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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Mini-Review Article

Review on Deep Learning Methodologies in Medical Image Restoration and Segmentation

Author(s): R. Hephzibah, Hepzibah Christinal Anandharaj*, G. Kowsalya, R. Jayanthi and D. Abraham Chandy

Volume 19, Issue 8, 2023

Published on: 29 December, 2022

Article ID: e070422203255 Pages: 11

DOI: 10.2174/1573405618666220407112825

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Abstract

This paper comprehensively reviews two major image processing tasks, such as restoration and segmentation in the medical field, from a deep learning perspective. These processes are essential because restoration removes noise and segmentation extracts the specific region of interest of an image, both of which are necessary for accurate diagnosis and therapy. This paper mainly focuses on deep learning techniques. It plays a prominent role over other conventional techniques in handling a large number of datasets in the medical field and provides accurate results. This paper reviewed the application of different convolutional neural network architectures in the restoration and segmentation processes. Based on the results in the case of image restoration, TLR-CNN and Stat-CNN are promising in achieving better PSNR, noise suppression, artifact suppression and improving the overall image quality. For the segmentation process, LCP net achieves the Dice score of 98.12% and sensitivity of 98.95% in the cell contour segmentation; the 3D FCNN model is found to be the best method for the segmentation of brain tumors. This review shows that deep learning methodologies can be a better alternative for medical image restoration and segmentation tasks, as data size is an important concern today.

Keywords: Deep learning, convolutional neural network, image segmentation, image restoration, medical images, algorithms

