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

Brain hemorrhage is one of the leading causes of death due to the sudden rupture of a blood vessel in the brain, resulting in bleeding in the brain parenchyma. The early detection and segmentation of brain damage are extremely important for prompt treatment.

Some previous studies focused on localizing cerebral hemorrhage based on bounding boxes without specifying specific damage regions. However, in practice, doctors need to detect and segment the hemorrhage area more accurately. In this paper, we propose a method for automatic brain hemorrhage detection and segmentation using the proposed network models, which are improved from the U-Net by changing its backbone with typical feature extraction networks, , DenseNet-121, ResNet-50, and MobileNet-V2. The U-Net architecture has many outstanding advantages.

It does not need to do too many preprocessing techniques on the original images and it can be trained with a small dataset providing low error segmentation in medical images. We use the transfer learning approach with the head CT dataset gathered on Kaggle including two classes, bleeding and non-bleeding.

Besides, we give some comparison results between the proposed models and the previous works to provide an overview of the suitable model for cerebral CT images. On the head CT dataset, our proposed models achieve a segmentation accuracy of up to 99%.

© 2024 The Author(s). Published by Bentham Open.
© 2024 The Author(s). Published by Bentham Open. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2024-01-01
2024-11-23

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References

  1. BroderickJ. ConnollyS. FeldmannE. HanleyD. KaseC. KriegerD. MaybergM. MorgensternL. OgilvyC.S. VespaP. ZuccarelloM. American Heart Association American Stroke Association Stroke Council High Blood Pressure Research Council Quality of Care and Outcomes in Research Interdisciplinary Working Group Guidelines for the management of spontaneous intracerebral hemorrhage in adults.Stroke20073862001202310.1161/STROKEAHA.107.18368917478736
     [Google Scholar]
  2. AguilarMI FreemanWD Spontaneous intracerebral hemorrhage.Semin. Nephrol.201030555556410.1055/s‑0030‑1268865
     [Google Scholar]
  3. van AschC.J.J. LuitseM.J.A. RinkelG.J.E. van der TweelI. AlgraA. KlijnC.J.M. Incidence, case fatality, and functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: A systematic review and meta-analysis.Lancet Neurol.20109216717610.1016/S1474‑4422(09)70340‑020056489
     [Google Scholar]
  4. ElliottJ. SmithM. The acute management of intracerebral hemorrhage: A clinical review.Anesth. Analg.201011051419142710.1213/ANE.0b013e3181d568c820332192
     [Google Scholar]
  5. CurrieS. SaleemN. StraitonJ.A. Macmullen-PriceJ. WarrenD.J. CravenI.J. Imaging assessment of traumatic brain injury.Postgrad. Med. J.2016921083415010.1136/postgradmedj‑2014‑13321126621823
     [Google Scholar]
  6. XueZ. AntaniS. LongL.R. Demner-FushmanD. ThomaG.R. Window classification of brain CT images in biomedical articles.AMIA Annu Symp Proc2012201210231029
     [Google Scholar]
  7. WuY. SupanichM.P. DengJ. Ensembled deep neural network for intracranial hemorrhage detection and subtype classification on noncontrast CT images.Artif Intell Med202121-2122010.2991/jaims.d.210618.001
     [Google Scholar]
  8. BurdujaM. IonescuR.T. VergaN. Accurate and efficient intracranial hemorrhage detection and subtype classification in 3D CT scans with convolutional and long short-term memory neural networks.Sensors20202019561110.3390/s2019561133019508
     [Google Scholar]
  9. DanilovG. KotikK. NegreevaA. TsukanovaT. ShifrinM. ZakharovaN. BatalovA. ProninI. PotapovA. Classification of intracranial hemorrhage subtypes using deep learning on CT scans.Stud. Health Technol. Inform.202027237037310.3233/SHTI20057232604679
     [Google Scholar]
  10. SageA. BaduraP. Intracranial hemorrhage detection in head CT using double-branch convolutional neural network, support vector machine, and random forest.Appl. Sci.20201021757710.3390/app10217577
     [Google Scholar]
  11. CastroJ.S. ChabertS. SaavedraC. SalasR. Convolutional neural networks for detection intracranial hemorrhage in CT images.CRoNe.201925643743
     [Google Scholar]
  12. HeJ. Automated detection of intracranial hemorrhage on head computed tomography with deep learning.ICBET 2020: Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology25-28 March, Tokyo, Japan, 2020, pp. 117-121.10.1145/3397391.3397436
     [Google Scholar]
  13. SanthoshkumarS. VaradarajanV. GavaskarS. AmalrajJ.J. SumathiA. Machine learning model for intracranial hemorrhage diagnosis and classification.Electronics20211021257410.3390/electronics10212574
     [Google Scholar]
  14. MengF. WangJ. ZhangH. LiW. Artificial intelligence-enabled medical analysis for intracranial cerebral hemorrhage detection and classification.J. Healthc. Eng.2022202211310.1155/2022/201722335356628
     [Google Scholar]
  15. ChangP.D. KuoyE. GrinbandJ. WeinbergB.D. ThompsonM. HomoR. ChenJ. AbcedeH. ShafieM. SugrueL. FilippiC.G. SuM.Y. YuW. HessC. ChowD. Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT.AJNR Am. J. Neuroradiol.20183991609161610.3174/ajnr.A574230049723
     [Google Scholar]
