Skip to main content
SpringerLink
Log in

Perspective Review on Deep Learning Models to Medical Image Segmentation

  • Conference paper
  • First Online:
Computational Intelligence in Data Science (ICCIDS 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 654))

Included in the following conference series:

  • 374 Accesses

Abstract

In recent days, deep learning is on rage and is gaining a huge amount of popularity due to its supremacy in terms of accuracy. Deep learning is being used for a vast number of applications out of which healthcare is an important category. In this paper, we discuss the role of deep learning in medical image segmentation. It is also known as the automated or semi-automated detection of edges within various medical image modalities so as to identify the region of interest. Furthermore, we also explore the various deep learning networks that are widely preferred for medical image segmentation along with the architecture and overview of each network. This paper covers the most recent and widely preferred deep learning networks such as Convolutional Neural Network (CNN) and other related networks such as Alexnet, Resnet, U-net and V-net. The challenges and limitations of the emerging DL networks is also studied.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Van Hiep Phung, E.J.: A high‐accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Appl. Sci. 9, 4500 (2019)

    Google Scholar 

  2. Ke, Q., Boussaid, F.: Computer vision for human–machine interaction. Comput. Vis. Assist. Heathcare (2018)

    Google Scholar 

  3. Yang, B., Guo, H.: Design of cyber-physical-social systems with forensic-awareness based on deep learning. Adv. Comput. 120, 39–79 (2020)

    Google Scholar 

  4. Thillaikkarasi, R., Saravanan, S.: An enhancement of deep learning algorithm for brain tumor segmentation using kernel based CNN with M-SVM. J. Med. Syst. 43, 1–7 (2019)

    Google Scholar 

  5. Sajid, S., Hussain, S.: Brain tumor detection and segmentation in MR images using deep learning. Arab. J. Sci. Eng. 44, 9249–9261 (2019)

    Google Scholar 

  6. Ramzan, F., Khan, M.U.G., Iqbal, S., Saba, T., Rehman, A.: Volumetric segmentation of brain regions from MRI scans using 3D convolutional neural networks. IEEE Access 8, 103697–103709 (2020). https://doi.org/10.1109/ACCESS.2020.2998901

    Article  Google Scholar 

  7. Anand Kumar, G., Sridevi, P.V.: 3D deep learning for automatic brain MR tumor segmentation with T-spline intensity inhomogeneity correction. Autom. Control Comput. Sci. 52(5), 439–450 (2018). https://doi.org/10.3103/S0146411618050048

    Article  Google Scholar 

  8. Ben Naceur, M., Akil, M., Saouli, R., Kachouri, R.: Fully automatic brain tumour segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy. Med. Image Anal. 63, 101692 (2020). https://doi.org/10.1016/j.media.2020.101692. Epub 29 Apr 2020. PMID: 32417714

    Article  Google Scholar 

  9. Feng, N., Geng, X., Qin, L.: Study on MRI medical image segmentation technology based on CNN-CRF model. IEEE Access 8, 60505–60514 (2020). https://doi.org/10.1109/ACCESS.2020.2982197

    Article  Google Scholar 

  10. Xiong, Z., Fedorov, V.V., Fu, X., Cheng, E., Macleod, R., Zhao, J.: Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network. IEEE Trans. Med. Imaging 38(2), 515–524 (2019). https://doi.org/10.1109/TMI.2018.2866845. PMID: 30716023; PMCID: PMC6364320

    Article  Google Scholar 

  11. Mittal, M., Goyal, L.M., Kaur, S., Kaur, I., Amit Verma, D., Hemanth, J.: Deep learning based enhanced tumour segmentation approach for MR brain images. Appl. Soft Comput. 78, 346–354 (2019)

    Article  Google Scholar 

  12. Deng, W., Shi, Q., Wang, M., Zheng, B., Ning, N.: Deep learning-based HCNN and CRF-RRNN model for brain tumor segmentation. IEEE Access 8, 26665–26675 (2020). https://doi.org/10.1109/ACCESS.2020.2966879

    Article  Google Scholar 

  13. Ilesanmi, A.E., Chaumrattanakul, U., Makhanov, S.S.: A method for segmentation of tumours in breast ultrasound images using the variant enhanced deep learning. Biocybern. Biomed. Eng. 41, 802–818 (2021)

