Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Medical Imaging
  4. Volume 20, Issue 1
  5. Article
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Background:

Accurate segmentation of liver tumor regions in medical images is of great significance for clinical diagnosis and the planning of surgical treatments. Recent advancements in machine learning have shown that convolutional neural networks are powerful in such image processing while largely reducing human labor. However, the variable shape, fuzzy boundary, and discontinuous tumor region of liver tumors in medical images bring great challenges to accurate segmentation. The feature extraction capability of a neural network can be improved by expanding its architecture, but it inevitably demands more computing resources in training and hyperparameter tuning.

Methods:

This study presents a Dynamic Context Encoder Network (DCE-Net), which incorporates multiple new modules, such as the Involution Layer, Dynamic Residual Module, Context Extraction Module, and Channel Attention Gates, for feature extraction and enhancement.

Results:

In the experiment, we used a liver tumor CT dataset of LiTS2017 to train and test the DCE-Net for liver tumor segmentation. The experimental results showed that the four evaluation indexes of the method, precision, recall, dice, and AUC, were 0.8961, 0.9711, 0.9270, and 0.9875, respectively. Furthermore, our ablation study reported that the accuracy and training efficiency of our network were markedly superior to the networks without involution or dynamic residual modules.

Conclusion:

Therefore, the DCE-Net proposed in this study has great potential for automatic segmentation of liver lesion tumors in the clinical diagnostic environment.

© 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.
Loading

Article metrics loading...

/content/journals/cmir/10.2174/0115734056303257240529100041
2024-01-01
2025-07-06

  • From This Site

    /content/journals/cmir/10.2174/0115734056303257240529100041
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
The full text of this item is not currently available.