Graphical Abstract

[1]
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521(7553): 436-44.
[2]
Wan J, Wang D, Hoi SC, et al. Deep learning for content-based image retrieval: A comprehensive study. In Proceedings of the 22nd ACM International Conference on Multimedia. 2014 Nov 3; Orlando, FL, USA.. 157-66.
[http://dx.doi.org/10.1145/2647868.2654948]
[3]
Deng L, Yu D. Deep learning: Methods and applications. Foundations and trends in signal processing 2014; 7(3-4): 197-387.
[http://dx.doi.org/10.1561/9781601988157]
[4]
Chen XW, Lin X. Big data deep learning: Challenges and perspectives. IEEE Access 2014; 2: 514-25.
[http://dx.doi.org/10.1109/ACCESS.2014.2325029]
[5]
Abdel-Hamid O, Deng L, Yu D. Exploring convolutional neural network structures and optimization techniques for speech recognition. In: Interspeech. Lyon, France 2013 Aug 25; pp. 73-5.
[http://dx.doi.org/10.21437/Interspeech.2013-744]
[6]
Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V. Deep neural nets as a method for quantitative structure-activity relationships. J Chem Inf Model 2015; 55(2): 263-74.
[http://dx.doi.org/10.1021/ci500747n] [PMID: 25635324]
[7]
Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P. Natural language processing (almost) from scratch. J Mach Learn Res 2011; 12: 2493-537.
[8]
Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems. 2014 Dec 8; Montreal, Quebec, Canada.. 3104-12.
[9]
Janowczyk A, Madabhushi A. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. J Pathol Inform 2016; 7(1): 29.
[http://dx.doi.org/10.4103/2153-3539.186902] [PMID: 27563488]
[10]
Abadi M, Agarwal A, Barham P, et al. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv 2016; 1603.04467.
[11]
Heidenreich A, Desgrandschamps F, Terrier F. Modern approach of diagnosis and management of acute flank pain: Review of all imaging modalities. Eur Urol 2002; 41(4): 351-62.
[http://dx.doi.org/10.1016/S0302-2838(02)00064-7] [PMID: 12074804]
[12]
Liu Y, Cheng HD, Huang JH, et al. Computer aided diagnosis system for breast cancer based on color Doppler flow imaging. J Med Syst 2012; 36(6): 3975-82.
[http://dx.doi.org/10.1007/s10916-012-9869-4] [PMID: 22791011]
[13]
Diao XF, Zhang XY, Wang TF, Chen SP, Yang Y, Zhong L. Highly sensitive computer aided diagnosis system for breast tumor based on color Doppler flow images. J Med Syst 2011; 35(5): 801-9.
[http://dx.doi.org/10.1007/s10916-010-9461-8] [PMID: 20703733]
[14]
Bhatt C, Kumar I, Vijayakumar V, Singh KU, Kumar A. The state of the art of deep learning models in medical science and their challenges. Multimedia Syst 2021; 27: 599-613.
[15]
Abreu S. 2019; Automated architecture design for deep neural networks. arXiv preprint arXiv 2019; 1908.10714.
[16]
Larochelle H, Bengio Y, Louradour J, Lamblin P. Exploring strategies for training deep neural networks. J Mach Learn Res 2009; 10(1): 1-40.
[17]
Haber E, Ruthotto L. Stable architectures for deep neural networks. Inverse Probl 2017; 34(1): 014004.
[http://dx.doi.org/10.1088/1361-6420/aa9a90]
[18]
Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are featured in deep neural networks? arXiv preprint arXiv 2014; 1411.1792.
[19]
Fernandes FE, Yen GG. Automatic searching and pruning of deep neural networks for medical imaging diagnostic. IEEE Trans Neural Netw Learn Syst 2021; 32(12): 566-74.
[PMID: 33048758]
[20]
Miikkulainen R, Liang J, Meyerson E, et al. Evolving deep neural networks. In: Kozma R, Alippi C, Choe Y, Morabito FC, Eds. Artificial intelligence in the age of neural networks and brain computing cambridge, massachusetts. Academic press 2019; pp. 293-312.
[http://dx.doi.org/10.1016/B978-0-12-815480-9.00015-3]
[21]
Chen Y, Xie Y, Song L, Chen F, Tang T. A survey of accelerator architectures for deep neural networks. Engineering (Beijing) 2020; 6(3): 264-74.
[http://dx.doi.org/10.1016/j.eng.2020.01.007]
[22]
Kollias D, Tagaris A, Stafylopatis A, Kollias S, Tagaris G. Deep neural architectures for prediction in healthcare. Complex Intell Syst 2018; 4(2): 119-31.
[http://dx.doi.org/10.1007/s40747-017-0064-6]
[23]
Lucas A, Iliadis M, Molina R, Katsaggelos AK. Using deep neural networks for inverse problems in imaging: Beyond analytical methods. IEEE Signal Process Mag 2018; 35(1): 20-36.
[http://dx.doi.org/10.1109/MSP.2017.2760358]
[24]
Kaji S, Kida S. Overview of image-to-image translation by use of deep neural networks: Denoising, super-resolution, modality conversion, and reconstruction in medical imaging. Radiological Phys Technol 2019; 12(3): 235-48.
[http://dx.doi.org/10.1007/s12194-019-00520-y] [PMID: 31222562]
[25]
Albawi S, Mohammed TA, Al-Zawi S. Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET). 2017 Aug 21-23; Antalya, Turkey. 1-6.
[26]
O’Shea K, Nash R. An introduction to convolutional neural networks. arXiv preprint arXiv 2015; 1511.08458.