    Article  Google Scholar 

  14. Al-antari, M.A., Al-masni, M.A., Choi, M.-T., Han, S.-M., Kim, T.-S.: A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int. J. Med. Inform. 117, 44–54 (2018)

    Article  Google Scholar 

  15. Webb, J.M., Meixner, D.D., Adusei, S.A., Polley, E.C., Fatemi, M., Alizad, A.: Automatic deep learning semantic segmentation of ultrasound thyroid cineclips using recurrent fully convolutional networks. IEEE Access 9, 5119–5127 (2021). https://doi.org/10.1109/ACCESS.2020.3045906

    Article  Google Scholar 

  16. Kumar, V., et al.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. IEEE Access 8, 63482–63496 (2020). https://doi.org/10.1109/ACCESS.2020.2982390

    Article  Google Scholar 

  17. Nguyen, N., Lee, S.: Robust boundary segmentation in medical images using a consecutive deep encoder-decoder network. IEEE Access 7, 33795–33808 (2019). https://doi.org/10.1109/ACCESS.2019.2904094

    Article  Google Scholar 

  18. Al-Louzi, O.: Progressive multifocal leukoencephalopathy lesion and brain parenchymal segmentation from MRI using serial deep convolutional neural networks. NeuroImage Clin. 28, 102499 (2020)

    Google Scholar 

  19. . Chen, Y, Wang, Y., Hu, F., Wang, D.: A lung dense deep convolution neural network for robust lung parenchyma segmentation. IEEE Access 8, 93527–93547 (2020). https://doi.org/10.1109/ACCESS.2020.2993953

  20. Ramya, J., Rajakumar, M.P., Uma Maheswari, B.: HPWO-LS-based deep learning approach with S-ROA-optimized optic cup segmentation for fundus image classification. Neural Comput. Appl. 33(15), 9677–9690 (2021). https://doi.org/10.1007/s00521-021-05732-1

    Article  Google Scholar 

  21. Karimi, D., et al.: Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images. Med. Image Anal. 57, 186–196 (2019). https://doi.org/10.1016/j.media.2019.07.005

    Article  Google Scholar 

  22. Yan, K., Wang, X., Kim, J., Khadra, M., Fulham, M., Feng, D.: A propagation-DNN: deep combination learning of multi-level features for MR prostate segmentation. Comput. Methods Programs Biomed. 170, 11–21 (2019)

    Article  Google Scholar 

  23. Salvi, M., et al.: A hybrid deep learning approach for gland segmentation in prostate histopathological images. Artif. Intell. Med. 115, 102076 (2021)

    Article  Google Scholar 

  24. Hu, H., et al.: Automatic segmentation of left and right ventricles in cardiac MRI using 3D-ASM and deep learning. Signal Process. Image Commun. 96, 116303, 101902 (2021)

    Google Scholar 

  25. Abdeltawab, H., et al.: A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images. Comput. Med. Imaging Graph. 81, 101717 (2021)

    Article  Google Scholar 

  26. Tang, X., et al.: Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT. Eur. J. Nucl. Med. Mol. Imaging 47(12), 2742–2752 (2020). https://doi.org/10.1007/s00259-020-04800-3

    Article  Google Scholar 

  27. Ryu, H., Shin, S.Y., Lee, J.Y., Lee, K.M., Kang, H.-J., Yi, J.: Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning. Eur. Radiol. 31(11), 8733–8742 (2021). https://doi.org/10.1007/s00330-021-07850-9

    Article  Google Scholar 

  28. Apiparakoon, T., et al.: MaligNet: semisupervised learning for bone lesion instance segmentation using bone scintigraphy. IEEE Access 8, 27047–27066 (2020). https://doi.org/10.1109/ACCESS.2020.2971391

    Article  Google Scholar 

  29. Allehaibi, K.H.S., et al.: Segmentation and classification of cervical cells using deep learning. IEEE Access 7, 116925–116941 (2019). https://doi.org/10.1109/ACCESS.2019.2936017

  30. Lee, J.: Segmentation of coronary calcified plaque in intravascular OCT images using a two-step deep learning approach. IEEE Access 8, 225581–225593 (2020)

    Google Scholar 

  31. Nida, N., Irtaza, A., Javed, A., Yousaf, M.H., Mahmood, M.T.: Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. Int. J. Med. Inform. 124, 37–48 (2019)