  16. ReisE.P. NascimentoF. AranhaM. SecolF.M. MachadoB. FelixM. SteinA. AmaroE. Brain Hemorrhage Extended (BHX): Bounding box extrapolation from thick to thin slice CT images.PhysioNet.202010123e215e220
     [Google Scholar]
  17. NemcekJ. VicarT. JakubicekR Weakly supervised deep learning-based intracranial hemorrhage localization.arXiv:2105.007812021
     [Google Scholar]
  18. ErtuğrulÖ.F. AkılM.F. Detecting hemorrhage types and bounding box of hemorrhage by deep learning.Biomed. Signal Process. Control20227110308510.1016/j.bspc.2021.103085
     [Google Scholar]
  19. PhanA.C. TranH.D. PhanT.C. Efficient brain hemorrhage detection on 3D CT scans with deep neural network. , FDSE 2021, Virtual Event, November 24–26, 2021.Future Data and Security Engineering: 8th International ConferenceVirtual Event, November 24–26, 2021, pp. 81-96.
     [Google Scholar]
  20. ChenJ. LuY. YuQ. LuoX. AdeliE. WangY. LuL. YuilleA.L. ZhouY Transunet: Transformers make strong encoders for medical image segmentationarXiv:2102.043062021
     [Google Scholar]
  21. WangW. ChenC. DingM. YuH. ZhaS. LiJ. Transbts: Multimodal brain tumor segmentation using transformer.Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conferencetrasbourg, France, September 27–October 1, 2021, pp. 109-119
     [Google Scholar]
  22. WuJ. FangH. ZhangY. YangY. XuY Medsegdiff: Medical image segmentation with diffusion probabilistic model.arXiv:2211.006112022
     [Google Scholar]
  23. SharmaN. SharmaR. JindalN. Machine learning and deep learning applications-a vision.Glob. Transit. Proc.202121242810.1016/j.gltp.2021.01.004
     [Google Scholar]
  24. RonnebergerO. FischerP. BroxT. U-net: Convolutional networks for biomedical image segmentation.Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International ConferenceOctober 5-9, Munich, Germany, 2015, pp. 234-241.
     [Google Scholar]
  25. ÇiçekÖ. AbdulkadirA. LienkampS.S. BroxT. RonnebergerO. 3D U-Net: Learning dense volumetric segmentation from sparse annotation.Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International ConferenceOctober 17-21, Athens, Greece, 2016, pp. 424-434.
     [Google Scholar]
  26. HssayeniM.D. CroockM.S. SalmanA.D. Al-khafajiH.F. YahyaZ.A. GhoraaniB. Intracranial hemorrhage segmentation using a deep convolutional model.Data2020511410.3390/data5010014
     [Google Scholar]
  27. FalkT. MaiD. BenschR. ÇiçekÖ. AbdulkadirA. MarrakchiY. BöhmA. DeubnerJ. JäckelZ. SeiwaldK. DovzhenkoA. TietzO. Dal BoscoC. WalshS. SaltukogluD. TayT.L. PrinzM. PalmeK. SimonsM. DiesterI. BroxT. RonnebergerO. U-Net: Deep learning for cell counting, detection, and morphometry.Nat. Methods2019161677010.1038/s41592‑018‑0261‑230559429
     [Google Scholar]
  28. KohlS. Romera-ParedesB. MeyerC. De FauwJ. LedsamJ.R. Maier-HeinK. EslamiS.M. Jimenez RezendeD. RonnebergerO. A probabilistic u-net for segmentation of ambiguous images.Adv. Neural Inf. Process. Syst.201831
     [Google Scholar]
  29. HeK. ZhangX. RenS. SunJ. Deep residual learning for image recognition.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Las Vegas, NV, USA, 2016, pp. 770-778.10.1109/CVPR.2016.90
     [Google Scholar]
  30. RahimzadehM AttarA. A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2.Inform. Med. Unlocked202019100360
     [Google Scholar]
  31. HuangG. LiuZ. Van Der MaatenL. WeinbergerK.Q. Densely connected convolutional networks.2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Honolulu, HI, USA, 2017, pp. 2261-2269.10.1109/CVPR.2017.243
     [Google Scholar]
  32. HowardA.G. ZhuM. ChenB. KalenichenkoD. WangW. WeyandT. AndreettoM. AdamH Mobilenets: Efficient convolutional neural networks for mobile vision applications.arXiv:1704.048612017
     [Google Scholar]
  33. SandlerM. HowardA. ZhuM. ZhmoginovA. ChenL.C. Mobilenetv2: Inverted residuals and linear bottlenecks.Proceedings of the IEEE conference on computer vision and pattern recognitionSalt Lake City, UT, USA, 2018, pp. 4510-4520.
     [Google Scholar]
  34. XiangQ. WangX. LiR. ZhangG. LaiJ. HuQ. Fruit image classification based on MobileNetV2 with transfer learning technique.Proceedings of the 3rd International Conference on Computer Science and Application EngineeringSanya, China, 2019, pp. 1-710.1145/3331453.3361658
     [Google Scholar]
  35. DaiW. DaiY. HirotaK. JiaZ. A flower classification approach with mobileNetV2 and transfer learning.Proceedings of the 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020)Beijing, China, 2020.
     [Google Scholar]
  36. GomezR. Understanding categorical cross-entropy loss, binary cross-entropy loss, softmax loss, logistic loss, focal loss and all those confusing names.2018https://gombru.github.io/2018/ 05/23/cross_entropy_loss/
/content/journals/cmir/10.2174/1573405620666230915125635
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Automatic Detection and Segmentation of Brain Hemorrhage based on Improved U-Net Model
Curr. Med. Imaging 20, e150923221166 (2024); https://doi.org/10.2174/1573405620666230915125635
/content/journals/cmir/10.2174/1573405620666230915125635
/content/journals/cmir/10.2174/1573405620666230915125635
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  • Article Type:     Research Article
Keyword(s): Brain hemorrhage; Brain hemorrhage segmentation; DenseNet-121; MobileNet-V2; ResNet-50; U-Net

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