[27]
Kuo CC. Understanding convolutional neural networks with a mathematical model. J Vis Commun Image Represent 2016; 41: 406-13.
[http://dx.doi.org/10.1016/j.jvcir.2016.11.003]
[28]
Suk HI. An introduction to neural networks and deep learning. In: Zhou SK, Greenspan H, Shen D, Eds. Deep learning for medical image analysis. Cambridge, massachusetts: Academic press 2017; pp. 3-24.
[http://dx.doi.org/10.1016/B978-0-12-810408-8.00002-X]
[29]
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. J Mach Learn Res 2014; 15(1): 1929-58.
[30]
Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning. 2015 Jul 6; pp. 448-56.
[31]
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012; 25: 1097-105.
[32]
Sagheer SV, George SN. A review on medical image denoising algorithms. Biomed Signal Process Control 2020; 61: 102036.
[http://dx.doi.org/10.1016/j.bspc.2020.102036]
[33]
Maini R, Aggarwal H. A comprehensive review of image enhancement techniques. arXiv preprint arXiv 2010; 1003.4053.
[34]
Rani S, Jindal S, Kaur B. A brief review on image restoration techniques. Int J Comput Appl 2016; 150(12): 30-3.
[35]
Hussain AJ, Al-Fayadh A, Radi N. Image compression techniques: A survey in lossless and lossy algorithms. Neurocomputing 2018; 300: 44-69.
[http://dx.doi.org/10.1016/j.neucom.2018.02.094]
[36]
Kowsalya G, Christinal H, Chandy DA, Jebasingh S, Bajaj C. Analysis of the impact of measurement matrices in compressive sensing for medical images. Adv Math Sci J 2020; 9(2): 591-600.
[37]
Christinal HA, Díaz-Pernil D, Real P. Region-based segmentation of 2D and 3D images with tissue-like P systems. Pattern Recognit Lett 2011; 32(16): 2206-12.
[http://dx.doi.org/10.1016/j.patrec.2011.05.004]
[38]
Tang J. A color image segmentation algorithm based on region growing. 2nd International Conference on Computer Engineering and Technology. 2010 Apr 16-18; Chengdu, China. 2010; pp. V6-634.
[39]
Irum I, Raza M, Sharif M. Morphological techniques for medical images: A review. Res J Appl Sci Eng Technol 2012; 4(17): 2948-62.
[40]
Cai W, Song Y, Kumar A, Kim J, Feng DD. Content-based large- scale medical image retrieval. In: Feng DD, Ed. Biomedical Information Technology. Cambridge, Massachusetts: Academic Press 2020; pp. 321-68.
[41]
Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK. Medical image analysis using convolutional neural networks: A review. J Med Syst 2018; 42(11): 226.
[http://dx.doi.org/10.1007/s10916-018-1088-1] [PMID: 30298337]
[42]
Maier A, Syben C, Lasser T, Riess C. A gentle introduction to deep learning in medical image processing. Z Med Phys 2019; 29(2): 86-101.
[http://dx.doi.org/10.1016/j.zemedi.2018.12.003] [PMID: 30686613]
[43]
Alakwaa W, Nassef M, Badr A. Lung cancer detection and classification with 3D convolutional neural network (3D-CNN). Lung Cancer 2017; 8(8): 409.
[44]
Dabeer S, Khan MM, Islam S. Cancer diagnosis in histopathological image: CNN based approach. Inform Med Unlocked 2019; 16: 100231.
[http://dx.doi.org/10.1016/j.imu.2019.100231]
[45]
Balaji K, Lavanya K. Medical image analysis with deep neural networks. In: Sangaiah AK, Ed. Deep learning and parallel computing environment for bioengineering systems. Cambridge, Massachusetts: Academic Press 2019; pp. 75-97.
[http://dx.doi.org/10.1016/B978-0-12-816718-2.00012-9]
[46]
Tan YJ, Sim KS, Ting FF. Breast cancer detection using convolutional neural networks for mammogram imaging system. In 2017 International Conference on Robotics, Automation and Sciences (ICORAS). 2017 Nov 27-29; Melaka, Malaysia. 1-5.
[http://dx.doi.org/10.1109/ICORAS.2017.8308076]
[47]
Huang Z, Li Q, Lu J, Feng J, Hu J, Chen P. Recent advances in medical image processing. Acta Cytol 2021; 65(4): 310-23.
[PMID: 33176311]
[48]
Houssein EH, Emam MM, Ali AA, Suganthan PN. Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review. Expert Syst Appl 2021; 167: 114161.
[49]
Bhattacharya S, Reddy Maddikunta PK, Pham QV, et al. Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey. Sustain Cities Soc 2021; 65: 102589.
[http://dx.doi.org/10.1016/j.scs.2020.102589] [PMID: 33169099]
[50]
Cai X, Li X, Razmjooy N, Ghadimi N. Breast cancer diagnosis by convolutional neural network and advanced thermal exchange optimization algorithm. Comput Math Methods Med 2021; 2021: 5595180.
[http://dx.doi.org/10.1155/2021/5595180] [PMID: 34790252]
[51]
Guo Z, Xu L, Si Y, Razmjooy N. Novel computer‐aided lung cancer detection based on convolutional neural network‐based and feature‐based classifiers using metaheuristics. Int J Imaging Syst Technol 2021; 31(4): 1954-69.
[http://dx.doi.org/10.1002/ima.22608]
[52]
Hu A, Razmjooy N. Brain tumor diagnosis based on metaheuristics and deep learning. Int J Imaging Syst Technol 2021; 31(2): 657-69.