    Article  Google Scholar 

  32. Khan, T.M., Alhussein, M., Aurangzeb, K., Arsalan, M., Naqvi, S.S., Nawaz, S.J.: Residual connection-based encoder decoder network (RCED-Net) for retinal vessel segmentation. IEEE Access 8, 131257–131272 (2020). https://doi.org/10.1109/ACCESS.2020.3008899

    Article  Google Scholar 

  33. Veena, H.: A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images. J. King Saud Univ. (2021)

    Google Scholar 

  34. Vaishnavi, J.: An efficient adaptive histogram based segmentation and extraction model for the classification of severities on diabetic retinopathy. Multimedia Tools Appl. 79, 30439–30452 (2020)

    Google Scholar 

  35. Lu, S., Wang, S.-H., Zhang, Y.-D.: Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm. Neural Comput. Appl. 33(17), 10799–10811 (2020). https://doi.org/10.1007/s00521-020-05082-4

    Article  Google Scholar 

  36. Chen, J.: Medical image segmentation and reconstruction of prostate tumor based on 3D AlexNet. Comput. Methods Programs Biomed. 200, 105878 (2021)

    Google Scholar 

  37. Mansour, R.F.: Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomed. Eng. Lett. 8, 41–57 (2018)

    Google Scholar 

  38. He, K., Zhang, X.: Deep residual learning for image recognition. arXiv (2015)

    Google Scholar 

  39. Jeevakala, S., Sreelakshmi, C., Ram, K., Rangasami, R., Sivaprakasam, M.: Artificial intelligence in detection and segmentation of internal auditory canal and its nerves using deep learning techniques. Int. J. Comput. Assist. Radiol. Surg. 15(11), 1859–1867 (2020). https://doi.org/10.1007/s11548-020-02237-5

    Article  Google Scholar 

  40. Guo, S., Wang, K., Kang, H., Zhang, Y., Gao, Y., Li, T.: BTS-DSN: deeply supervised neural network with short connections for retinal vessel segmentation. Int. J. Med. Inform. 126, 105–113 (2019)

    Article  Google Scholar 

  41. Zhao, X.: EBioMedicine (2020)

    Google Scholar 

  42. Liu, Y.: Automatic segmentation of cervical nuclei based on deep learning and a conditional random field. IEEE Access 6, 53709–53721 (2018)

    Google Scholar 

  43. Ding, L.: A lightweight U-Net architecture multi-scale convolutional network for pediatric hand bone segmentation in X-ray image. IEEE Access 7, 68436–68445 (2019)

    Google Scholar 

  44. Pan, X.: A fundus retinal vessels segmentation scheme based on the improved deep learning U-Net model. IEEE Access 7, 122634–122643 (2019)

    Google Scholar 

  45. Jiang, Z., Ou, C., Qian, Y., Rehan, R., Yong, A.: Coronary vessel segmentation using multiresolution and multiscale deep learning. Inform. Med. Unlocked 24, 100602 (2021)

    Google Scholar 

  46. Xiong, Z., Fedorov, V.V., Fu, X., Cheng, E., Macleod, R., Zhao, J.: Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network. IEEE Trans. Med Imaging 38(2), 515–524 (2019). https://doi.org/10.1109/TMI.2018.2866845

    Article  Google Scholar 

  47. Han, S.Y., Kwon, H.J., Kim, Y., Cho, N.I.: Noise-robust pupil center detection through CNN-based segmentation with shape-prior loss. IEEE Access 8, 64739–64749 (2020). https://doi.org/10.1109/ACCESS.2020.2985095

    Article  Google Scholar 

  48. Daoud, B., Morooka, K., Kurazume, R., Leila, F., Mnejja, W., Daoud, J.: 3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning. Comput. Med. Imaging Graph. 77, 101644 (2019)

    Google Scholar 

  49. Alsaih, K., Yusoff, M.Z., Faye, I., Tang, T.B., Meriaudeau, F.: Retinal fluid segmentation using ensembled 2-dimensionally and 2.5-dimensionally deep learning networks. IEEE Access 8, 152452–152464 (2020). https://doi.org/10.1109/ACCESS.2020.3017449

