Semantic Segmentation of Medical Images with Deep Learning: Overview
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Published:
Jul 20, 2023
Keywords:
Semantic segmentation, Deep Learning, Medical images, Segmentation.
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Abstract
Semantic segmentation is one of the biggest challenging tasks in computer vision, especially in medical image analysis, it helps to locate and identify pathological structures automatically. It is an active research area. Continuously different techniques are proposed. Recently Deep Learning is the latest technique used intensively to improve the performance in medical image segmentation. For this reason, we present in this non-systematic review a preliminary description about semantic segmentation with deep learning and the most important steps to build a model that deal with this problem.
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Semantic Segmentation of Medical Images with Deep Learning: Overview. (2023). Medical Technologies Journal, 4(3), 568-575. https://doi.org/10.26415/2572-004X-vol4iss3p568-575
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Medical technologies
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Semantic Segmentation of Medical Images with Deep Learning: Overview. (2023). Medical Technologies Journal, 4(3), 568-575. https://doi.org/10.26415/2572-004X-vol4iss3p568-575
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References
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[2] Lee, JuneGoo; Sanghoom Jun, Young-Won Cho,Hyunna Lee,Guk Bae Kim, Joon Beom Seo,NamKug Kim, «Deep learning in medical imaging : general overview,» Koreanjournal of radiology, 2017.https://doi.org/10.3348/kjr.2017.18.4.570 PMid:28670152 PMCid:PMC5447633
[3] Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi,, «A Survey on Deep Learning in Medical Image Analysis,» Medical Image Analysis, vol. vol, n° %142, pp. 60-88, Décembre 2017. https://doi.org/10.1016/j.media.2017.07.005 PMid:28778026
[4] Baris Kayalibay, Grady Jensen, Patrick van der Smagt, «CNN-based Segmentation of Medical Imaging Data,» arXiv:1701.03056v2 [cs.CV], 25 Jul 2017.
[5] Zhao X, Wu Y, Song G, Li Z, Zhang Y, Fan Y,, «A deep learning model integrating FCNNs and CRFs for brain tumor segmentation,» Medical Image Analysis, vol. vol, n° %143, pp. 98-111, January 2018. https://doi.org/10.1016/j.media.2017.10.002 PMid:29040911 PMCid:PMC6029627
[6] Patrick Ferdinand Christ, Florian Ettlinger, Felix Grün, Mohamed Ezzeldin A. Elshaera, Jana Lipkova, Sebastian Schlecht, Freba Ahmaddy, Sunil Tatavarty, Marc Bickel, Patrick Bilic, Markus Rempfler, Felix Hofmann, Melvin D Anastasi, Seyed-Ahmad Ahmadi, Geo, «Automatic Liver and Tumor Segmentation of CT and MRI Volumes Using Cascaded Fully Convolutional Neural Networks,» arXiv:1702.05970v2, 2017.
[7] Kermi A., Mahmoudi I., Khadir M.T., «Deep Convolutional Neural Networks Using U-Net for Automatic Brain Tumor Segmentation in Multimodal MRI Volumes,» In: Crimi A., Bakas S., Kuijf H., Keyvan F., Reyes M., van Walsum T. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science, vol. vol, n° %111384 Springer, Cham, 2019. https://doi.org/10.1007/978-3-030-11726-9_4
[8] Weninger L., Rippel O., Koppers S., Merhof D., «Segmentation of Brain Tumors and Patient Survival Prediction: Methods for the BraTS 2018 Challenge,» In: Crimi A., Bakas S., Kuijf H., Keyvan F., Reyes M., van Walsum T. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science, Vols. %1 sur %2vol 11384. Springer, Cham, 2019. https://doi.org/10.1007/978-3-030-11726-9_1
[9] Marcinkiewicz M., Nalepa J., Lorenzo P.R., Dudzik W., Mrukwa G,, «Segmenting Brain Tumors from MRI Using Cascaded Multi-modal U-Nets,» In: Crimi A., Bakas S., Kuijf H., Keyvan F., Reyes M., van Walsum T. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in C, Vols. %1 sur %2vol 11384. Springer, Cham. https://doi.org/10.1007/978-3-030-11726-9_2
[10] S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, et al.,, «"Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge",,» arXiv preprint arXiv:1.
[11] B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al., «"The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)",» , IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI: 10.1109/TMI.2014.2377694. https://doi.org/10.1109/TMI.2014.2377694 PMid:25494501 PMCid:PMC4833122
[12] Ronneberger, O.; Fischer, P.; and T. Brox, «U-net: Convolutional networks for biomedical image segmentation,» arXiv:1505.04597v1 [cs.CV], 2015. https://doi.org/10.1007/978-3-319-24574-4_28
[13] Zhang, Hejia; Zhu, Xia; Willke, Theodore L., «Segmenting Brain Tumors with Symmetry,» arXiv:1711.06636v1[cs.CV], 2017.
[14] Saddam Hussain, Syed Muhammad Anwar, Muhammad Majid,, «Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural,» arXiv, 1 Aug 2017. https://doi.org/10.1016/j.neucom.2017.12.032
[15] Salma Alqazzaz, Xianfang Sun, Xin Yang, Len Nokes1, «Automated brain tumor segmentation on multi-modal MR image using SegNet».
[16] Ali Isin a, Cem Direkoglu,Melike sah,, «Review of mri-based brain tumor image segmentation using deep learning methods,,» Elsevier, , 30 august 2016. https://doi.org/10.1016/j.procs.2016.09.407
[17] Sanghoom Jun,Young-Won Cho,Hyunna Lee,Guk Bae Kim,Joon Beom Seo,NamKug, «Deep learning in medical imaging : general overview.,» Koreanjournal, 18/04/2017. https://doi.org/10.3348/kjr.2017.18.4.570 PMid:28670152 PMCid:PMC5447633
[18] van der Laak,Bram van Ginneken,Clara I,Sanchez Mohsen ghafoorian,Jeroen, «A survey on deep learning in medical image analysis.,» Elsevier, 2017.
[19] A. Garcia-Garcia, S. Orts-Escolano, S.O. Oprea, V. Villena-Martinez, and J. Garcia-Rodriguez, «A Review on Deep Learning Techniques Applied to Semantic Segmentation,» arXiv:1704.06857v1 [cs.CV] , 22 Apr 2017.
[20] Holger R. ROTH,Chen SHEN, Hirohisa ODA,Masahiro ODA,Yuichiro HAYASHI,Kazunari MISAWA,Kensaku MORI, «Deep learning and its application to medical image segmentation,» arXiv:1803.08691v1 [cs.CV] , 23 Mar 2018.