage detection. Multimed Tools Appl 80:35789–35807
Xia K, Yin H, Qian P, Jiang Y, Wang S (2019) Liver semantic segmentation algorithm based on improved deep adversarial networks in combination of weighted loss function on abdominal CT images. IEEE Access 7:96349–96358
Zhang J, Xie Y, Zhang P, Chen H, Xia Y, Shen C (2019) Light-weight hybrid convolutional network for liver tumor segmentation. In: IJCAI, pp 4271–4277
Habib AB, Akhter ME, Sultaan R, Zahir ZB, Arfin R, Haque F, Amir SAB, Hussain MS, Palit R (2020) Performance analysis of different 2D and 3D CNN model for liver semantic segmentation: a review. In: International Conference on Medical Imaging and Computer-Aided Diagnosis. Springer, pp 166–174
Kuo C-L, Cheng S-C, Lin C-L, Hsiao K-F, Lee S-H (2017) Texture-based treatment prediction by automatic liver tumor segmentation on computed tomography. In: 2017 International Conference on Computer, Information and Telecommunication Systems (CITS). IEEE, pp 128–132
Almotairi S, Kareem G, Aouf M, Almutairi B, Salem MA-M (2020) Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5):1516
Umer J, Irtaza A, Nida N (2020) MACCAI LiTS17 liver tumor segmentation using RetinaNet. In: 2020 IEEE 23rd International Multitopic Conference (INMIC). IEEE, pp 1–5
Wong D, Liu J, Fengshou Y, Tian Q, Xiong W, Zhou J, Qi Y, Han T, Venkatesh S, Wang S-C (2008) A semi-automated method for liver tumor segmentation based on 2D region growing with knowledge-based constraints. In: MICCAI Workshop, vol 43, p 159
Li X, Chen H, Qi X, Dou Q, Fu C-W, Heng P-A (2018) H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imaging 37(12):2663–2674
Huang W, Li N, Lin Z, Huang G-B, Zong W, Zhou J, Duan Y (2013) Liver tumor detection and segmentation using kernel-based extreme learning machine. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 3662–3665
Wu W, Wu S, Zhou Z, Zhang R, Zhang Y (2017) 3D liver tumor segmentation in CT images using improved fuzzy C-means and graph cuts. BioMed Res Int 2017:1–11
Raj A, Jayasree M (2016) Automated liver tumor detection using Markov random field segmentation. Procedia Technol 24:1305–1310
Yang Z, Zhao Y, Liao M, Di S, Zeng Y (2021) Semi-automatic liver tumor segmentation with adaptive region growing and graph cuts. Biomed Signal Process Control 68:10267
Zhang X, Tian J, Deng K, Wu Y, Li X (2010) Automatic liver segmentation using a statistical shape model with optimal surface detection. IEEE Trans Biomed Eng 57(10):2622–2626
Li D, Liu L, Chen J, Li H, Yin Y, Ibragimov B, Xing L (2016) Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours. Phys Med Biol 62(1):272
Chartrand G, Cresson T, Chav R, Gotra A, Tang A, De Guise JA (2016) Liver segmentation on CT and MR using Laplacian mesh optimization. IEEE Trans Biomed Eng 64(9):2110–2121
Luo Q, Qin W, Wen T, Gu J, Gaio N, Chen S, Li L, Xie Y (2013) Segmentation of abdomen MR images using kernel graph cuts with shape priors. Biomed Eng Online 12(1):1–19
Christ PF, Elshaer MEA, Ettlinger F, Tatavarty S, Bickel M, Bilic P, Rempfler M, Armbruster M, Hofmann F, D’Anastasi M (2016) 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. Springer, pp 415–423
Tran S-T, Cheng C-H, Liu D-G (2020) A multiple layer U-Net, U n-Net, for liver and liver tumor segmentation in CT. IEEE Access 9:3752–3764
Seo H, Huang C, Bassenne M, Xiao R, Xing L (2019) Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images. IEEE Trans Med Imaging 39(5):1316–1325
Liu Z, Song Y-Q, Sheng VS, Wang L, Jiang R, Zhang X, Yuan D (2019) Liver CT sequence segmentation based with improved U-Net and graph cut. Expert Syst Appl 126:54–63
Bai Z, Jiang H, Li S, Yao Y-D (2019) Liver tumor segmentation based on multi-scale candidate generation and fractal residual network. IEEE Access 7:82122–82133
Budak Ü, Guo Y, Tanyildizi E, Şengür A (2020) Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation. Med Hypotheses 134:109431
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp 234–241
Christ PF, Ettlinger F, Grün F, Elshaera MEA, Lipkova J, Schlecht S, Ahmaddy F, Tatavarty S, Bickel M, Bilic P (2017) Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. arXiv preprint arXiv:1702.05970
He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1026–1034
Li S, Tso GKF (2018) Bottleneck supervised u-net for pixel-wise liver and tumor segmentation. arXiv preprint arxiv:1810.10331
Ali Z, Irtaza A, Maqsood M (2021) An IOMT assisted lung nodule segmentation using enhanced receptive field-based modified UNet. Pers Ubiquitous Comput. https://doi.org/10.1007/s00779-021-01637-x
Bukhari M, Bajwa KB, Gillani S, Maqsood M, Durrani MY, Mehmood I, Ugail H, Rho S (2020) An efficient gait recognition method for known and unknown covariate conditions. IEEE Access 9:6465–6477
Afzal S, Maqsood M, Mehmood I, Niaz MT, Seo S (2021) An efficient false-positive reduction system for cerebral microbleeds detection. CMC Comput Mater Contin 66(3):2301–2315
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(8):1169–1176
Han Y, Li X, Wang B, Wang L (2021) Boundary loss-based 2.5 D fully convolutional neural networks approach for segmentation: a case study of the liver and tumor on computed tomography. Algorithms 14(5):144
Zhang C, Ai D, Feng C, Fan J, Song H, Yang J (2020) Dial/hybrid cascade 3DResUNet for liver and tumor segmentation. In: Proceedings of the 2020 4th International Conference on Digital Signal Processing, pp 92–96
Acknowledgements
This research was supported by the Chung-Ang University Research Scholarship Grants in 2021 and also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1F1A1060668).
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This paper is an extended version of our paper published in the Proceedings of the 2020 International Conference on Artificial Intelligence (ICAI), Las Vegas, USA, 27–30 July 2020.
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Kalsoom, A., Maqsood, M., Yasmin, S. et al. A computer-aided diagnostic system for liver tumor detection using modified U-Net architecture. J Supercomput 78, 9668–9690 (2022). https://doi.org/10.1007/s11227-021-04266-6
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DOI: https://doi.org/10.1007/s11227-021-04266-6