    Article  Google Scholar 

  50. Mangipudi, P.S., Pandey, H.M., Choudhary, A.: Improved optic disc and cup segmentation in Glaucomatic images using deep learning architecture. Multimedia Tools Appl. 80(20), 30143–30163 (2021). https://doi.org/10.1007/s11042-020-10430-6

    Article  Google Scholar 

  51. Bhatkalkar, B.J., Reddy, D.R., Prabhu, S., Bhandary, S.V.: Improving the performance of convolutional neural network for the segmentation of optic disc in fundus images using attention gates and conditional random fields. IEEE Access 8, 29299–29310 (2020). https://doi.org/10.1109/ACCESS.2020.2972318

    Article  Google Scholar 

  52. Sardar, M., Banerjee, S., Mitra, S.: Iris segmentation using interactive deep learning. IEEE Access 8, 219322–219330 (2020). https://doi.org/10.1109/ACCESS.2020.3041519

    Article  Google Scholar 

  53. Lu, Y.: Automatic tumor segmentation by means of deep convolutional U-Net with pre-trained encoder in PET images. IEEE Access 8, 113636–113648 (2020)

    Google Scholar 

  54. Lu, Y., Lin, J., Chen, S., He, H., Cai, Y.: Automatic tumor segmentation by means of deep convolutional U-Net with pre-trained encoder in PET images. IEEE Access 8, 113636–113648 (2020). https://doi.org/10.1109/ACCESS.2020.3003138

    Article  Google Scholar 

  55. Ali, M., Gilani, S.O., Waris, A., Zafar, K., Jamil, M.: Brain tumour image segmentation using deep networks. IEEE Access 8, 153589–153598 (2020). https://doi.org/10.1109/ACCESS.2020.3018160

    Article  Google Scholar 

  56. Naser, M.A., Jamal Deen, M.: Brain tumour segmentation and grading of lower-grade glioma using deep learning in MRI images. Comput. Biol. Med. 121, 103758 (2020)

    Google Scholar 

  57. Tran, S.-T.: A multiple layer U-Net, Un-Net, for liver and liver tumor segmentation in CT. IEEE Access 9, 3752–3764 (2020)

    Google Scholar 

  58. Zhang, Z., Li, J., Tian, C., Zhong, Z., Jiao, Z., Gao, X.: Quality-driven deep active learning method for 3D brain MRI segmentation. Neurocomputing 446, 106–117 (2021)

    Article  Google Scholar 

  59. Lei, T., Wang, R., Zhang, Y., Wan, Y., Liu, C., Nandi, A.K.: DefED-Net: deformable encoder-decoder network for liver and liver tumor segmentation. IEEE Trans. Radiat. Plasma Med. Sci. (2021). https://doi.org/10.1109/TRPMS.2021.3059780

  60. Gegundez-Arias, M.E., Marin-Santos, D., Perez-Borrero, I., Vasallo-Vazquez, M.J.: A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model. Comput. Methods Programs Biomed. 205, 106081 (2021)

    Google Scholar 

  61. Boudegga, H., Elloumi, Y., Akil, M., Bedoui, M.H., Kachouri, R., Abdallah, A.B.: Fast and efficient retinal blood vessel segmentation method based on deep learning network. Comput. Med. Imaging Graph. 90, 101902 (2021)

    Google Scholar 

  62. Gurpreet, S., et al.: Deep learning based automatic segmentation of cardiac computed tomography. J. Am. Coll. Cardiol. 73, 1643–1643 (2019)

    Google Scholar 

  63. Xiao, C., Li, Y., Jiang, Y.: Heart coronary artery segmentation and disease risk warning based on a deep learning algorithm. IEEE Access 8, 140108–140121 (2020). https://doi.org/10.1109/ACCESS.2020.3010800

    Article  Google Scholar 

  64. Baskaran, L., et al.: Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning (2020). https://doi.org/10.1371/journal.pone.0232573

  65. Lu, L., Jian, L., Luo, J., Xiao, B.: Pancreatic segmentation via ringed residual U-Net. IEEE Access 7, 172871–172878 (2019). https://doi.org/10.1109/ACCESS.2019.2956550

    Article  Google Scholar 

  66. Liu, T., Tian, Y., Zhao, S., Huang, X., Wang, Q.: Residual convolutional neural network for cardiac image segmentation and heart disease diagnosis. IEEE Access 8, 82153–82161 (2020). https://doi.org/10.1109/ACCESS.2020.2991424

