Enhancing brain tumor segmentation in MRI images using the IC-net algorithm framework.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

Brain Neoplasms*/diagnostic imaging ; Brain Neoplasms*/pathology ; Magnetic Resonance Imaging*/methods ; Algorithms*; Humans ; Image Processing, Computer-Assisted/methods ; Image Interpretation, Computer-Assisted/methods ; Neural Networks, Computer

Brain tumors, often referred to as intracranial tumors, are abnormal tissue masses that arise from rapidly multiplying cells. During medical imaging, it is essential to separate brain tumors from healthy tissue. The goal of this paper is to improve the accuracy of separating tumorous regions from healthy tissues in medical imaging, specifically for brain tumors in MRI images which is difficult in the field of medical image analysis. In our research work, we propose IC-Net (Inverted-C), a novel semantic segmentation architecture that combines elements from various models to provide effective and precise results. The architecture includes Multi-Attention (MA) blocks, Feature Concatenation Networks (FCN), Attention-blocks which performs crucial tasks in improving brain tumor segmentation. MA-block aggregates multi-attention features to adapt to different tumor sizes and shapes. Attention-block is focusing on key regions, resulting in more effective segmentation in complex images. FCN-block captures diverse features, making the model more robust to various characteristics of brain tumor images. Our proposed architecture is used to accelerate the training process and also to address the challenges posed by the diverse nature of brain tumor images, ultimately leads to potentially improved segmentation performance. IC-Net significantly outperforms the typical U-Net architecture and other contemporary effective segmentation techniques. On the BraTS 2020 dataset, our IC-Net design obtained notable outcomes in Accuracy, Loss, Specificity, Sensitivity as 99.65, 0.0159, 99.44, 99.86 and DSC (core, whole, and enhancing tumors as 0.998717, 0.888930, 0.866183) respectively.
(© 2024. The Author(s).)

Diagnostics (Basel). 2023 May 04;13(9):. (PMID: 37175015)
Front Oncol. 2024 Jan 08;13:1247603. (PMID: 38260848)
Front Neuroinform. 2013 Nov 18;7:29. (PMID: 24302911)
Sensors (Basel). 2022 Aug 29;22(17):. (PMID: 36080958)
Quant Imaging Med Surg. 2022 Apr;12(4):2397-2415. (PMID: 35371952)
BMC Med Imaging. 2021 Jul 9;21(1):109. (PMID: 34243703)
Med Image Anal. 2017 Jan;35:18-31. (PMID: 27310171)
IEEE/ACM Trans Comput Biol Bioinform. 2023 May-Jun;20(3):1890-1904. (PMID: 36350865)
Front Public Health. 2023 Feb 02;11:1091850. (PMID: 36817919)
BMC Med Imaging. 2022 Jan 27;22(1):14. (PMID: 35086482)
IEEE Trans Med Imaging. 2022 Jun;41(6):1520-1532. (PMID: 35020590)
Sensors (Basel). 2022 Oct 26;22(21):. (PMID: 36365900)
BMC Med Imaging. 2022 Jan 5;22(1):6. (PMID: 34986785)
Front Oncol. 2021 Jun 04;11:690244. (PMID: 34150660)
J Healthc Eng. 2022 Apr 15;2022:4189781. (PMID: 35463660)
BMC Med Imaging. 2022 Nov 18;22(1):199. (PMID: 36401207)
IEEE Trans Med Imaging. 2016 May;35(5):1240-1251. (PMID: 26960222)
IEEE J Transl Eng Health Med. 2022 May 23;10:1800508. (PMID: 35774412)
Diagnostics (Basel). 2023 Aug 09;13(16):. (PMID: 37627893)
Biomed Res Int. 2022 Jun 6;2022:5164970. (PMID: 35707373)
Front Neurosci. 2019 Aug 16;13:810. (PMID: 31474816)
IEEE Rev Biomed Eng. 2020;13:156-168. (PMID: 31613783)
Front Oncol. 2022 Jun 01;12:873268. (PMID: 35719987)
IEEE Trans Neural Netw Learn Syst. 2023 Jun 20;PP:. (PMID: 37339028)
Healthcare (Basel). 2021 Aug 16;9(8):. (PMID: 34442188)

Date Created: 20240708 Date Completed: 20240708 Latest Revision: 20240729

20240729

PMC11231217

10.1038/s41598-024-66314-4

38977779