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2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Background

Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually.

Objective

This study proposes a new method called the enhanced regularized ensemble encoder-decoder network (EREEDN) for more accurate brain tumor segmentation.

Methods

The EREEDN model first preprocesses the MRI data by normalizing the intensity levels. It then uses a series of autoencoder networks to segment the tumor. These autoencoder networks are trained using back-propagation and gradient descent. To prevent overfitting, the EREEDN model also uses L2 regularization and dropout mechanisms.

Results

The EREEDN model was evaluated on the BraTS 2020 dataset. It achieved high performance on various metrics, including accuracy, sensitivity, specificity, and dice coefficient score. The EREEDN model outperformed other methods on the BraTS 2020 dataset.

Conclusion

The EREEDN model is a promising new method for brain tumor segmentation. It is more accurate and efficient than previous methods. Future studies will focus on improving the performance of the EREEDN model on complex tumors.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-01-01
2025-07-15

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References

  1. IşınA. DirekoğluC. ŞahM. Review of MRI-based brain tumor image segmentation using deep learning methods.Procedia Comput. Sci.201610231732410.1016/j.procs.2016.09.407
     [Google Scholar]
  2. HarisM. GuptaR.K. SinghA. HusainN. HusainM. PandeyC.M. SrivastavaC. BehariS. RathoreR.K.S. Differentiation of infective from neoplastic brain lesions by dynamic contrast-enhanced MRI.Neuroradiology200850653154010.1007/s00234‑008‑0378‑618379766
     [Google Scholar]
  3. SautO. LagaertJ.B. ColinT. Fathallah-ShaykhH.M. A multilayer grow-or-go model for GBM: effects of invasive cells and anti-angiogenesis on growth.Bull. Math. Biol.20147692306233310.1007/s11538‑014‑0007‑y25149139
     [Google Scholar]
  4. Jin Liu Min Li Jianxin Wang Fangxiang Wu Tianming Liu Yi Pan A survey of MRI-based brain tumor segmentation methods.Tsinghua Sci. Technol.201419657859510.1109/TST.2014.6961028
     [Google Scholar]
  5. GonzalezR. C. Digital image processing.Google Book2009
     [Google Scholar]
  6. MenzeB.H. JakabA. BauerS. Kalpathy-CramerJ. FarahaniK. KirbyJ. BurrenY. PorzN. SlotboomJ. WiestR. LancziL. GerstnerE. WeberM.A. ArbelT. AvantsB.B. AyacheN. BuendiaP. CollinsD.L. CordierN. CorsoJ.J. CriminisiA. DasT. DelingetteH. DemiralpC. DurstC.R. DojatM. DoyleS. FestaJ. ForbesF. GeremiaE. GlockerB. GollandP. GuoX. HamamciA. IftekharuddinK.M. JenaR. JohnN.M. KonukogluE. LashkariD. MarizJ.A. MeierR. PereiraS. PrecupD. PriceS.J. RavivT.R. RezaS.M.S. RyanM. SarikayaD. SchwartzL. ShinH.C. ShottonJ. SilvaC.A. SousaN. SubbannaN.K. SzekelyG. TaylorT.J. ThomasO.M. TustisonN.J. UnalG. VasseurF. WintermarkM. YeD.H. ZhaoL. ZhaoB. ZikicD. PrastawaM. ReyesM. Van LeemputK. The multimodal brain tumor image segmentation benchmark (BRATS).IEEE Trans. Med. Imaging201534101993202410.1109/TMI.2014.237769425494501
     [Google Scholar]
  7. MohanG. SubashiniM.M. MRI based medical image analysis: Survey on brain tumor grade classification.Biomed. Signal Process. Control20183913916110.1016/j.bspc.2017.07.007
     [Google Scholar]
  8. DeviC.N. ChandrasekharanA. SundararamanV.K. AlexZ.C. Neonatal brain MRI segmentation: A review.Comput. Biol. Med.20156416317810.1016/j.compbiomed.2015.06.01626189155
     [Google Scholar]
  9. LitjensG. KooiT. BejnordiB.E. SetioA.A.A. CiompiF. GhafoorianM. van der LaakJ.A.W.M. van GinnekenB. SánchezC.I. A survey on deep learning in medical image analysis.Med. Image Anal.201742608810.1016/j.media.2017.07.00528778026
     [Google Scholar]
  10. HavaeiM. DavyA. Warde-FarleyD. BiardA. CourvilleA. BengioY. PalC. JodoinP.M. LarochelleH. Brain tumor segmentation with deep neural networks.Med. Image Anal.201735183110.1016/j.media.2016.05.00427310171
     [Google Scholar]
  11. LeaderJ.K. ZhengB. RogersR.M. SciurbaF.C. PerezA. ChapmanB.E. PatelS. FuhrmanC.R. GurD. Automated lung segmentation in X-ray computed tomography.Acad. Radiol.200310111224123610.1016/S1076‑6332(03)00380‑514626297
     [Google Scholar]
  12. TangH. WuE.X. MaQ.Y. GallagherD. PereraG.M. ZhuangT. MRI brain image segmentation by multi-resolution edge detection and region selection.Comput. Med. Imaging Graph.200024634935710.1016/S0895‑6111(00)00037‑911008183
     [Google Scholar]
  13. SulaimanS. Mat IsaN. Adaptive fuzzy-K-means clustering algorithm for image segmentation.IEEE Trans. Consum. Electron.20105642661266810.1109/TCE.2010.5681154
     [Google Scholar]
  14. RonnebergerO. FischerP. BroxT. U-net: Convolutional networks for biomedical image segmentation.18th International Conference.Munich, GermanyOctober 5-9201523424110.1007/978‑3‑319‑24574‑4_28
     [Google Scholar]
  15. MyronenkoA. 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II.Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries.Springer2018