References

  1. SungH. FerlayJ. SiegelR.L. LaversanneM. SoerjomataramI. JemalA. BrayF. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin.202171320924910.3322/caac.2166033538338
     [Google Scholar]
  2. HaugenA.S. SøftelandE. AlmelandS.K. SevdalisN. VonenB. EideG.E. NortvedtM.W. HarthugS. Effect of the World Health Organization checklist on patient outcomes: A stepped wedge cluster randomized controlled trial.Ann. Surg.2015261582182810.1097/SLA.000000000000071624824415
     [Google Scholar]
  3. MenonB.K. d’EsterreC.D. QaziE.M. AlmekhlafiM. HahnL. DemchukA.M. GoyalM. Multiphase CT angiography: A new tool for the imaging triage of patients with acute ischemic stroke.Radiology2015275251052010.1148/radiol.1514225625633505
     [Google Scholar]
  4. ZhaoJ. LiD. XiaoX. AccorsiF. MarshallH. CossettoT. KimD. McCarthyD. DawsonC. KnezevicS. ChenB. LiS. United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI.Med. Image Anal.20217310215410.1016/j.media.2021.10215434280670
     [Google Scholar]
  5. Mehr Yahya DurraniM.Y.D. Mehr Yahya DurraniS.Y. Sadaf YasminS.R. An Internet of Medical Things Based Liver Tumor Detection System using Semantic Segmentation.Wangji Wanglu Jishu Xuekan202223236337510.53106/160792642022032302015
     [Google Scholar]
  6. TangX. PangY. ZhangH. Fast image segmentation method based on threshold.2008 Chinese Control and Decision ConferenceYantai, China, 2008, pp. 3281-32852008
     [Google Scholar]
  7. SethiG. SainiB.S. SinghD. Segmentation of cancerous regions in liver using an edge-based and phase congruent region enhancement method.Comput. Electr. Eng.20165324426210.1016/j.compeleceng.2015.06.025
     [Google Scholar]
  8. WuW WuS ZhouZ 3D liver tumor segmentation in CT images using improved fuzzy C-means and graph cuts.Biomed Res. Int.201710.1155/2017/5207685
     [Google Scholar]
  9. JiangH.Y. SiY.P. LuoX.G. Medical image segmentation based on improved Ostu algorithm and regional growth algorithm.Journal of Northeastern University2006274398
     [Google Scholar]
  10. BaâzaouiA. BarhoumiW. AhmedA. ZagroubaE. Semi-automated segmentation of single and multiple tumors in liver CT images using entropy-based fuzzy region growing.IRBM20173829810810.1016/j.irbm.2017.02.003
     [Google Scholar]
  11. XuL. ZhuY. ZhangY. YangH. Liver segmentation based on region growing and level set active contour model with new signed pressure force function.Optik202020216370510.1016/j.ijleo.2019.163705
     [Google Scholar]
  12. IsenseeF. JaegerP.F. KohlS.A.A. PetersenJ. Maier-HeinK.H. nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation.Nat. Methods202118220321110.1038/s41592‑020‑01008‑z33288961
     [Google Scholar]
  13. KingmaD.P. BaJ. Adam: A method for stochastic optimizationarXiv:141269802014
     [Google Scholar]
  14. ChlebusG. SchenkA. MoltzJ.H. van GinnekenB. HahnH.K. MeineH. Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing.Sci. Rep.2018811549710.1038/s41598‑018‑33860‑730341319
     [Google Scholar]
  15. ZhengS. FangB. LiL. GaoM. WangY. PengK. Automatic liver tumour segmentation in CT combining FCN and NMF-based deformable model.Comput. Methods Biomech. Biomed. Eng. Imaging Vis.20208546847710.1080/21681163.2018.1493618
     [Google Scholar]
  16. ZhangZ. FuH. DaiH. ShenJ. PangY. ShaoL. Et-net: A generic edge-attention guidance network for medical image segmentation.International Conference on Medical Image Computing and Computer-Assisted InterventionShenzhen, China, 13-17 October, 2019, pp. 442-450.10.1007/978‑3‑030‑32239‑7_49
     [Google Scholar]
  17. ChungM. LeeJ. ParkS. LeeC.E. LeeJ. ShinY.G. Liver segmentation in abdominal CT images via auto-context neural network and self-supervised contour attention.Artif. Intell. Med.202111310202310.1016/j.artmed.2021.10202333685586
     [Google Scholar]
  18. HongY. MaoX. HuiQ. OuyangX. PengZ. KongD. Automatic liver and tumor segmentation based on deep learning and globally optimized refinement.Appl. Math. J. Chin. Univ.202136230431610.1007/s11766‑021‑4376‑3
     [Google Scholar]
  19. LuanS. XueX. DingY. WeiW. ZhuB. Adaptive attention convolutional neural network for liver tumor segmentation.Front. Oncol.20211168080710.3389/fonc.2021.68080734434891
     [Google Scholar]
  20. HuJ. WangH. WangJ. WangY. HeF. ZhangJ. SA-Net: A scale-attention network for medical image segmentation.PLoS One2021164e024738810.1371/journal.pone.024738833852577
     [Google Scholar]
  21. ZhangC. LuJ. HuaQ. LiC. WangP. SAA-Net: U-shaped network with Scale-Axis-Attention for liver tumor segmentation.Biomed. Signal Process. Control20227310346010.1016/j.bspc.2021.103460
     [Google Scholar]
  22. WuJ. FuruzukiM. LiG. KamiyaT. MabuS. TanabeM. ItoK. KidoS. Segmentation of liver tumors in multiphase computed tomography images using hybrid method.Comput. Electr. Eng.20229710762610763610.1016/j.compeleceng.2021.107626
     [Google Scholar]
  23. ShaoL. LiuJ. YanZ. Segmentation of liver tumor based on mobilenetv3 U-shape net.IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence(ICIBA)Chongqing, China, 2023, pp. 80-85.
     [Google Scholar]
  24. RonnebergerO. FischerP. BroxT. U-net: Convolutional networks for biomedical image segmentation.International Conference on Medical image computing and computer-assisted interventionMunich, Germany, October 5-9, 2015, pp. 234-241.10.1007/978‑3‑319‑24574‑4_28
     [Google Scholar]
  25. LiD. HuJ. WangC. LiX. SheQ. ZhuL. Involution: Inverting the inherence of convolution for visual recognition.2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Nashville, TN, USA, 2021, pp. 12316-12325.10.1109/CVPR46437.2021.01214
     [Google Scholar]
  26. ChenY. DaiX. LiuM. ChenD. YuanL. LiuZ. Dynamic convolution: Attention over convolution kernels.Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern RecognitionSeattle, WA, USA, 2020, pp. 11027-11036.10.1109/CVPR42600.2020.01104