[http://dx.doi.org/10.1002/ima.22495]
[53]
Tian Q, Wu Y, Ren X, Razmjooy N. A New optimized sequential method for lung tumor diagnosis based on deep learning and converged search and rescue algorithm. Biomed Signal Process Control 2021; 68: 102761.
[http://dx.doi.org/10.1016/j.bspc.2021.102761]
[54]
Toğaçar M, Özkurt KB, Ergen B, Cömert Z. BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Physica A 2020; 545: 123592.
[http://dx.doi.org/10.1016/j.physa.2019.123592]
[55]
Ashhar SM, Mokri SS, Abd Rahni AA, et al. Comparison of deep learning Convolutional Neural Network (CNN) architectures for CT lung cancer classification. International Journal of Advanced Technology and Engineering Exploration 2021; 8(74): 126-34.
[http://dx.doi.org/10.19101/IJATEE.2020.S1762126]
[56]
Zhang Q, Wang Y, Qiu S, Chen J, Sun L, Li Q. 3D-PulCNN: Pulmonary cancer classification from hyperspectral images using convolution combination unit based CNN. J Biophotonics 2021; 14(12): e202100142.
[http://dx.doi.org/10.1002/jbio.202100142] [PMID: 34405557]
[57]
Toğaçar M, Ergen B, Cömert Z. BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Med Hypotheses 2020; 134: 109531.
[http://dx.doi.org/10.1016/j.mehy.2019.109531] [PMID: 31877442]
[58]
Shakeel PM, Burhanuddin MA, Desa MI. Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier. Neural Comput Appl 2020; 1-14.
[http://dx.doi.org/10.1007/s00521-020-04842-6]
[59]
Liu C, Pang M, Zhao R. Novel superpixel-based algorithm for segmenting lung images via convolutional neural network and random forest. IET Image Process 2020; 14(16): 4340-8.
[http://dx.doi.org/10.1049/iet-ipr.2019.1171]
[60]
Aswathi VM, Mathew J. A review on image restoration in medical images. Compusoft 2015; 4(4): 1588.
[61]
McCann MT, Jin KH, Unser M. Convolutional neural networks for inverse problems in imaging: A review. IEEE Signal Process Mag 2017; 34(6): 85-95.
[http://dx.doi.org/10.1109/MSP.2017.2739299]
[62]
Yang Q, Yan P, Zhang Y, et al. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans Med Imaging 2018; 37(6): 1348-57.
[http://dx.doi.org/10.1109/TMI.2018.2827462] [PMID: 29870364]
[63]
Yang Q, Yan P, Kalra MK, Wang G. CT image denoising with perceptive deep neural networks. arXiv preprint arXiv 2017; 1702.07019.
[64]
Choi K, Lim JS, Kim S. StatNet: Statistical image restoration for low-dose CT using deep learning. IEEE J Sel Top Signal Process 2020; 14(6): 1137-50.
[http://dx.doi.org/10.1109/JSTSP.2020.2998413]
[65]
Kim J, Kim J, Han G, Rim C, Jo H. Low-dose CT image restoration using generative adversarial networks. Inform Med Unlocked 2020; 21: 100468.
[http://dx.doi.org/10.1016/j.imu.2020.100468]
[66]
Zhong A, Li B, Luo N, Xu Y, Zhou L, Zhen X. Image restoration for low-dose CT via transfer learning and residual network. IEEE Access 2020; 8: 112078-91.
[http://dx.doi.org/10.1109/ACCESS.2020.3002534]
[67]
Christ PF, Ettlinger F, Grün F, et al. Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. arXiv preprint arXiv 1702.05970.2017;
[68]
Seeja RD, Suresh A. Deep learning based skin lesion segmentation and classification of melanoma using Support Vector Machine (SVM). APJCP 2019; 20(5): 1555.
[PMID: 31128062]
[69]
Wang D, Hu G, Lyu C. Multi-path connected network for medical image segmentation. J Vis Commun Image Represent 2020; 71: 102852.
[http://dx.doi.org/10.1016/j.jvcir.2020.102852]
[70]
Daimary D, Bora MB, Amitab K, Kandar D. Brain tumor segmentation from MRI images using hybrid convolutional neural networks. Procedia Comput Sci 2020; 167: 2419-28.
[http://dx.doi.org/10.1016/j.procs.2020.03.295]
[71]
Alalwan N, Abozeid A, ElHabshy AA, Alzahrani A. Efficient 3d deep learning model for medical image semantic segmentation. Alex Eng J 2021; 60(1): 1231-9.
[http://dx.doi.org/10.1016/j.aej.2020.10.046]
[72]
Peng D, Xiong S, Peng W, Lu J. LCP-Net: A local context-perception deep neural network for medical image segmentation. Expert Syst Appl 2021; 168: 114234.
[http://dx.doi.org/10.1016/j.eswa.2020.114234]
[73]
Jha D, Smedsrud PH, Riegler MA, et al. Resunet++: An advanced architecturefor medical image segmentation. In: 2019 IEEE International Symposium on Multimedia (ISM). 2019 Dec 9-11; San Diego, CA, USA. 225-2255.
[http://dx.doi.org/10.1109/ISM46123.2019.00049]
[74]
Jha D, Riegler MA, Johansen D, Halvorsen P, Johansen HD. Doubleu-net: A deep convolutional neural network for medical image segmentation. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). 2020 Jul 28-30; Rochester, MN, USA. 558-64.
[http://dx.doi.org/10.1109/CBMS49503.2020.00111]
[75]
Sun J, Peng Y, Guo Y, Li D. Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN. Neurocomputing 2021; 423: 34-4.
[http://dx.doi.org/10.1016/j.neucom.2020.10.031]

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