    Article  Google Scholar 

  67. Van De Leemput, S.C., Meijs, M., Patel, A., Meijer, F.J.A., Van Ginneken, B., Manniesing, R.: Multiclass brain tissue segmentation in 4D CT using convolutional neural networks. IEEE Access 7, 51557–51569 (2019). https://doi.org/10.1109/ACCESS.2019.2910348

    Article  Google Scholar 

  68. Yamanakkanavar, N., Lee, B.: Using a patch-wise M-Net convolutional neural network for tissue segmentation in brain MRI images. IEEE Access 8, 120946–120958 (2020). https://doi.org/10.1109/ACCESS.2020.3006317

    Article  Google Scholar 

  69. Zhang, F., et al.: Deep learning based segmentation of brain tissue from diffusion MRI. Neuroimage 233, 117934 (2021)

    Google Scholar 

  70. Jonmohamadi, Y.: Automatic segmentation of multiple structures in knee arthroscopy using deep learning. IEEE Access 8, 51853–51861 (2020)

    Google Scholar 

  71. Hariyani, Y.S., Eom, H., Park, C.: DA-CapNet: dual attention deep learning based on U-Net for nailfold capillary segmentation. IEEE Access 8, 10543–10553 (2020). https://doi.org/10.1109/ACCESS.2020.2965651

  72. Chen, S.: U-Net plus: deep semantic segmentation for esophagus and esophageal cancer in computed tomography images. IEEE Access 7, 82867–82877 (2019)

    Google Scholar 

  73. Li, S.: Attention dense-U-net for automatic breast mass segmentation in digital mammogram. IEEE Access 7, 59037–59047 (2019)

    Google Scholar 

  74. Stenman, S., et al.: Antibody supervised training of a deep learning based algorithm for leukocyte segmentation in papillary thyroid carcinoma. IEEE J. Biomed. Health Inform. 25(2), 422–428 (2021). https://doi.org/10.1109/JBHI.2020.2994970

    Article  Google Scholar 

  75. Lal, S., Das, D., Alabhya, K., Kanfade, A., Kumar, A., Kini, J.: NucleiSegNet: robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images. Comput. Biol. Med. 128, 104075 (2021)

    Article  Google Scholar 

  76. Gonzalez, Y., et al.: Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach. Med. Image Anal. 68, 101896 (2021)

    Google Scholar 

  77. Li, X., Wang, Y., Tang, Q., Fan, Z., Yu, J.: Dual U-Net for the segmentation of overlapping glioma nuclei. IEEE Access 7, 84040–84052 (2019). https://doi.org/10.1109/ACCESS.2019.2924744

    Article  Google Scholar 

  78. Cheng, J., Tian, S., Yu, L., Ma, X., Xing, Y.: A deep learning algorithm using contrast-enhanced computed tomography (CT) images for segmentation and rapid automatic detection of aortic dissection. Biomed. Signal Process. Control 62, 102145 (2020)

    Google Scholar 

  79. Huang, C., Ding, H., Liu, C.: Segmentation of cell images based on improved deep learning approach. IEEE Access 8, 110189–110202 (2020). https://doi.org/10.1109/ACCESS.2020.3001571

    Article  Google Scholar 

  80. Zheng, B., et al.: MSD-Net: multi-scale discriminative network for COVID-19 lung infection segmentation on CT. IEEE Access 8, 185786–185795 (2020). https://doi.org/10.1109/ACCESS.2020.3027738

  81. Amyar, A., Modzelewski, R., Li, H., Ruan, S.: Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: classification and segmentation. Comput. Biol. Med 126, 104037 (2020). https://doi.org/10.1016/j.compbiomed.2020.104037

    Article  Google Scholar 

  82. Jayapandian, C.P., Chen, Y., Janowczyk, A.R., Palmer, M.B.: Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains. Kidney Int. 99(1), 86–101 (2021)

    Google Scholar 

  83. Wang, D., Zhang, T., Li, M., Bueno, R., Jayender, J.: 3D deep learning based classification of pulmonary ground glass opacity nodules with automatic segmentation. Comput. Med. Imaging Graph. 88, 101814 (2021)