     [Google Scholar]
  16. McKinleyR. MeierR. WiestR. Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II.Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries.Springer201845665
     [Google Scholar]
  17. HuJ. ShenL. SunG. Squeeze-and-excitation networksIEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City, UT, USA18-23 June201871327141
     [Google Scholar]
  18. RoyA.G. NavabN. WachingerC. Concurrent spatial and channel ‘squeeze and excitation’in fully convolutional networks.21st International Conference.Granada, SpainSep 16-202018421429
     [Google Scholar]
  19. GravesA. MohamedA. HintonG. Speech recognition with deep recurrent neural networks.IEEE International Conference on Acoustics, Speech and Signal Processing.Vancouver, BC, Canada26-31 May2013201310.1109/ICASSP.2013.6638947
     [Google Scholar]
  20. StollengaM.F. ByeonW. LiwickiM. SchmidhuberJ. Parallel multi-dimensional LSTM, with application to fast biomedical volumetric image segmentation.Adv. Neural Inf. Process. Syst.201528
     [Google Scholar]
  21. ComelliA. DahiyaN. StefanoA. BenfanteV. GentileG. AgneseV. RaffaG.M. PilatoM. YezziA. PetrucciG. PastaS. Deep learning approach for the segmentation of aneurysmal ascending aorta.Biomed. Eng. Lett.2021111152410.1007/s13534‑020‑00179‑033747600
     [Google Scholar]
  22. RehmanM.U. ChoS. KimJ. ChongK.T. Brainseg-net: Brain tumor mr image segmentation via enhanced encoder-decoder network.Diagnostics202111216910.3390/diagnostics1102016933504047
     [Google Scholar]
  23. YuanY. Automatic brain tumor segmentation with scale attention network.Juries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I 6.Springer2020
     [Google Scholar]
  24. NemaS. DudhaneA. MuralaS. NaiduS. RescueNet: An unpaired GAN for brain tumor segmentation.Biomed. Signal Process. Control20205510164110.1016/j.bspc.2019.101641
     [Google Scholar]
  25. ZhangW. YangG. HuangH. YangW. XuX. LiuY. LaiX. ME‐Net : Multi‐encoder net framework for brain tumor segmentation.Int. J. Imaging Syst. Technol.20213141834184810.1002/ima.22571
     [Google Scholar]
  26. PereiraS. MeierR. McKinleyR. WiestR. AlvesV. SilvaC.A. ReyesM. Enhancing interpretability of automatically extracted machine learning features: Application to a RBM-Random Forest system on brain lesion segmentation.Med. Image Anal.20184422824410.1016/j.media.2017.12.00929289703
     [Google Scholar]
  27. ChenG. LiQ. ShiF. RekikI. PanZ. RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields.Neuroimage202021111662011662010.1016/j.neuroimage.2020.11662032057997
     [Google Scholar]
  28. AmiriS. MahjoubM.A. RekikI. Bayesian network and structured random forest cooperative deep learning for automatic multi-label brain tumor segmentation.ICAART2018218319010.5220/0006629901830190
     [Google Scholar]
  29. YangH. HuangC. WangL. LuoX. An improved encoder–decoder network for ore image segmentation.IEEE Sens. J.20212110114691147510.1109/JSEN.2020.3016458
     [Google Scholar]
  30. ClarkT. WongA. HaiderM.A. KhalvatiF. Fully deep convolutional neural networks for segmentation of the prostate gland in diffusion-weighted MR images14th International Conference, ICIAR 2017Montreal, QC, CanadaJuly 5–720179710410.1007/978‑3‑319‑59876‑5_12
     [Google Scholar]
  31. MilletariF. NavabN. AhmadiS-A. V-net: Fully convolutional neural networks for volumetric medical image segmentation.Fourth International Conference on 3D Vision (3DV).Stanford, CA, USA25-28 Oct201610.1109/3DV.2016.79
     [Google Scholar]
  32. SandlerM. HowardA. ZhuM. ZhmoginovA. ChenL-C. Mobilenetv2: Inverted residuals and linear bottlenecksIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).201845104520
     [Google Scholar]
  33. HuaR. HuoQ. GaoY. SuiH. ZhangB. SunY. MoZ. ShiF. Segmenting brain tumor using cascaded V-Nets in multimodal MR images.Front. Comput. Neurosci.202014910.3389/fncom.2020.0000932116623
     [Google Scholar]
  34. RoyS. SahaR. SarkarS. MeheraR. PalR.K. BandyopadhyayS.K. Brain tumour segmentation using S-Net and SA-Net.IEEE Access202311286582867910.1109/ACCESS.2023.3257722
     [Google Scholar]
  35. AlqazzazS. SunX. YangX. NokesL. Automated brain tumor segmentation on multi-modal MR image using SegNet.Computat. Visual Media20195220921910.1007/s41095‑019‑0139‑y
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056275635231218114742
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Enhanced Regularized Ensemble Encoderdecoder Network for Accurate Brain Tumor Segmentation
Curr. Med. Imaging 20, e15734056275635 (2024); https://doi.org/10.2174/0115734056275635231218114742
/content/journals/cmir/10.2174/0115734056275635231218114742
/content/journals/cmir/10.2174/0115734056275635231218114742
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  • Article Type:     Research Article
Keyword(s): Autoencoder; Brain tumor; Computer vision; Medical imaging; MRI; Segmentation

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