     [Google Scholar]
  27. GuZ. ChengJ. FuH. ZhouK. HaoH. ZhaoY. ZhangT. GaoS. LiuJ. Ce-net: Context encoder network for 2d medical image segmentation.IEEE Trans. Med. Imaging201938102281229210.1109/TMI.2019.290356230843824
     [Google Scholar]
  28. OktayO. SchlemperJ. FolgocL.L. LeeM. HeinrichM. MisawaK. Attention u-net: Learning where to look for the pancreasarXiv:1804.039992018
     [Google Scholar]
  29. WooS. ParkJ. LeeJ-Y. KweonI.S. CBAM: Convolutional block attention module.Proceedings of the European conference on computer vision (ECCV)Munich, Germany, September 8–14, 2018, pp. 3-19.201810.1007/978‑3‑030‑01234‑2_1
     [Google Scholar]
  30. LiuJ. YuanC. SunX. SunL. DongH. PengY. The measurement of Cobb angle based on spine X-ray images using multi-scale convolutional neural network.Physical and Engineering Sciences in Medicine202144380982110.1007/s13246‑021‑01032‑z34251603
     [Google Scholar]
  31. HeK. ZhangX. RenS. SunJ. Deep residual learning for image recognition.Proceedings of the IEEE conference on computer vision and pattern recognitionLas Vegas, NV, USA, 2016, pp. 770-778.10.48550/arXiv.1512.03385
     [Google Scholar]
  32. PunnN.S. AgarwalS. Inception u-net architecture for semantic segmentation to identify nuclei in microscopy cell images.ACM Trans. Multimed. Comput. Commun. Appl.202016111510.1145/3376922
     [Google Scholar]
  33. YuF. KoltunV. FunkhouserT. Dilated Residual Networks.2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)Honolulu, HI, USA, 2017, pp. 636-644.10.1109/CVPR.2017.75
     [Google Scholar]
  34. ChenL-C. ZhuY. PapandreouG. SchroffF. AdamH. Encoder-decoder with atrous separable convolution for semantic image segmentation.Proceedings of the European conference on computer vision (ECCV)Munich, Germany, September 8–14, 2018, pp. 833-851.10.1007/978‑3‑030‑01234‑2_49
     [Google Scholar]
  35. Rota BuloS. NeuholdG. KontschiederP. Loss max-pooling for semantic image segmentation.Proceedings of the IEEE conference on computer vision and pattern recognitionHonolulu, HI, USA, 2017, pp. 7082-7091.10.1109/CVPR.2017.749
     [Google Scholar]
  36. ApostolopoulosS. ZanetS.D. CillerC. WolfS. SznitmanR. Pathological OCT retinal layer segmentation using branch residual u-shape networks.International Conference on Medical Image Computing and Computer-Assisted InterventionQuebec City, QC, Canada, September 11-13, 2017, pp. 294-301.10.1007/978‑3‑319‑66179‑7_34
     [Google Scholar]
  37. HoerlA.E. KennardR.W. Ridge regression: Biased estimation for nonorthogonal problems.Technometrics1970121556710.1080/00401706.1970.10488634
     [Google Scholar]
  38. CrumW.R. CamaraO. HillD.L.G. Generalized overlap measures for evaluation and validation in medical image analysis.IEEE Trans. Med. Imaging200625111451146110.1109/TMI.2006.88058717117774
     [Google Scholar]
  39. MilletariF. NavabN. AhmadiS-A. V-Net: Fully convolutional neural networks for volumetric medical image segmentation. arXiv:1606.047972016
     [Google Scholar]
  40. SoomroT.A. AfifiA.J. GaoJ. HellwichO. PaulM. ZhengL. Strided U-net model: Retinal vessels segmentation using dice loss.2018 Digital Image Computing: Techniques and Applications (DICTA)Canberra, ACT, Australia, 2018, pp. 1-8.10.1109/DICTA.2018.8615770
     [Google Scholar]
  41. PaszkeA. GrossS. MassaF. LererA. BradburyJ. ChananG. Pytorch: An imperative style, high-performance deep learning library.arXiv:1912.01703201932
     [Google Scholar]
  42. FanT. WangG. LiY. WangH. Ma-net: A multi-scale attention network for liver and tumor segmentation.IEEE Access2020817965617966510.1109/ACCESS.2020.3025372
     [Google Scholar]
  43. BilicP. ChristP.F. VorontsovE. ChlebusG. ChenH. DouQ. The liver tumor segmentation benchmark (lits).Med Image Anal201984102680
     [Google Scholar]
  44. ThadaV. JaglanV. Comparison of jaccard, dice, cosine similarity coefficient to find best fitness value for web retrieved documents using genetic algorithm.Int. J. Innov. Eng. Technol..201324202205
     [Google Scholar]
  45. LoboJ.M. Jiménez-ValverdeA. RealR. AUC: A misleading measure of the performance of predictive distribution models.Glob. Ecol. Biogeogr.200817214515110.1111/j.1466‑8238.2007.00358.x
     [Google Scholar]
  46. GoceriE Polyp segmentation using a hybrid vision transformer and a hybrid loss function.J. Imaging Inform. Med.20243785186310.1007/s10278‑023‑00954‑2
     [Google Scholar]
  47. GoceriE. Medical image data augmentation: Techniques, comparisons and interpretations.Artif. Intell. Rev.20235611125611260510.1007/s10462‑023‑10453‑z37362888
     [Google Scholar]
  48. AfsharP PlataniotisK N MohammadiA Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries.IEEE International Conference on Acoustics, Speech, and Signal Processing2019201913681372
     [Google Scholar]
  49. AduK. YuY. CaiJ. Dilated capsule network for brain tumor type classification via MRI segmented tumor region2019 IEEE International Conference on Robotics and Biomimetics (ROBIO)Dali, China, 2019, pp. 942-947.
     [Google Scholar]
  50. AfsharP. PlataniotisK.N. MohammadiA. BoostCaps: A boosted capsule network for brain tumor classification.2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)Montreal, QC, Canada, 2020, pp. 1075-1079.
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056303257240529100041
Loading
A Dynamic Context Encoder Network for Liver Tumor Segmentation
Curr. Med. Imaging 20, e15734056303257 (2024); https://doi.org/10.2174/0115734056303257240529100041
/content/journals/cmir/10.2174/0115734056303257240529100041
/content/journals/cmir/10.2174/0115734056303257240529100041
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Attention; Dynamic context encoder network (DCE-Net); Feature extraction; Liver cancer; Liver tumor; Segmentation

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test