    Google Scholar 

  84. Pham, V.-T., Tran, T.-T., Wang, P.-C., Chen, P.-Y., Lo, M.-T.: EAR-UNet: a deep learning-based approach for segmentation of tympanic membranes from otoscopic images. Artif. Intell. Med. 115, 102065 (2021)

    Google Scholar 

  85. Zhang, Q.: Automatic epicardial fat segmentation and quantification of CT scans using dual U-Nets with a morphological processing layer. IEEE Access 8, 128032–128041 (2020)

    Google Scholar 

  86. Zhang, Q., Zhou, J., Zhang, B., Jia, W., Wu, E.: Automatic epicardial fat segmentation and quantification of CT scans using dual U-nets with a morphological processing layer. IEEE Access 8, 128032–128041 (2020). https://doi.org/10.1109/ACCESS.2020.3008190

  87. Marzola, F., van Alfen, N., Doorduin, J., Meiburger, K.M.: Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment. Comput. Biol. Med. 135, 104623 (2021)

    Google Scholar 

  88. Ding, L., Zhao, K., Zhang, X., Wang, X., Zhang, J.: A lightweight U-Net architecture multi-scale convolutional network for pediatric hand bone segmentation in X-ray image. IEEE Access 7, 68436–68445 (2019). https://doi.org/10.1109/ACCESS.2019.2918205

  89. Ding, Y.: A stacked multi-connection simple reducing net for brain tumor segmentation. IEEE Access 7, 104011–104024 (2019)

    Google Scholar 

  90. Civit-Masot, J., Luna-Perejón, F., Vicente-Díaz, S., Rodríguez Corral, J.M., Civit, A.: TPU cloud-based generalized U-Net for eye fundus image segmentation. IEEE Access 7,142379–142387 (2019). https://doi.org/10.1109/ACCESS.2019.2944692

  91. Rahman, T., et al.: Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization. IEEE Access 8, 191586–191601 (2020). https://doi.org/10.1109/ACCESS.2020.3031384

  92. Zeng, G., et al.: MRI-based 3D models of the hip joint enables radiation-free computer-assisted planning of periacetabular osteotomy for treatment of hip dysplasia using deep learning for automatic segmentation. Eur. J. Radiol. Open 8, 100303 (2020). https://doi.org/10.1016/j.ejro.2020.100303

  93. Al-Kofahi, Y.: A deep learning-based algorithm for 2-D cell segmentation in microscopy images . BMC Inform. 19, 1–11 (2018)

    Google Scholar 

  94. Milletari, F.: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultra-sound. Comput. Vis. Image Underst. 164, 92–102 (2017)

    Google Scholar 

  95. Milletari, F., et al.: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultra-sound Comput. Vis. Image Underst. 164, 92–102 (2017)

    Google Scholar 

  96. Gibson, E.: Automatic multi-organ segmentation on abdominal CT with dense V-networks. IEEE Trans. Medi. Imaging. IEEE Trans. Med. Imaging, 37(8), 1822–1834 (2018)

    Google Scholar 

  97. Zeng, Y., Tsui, P.-H., Wu, W., Zhou, Z., Wu, S.: Fetal ultrasound image segmentation for automatic head circumference biometry using deeply supervised attention-gated V-Net. J. Digit. Imaging 34(1), 134–148 (2021). https://doi.org/10.1007/s10278-020-00410-5

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India

    H. Heartlin Maria, A. Maria Jossy & S. Malarvizhi

Authors
  1. H. Heartlin Maria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Maria Jossy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Malarvizhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to H. Heartlin Maria .

Editor information

Editors and Affiliations

  1. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Lekshmi Kalinathan

  2. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Priyadharsini R.

  3. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Madheswari Kanmani

  4. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Manisha S.

Rights and permissions

Reprints and permissions

Copyright information

© 2022 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Maria, H.H., Jossy, A.M., Malarvizhi, S. (2022). Perspective Review on Deep Learning Models to Medical Image Segmentation. In: Kalinathan, L., R., P., Kanmani, M., S., M. (eds) Computational Intelligence in Data Science. ICCIDS 2022. IFIP Advances in Information and Communication Technology, vol 654. Springer, Cham. https://doi.org/10.1007/978-3-031-16364-7_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16364-7_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16363-0

  • Online ISBN: 978-3-031-16364-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation