Araştırma Makalesi
BibTex RIS Kaynak Göster

Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality

Yıl 2022, Cilt: 06 Sayı: 2, 67 - 72, 31.12.2022
https://doi.org/10.34110/forecasting.1190299

Öz

The pancreas is one of the vital organs in the human body. Early diagnosis of a disease in the pancreas is critical. In this way, the effects of pancreas diseases, especially pancreatic cancer on the person are decreased. With this purpose, artificial intelligence-assisted pancreatic cancer segmentation was performed for early diagnosis in this paper. For this aim, several state-of-the-art segmentation networks, UNet, LinkNet, SegNet, SQ-Net, DABNet, EDANet, and ESNet were used in this study. In the comparative analysis, the best segmentation performance has been achieved by SQ-Net. SQ-Net has achieved a 0.917 dice score, 0.847 IoU score, 0.920 sensitivity, 1.000 specificity, 0.914 precision, and 0.999 accuracy. Considering these results, an artificial intelligence-based decision support system was created in the study.

Teşekkür

This paper has been prepared by AKGUN Computer Incorporated Company. We would like to thank AKGUN Computer Inc. for providing all kinds of opportunities and funds for the execution of this project.

Kaynakça

  • J.X. Hu, Y.Y. Lin, C.F. Zhao, W.B. Chen, Q.C. Liu, Q.W. Li, F. Gao, Pancreatic cancer: A review of epidemiology, trend, and risk factors, World J. Gastroenterol. 27 (2021) 4298–4321. doi:10.3748/wjg.v27.i27.4298.
  • V. Chaudhary, S. Bano, Imaging of the pancreas: Recent advances, Indian J. Endocrinol. Metab. 15 (2011) 25. doi:10.4103/2230-8210.83060.
  • Z. Liu, J. Su, R. Wang, R. Jiang, Y.Q. Song, D. Zhang, Y. Zhu, D. Yuan, Q. Gan, V.S. Sheng, Pancreas Co-segmentation based on dynamic ROI extraction and VGGU-Net, Expert Syst. Appl. 192 (2022) 116444. doi:10.1016/j.eswa.2021.116444.
  • D. Zhang, J. Zhang, Q. Zhang, J. Han, S. Zhang, J. Han, Automatic pancreas segmentation based on lightweight DCNN modules and spatial prior propagation, Pattern Recognit. 114 (2021) 107762. doi:10.1016/j.patcog.2020.107762.
  • O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), Springer Verlag, 2015: pp. 234–241. doi:10.1007/978-3-319-24574-4_28/COVER.
  • A. Chaurasia, E. Culurciello, LinkNet: Exploiting encoder representations for efficient semantic segmentation, in: 2017 IEEE Vis. Commun. Image Process. VCIP 2017, Institute of Electrical and Electronics Engineers Inc., 2018: pp. 1–4. doi:10.1109/VCIP.2017.8305148.
  • V. Badrinarayanan, A. Kendall, R. Cipolla, SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 39 (2017) 2481–2495. doi:10.1109/TPAMI.2016.2644615.
  • M. Treml, J. Arjona-medina, T. Unterthiner, R. Durgesh, F. Friedmann, P. Schuberth, A. Mayr, M. Heusel, M. Hofmarcher, M. Widrich, B. Nessler, S. Hochreiter, Speeding up Semantic Segmentation for Autonomous Driving, NIPS 2016 Work. MLITS. (2016) 1–7. https://openreview.net/pdf?id=S1uHiFyyg%0Ahttps://openreview.net/forum?id=S1uHiFyyg (accessed 14 October 2022).
  • G. Li, J. Kim, DABNet: Depth-wise asymmetric bottleneck for real-time semantic segmentation, in: 30th Br. Mach. Vis. Conf. 2019, BMVC 2019, BMVA Press, 2020. doi:10.48550/arxiv.1907.11357.
  • S.Y. Lo, H.M. Hang, S.W. Chan, J.J. Li, Efficient dense modules of asymmetric convolution for real-time semantic segmentation, in: 1st ACM Int. Conf. Multimed. Asia, MMAsia 2019, Association for Computing Machinery, Inc, 2019. doi:10.1145/3338533.3366558.
  • Y. Wang, Q. Zhou, J. Xiong, X. Wu, X. Jin, ESNet: An efficient symmetric network for real-time semantic segmentation, in: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), Springer, 2019: pp. 41–52. doi:10.1007/978-3-030-31723-2_4.
  • A. Derin, C. Gurkan, A. Budak, H. Karatas, Pancreas Segmentation Using U-Net Based Segmentation Networks in CT Modality: A Comparative Analysis, Eur. J. Sci. Technol. 40 (2022) 94–98. doi:10.31590/EJOSAT.1171803.
  • H.R. Roth, A. Farag, L. Lu, E.B. Turkbey, R.M. Summers, Deep convolutional networks for pancreas segmentation in CT imaging, in: Med. Imaging 2015 Image Process., SPIE, 2015: p. 94131G. doi:10.1117/12.2081420.
  • W. Li, S. Qin, F. Li, L. Wang, MAD-UNet: A deep U-shaped network combined with an attention mechanism for pancreas segmentation in CT images, Med. Phys. 48 (2021) 329–341. doi:10.1002/mp.14617.
  • N. Zhao, N. Tong, D. Ruan, K. Sheng, Fully Automated Pancreas Segmentation with Two-Stage 3D Convolutional Neural Networks, in: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), Springer Science and Business Media Deutschland GmbH, 2019: pp. 201–209. doi:10.1007/978-3-030-32245-8_23.
  • E. Kurnaz, R. Ceylan, Pancreas Segmentation in Abdominal CT Images with U-Net Model, in: 2020 28th Signal Process. Commun. Appl. Conf. SIU 2020 - Proc., Institute of Electrical and Electronics Engineers Inc., 2020. doi:10.1109/SIU49456.2020.9302180.
  • G. Suman, A. Patra, P. Korfiatis, S. Majumder, S.T. Chari, M.J. Truty, J.G. Fletcher, A.H. Goenka, Quality gaps in public pancreas imaging datasets: Implications & challenges for AI applications, Pancreatology. 21 (2021) 1001–1008. doi:10.1016/j.pan.2021.03.016.
  • A.F. Bayram, A. Derin, C. Gurkan, A. Budak, H. Karatas, Analysis of the Effects of Segmentation Networks and Loss Functions in Ischemic Stroke Lesion Segmentation, Eur. J. Sci. Technol. 40 (2022) 82–87. doi:10.31590/EJOSAT.1173070.
  • A. Derin, A.F. Bayram, C. Gurkan, A. Budak, H. Karatas, Automatic Skull Stripping and Brain Segmentation with U-Net in MRI Database, Eur. J. Sci. Technol. 40 (2022) 75–81. doi:10.31590/EJOSAT.1173065.
  • F. Karakaya, C. Gurkan, A. Budak, H. Karatas, Classification and Segmentation of Alzheimer Disease in MRI Modality using the Deep Convolutional Neural Networks, Eur. J. Sci. Technol. 40 (2022) 99–105. doi:10.31590/EJOSAT.1171810.
Yıl 2022, Cilt: 06 Sayı: 2, 67 - 72, 31.12.2022
https://doi.org/10.34110/forecasting.1190299

Öz

Kaynakça

  • J.X. Hu, Y.Y. Lin, C.F. Zhao, W.B. Chen, Q.C. Liu, Q.W. Li, F. Gao, Pancreatic cancer: A review of epidemiology, trend, and risk factors, World J. Gastroenterol. 27 (2021) 4298–4321. doi:10.3748/wjg.v27.i27.4298.
  • V. Chaudhary, S. Bano, Imaging of the pancreas: Recent advances, Indian J. Endocrinol. Metab. 15 (2011) 25. doi:10.4103/2230-8210.83060.
  • Z. Liu, J. Su, R. Wang, R. Jiang, Y.Q. Song, D. Zhang, Y. Zhu, D. Yuan, Q. Gan, V.S. Sheng, Pancreas Co-segmentation based on dynamic ROI extraction and VGGU-Net, Expert Syst. Appl. 192 (2022) 116444. doi:10.1016/j.eswa.2021.116444.
  • D. Zhang, J. Zhang, Q. Zhang, J. Han, S. Zhang, J. Han, Automatic pancreas segmentation based on lightweight DCNN modules and spatial prior propagation, Pattern Recognit. 114 (2021) 107762. doi:10.1016/j.patcog.2020.107762.
  • O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), Springer Verlag, 2015: pp. 234–241. doi:10.1007/978-3-319-24574-4_28/COVER.
  • A. Chaurasia, E. Culurciello, LinkNet: Exploiting encoder representations for efficient semantic segmentation, in: 2017 IEEE Vis. Commun. Image Process. VCIP 2017, Institute of Electrical and Electronics Engineers Inc., 2018: pp. 1–4. doi:10.1109/VCIP.2017.8305148.
  • V. Badrinarayanan, A. Kendall, R. Cipolla, SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 39 (2017) 2481–2495. doi:10.1109/TPAMI.2016.2644615.
  • M. Treml, J. Arjona-medina, T. Unterthiner, R. Durgesh, F. Friedmann, P. Schuberth, A. Mayr, M. Heusel, M. Hofmarcher, M. Widrich, B. Nessler, S. Hochreiter, Speeding up Semantic Segmentation for Autonomous Driving, NIPS 2016 Work. MLITS. (2016) 1–7. https://openreview.net/pdf?id=S1uHiFyyg%0Ahttps://openreview.net/forum?id=S1uHiFyyg (accessed 14 October 2022).
  • G. Li, J. Kim, DABNet: Depth-wise asymmetric bottleneck for real-time semantic segmentation, in: 30th Br. Mach. Vis. Conf. 2019, BMVC 2019, BMVA Press, 2020. doi:10.48550/arxiv.1907.11357.
  • S.Y. Lo, H.M. Hang, S.W. Chan, J.J. Li, Efficient dense modules of asymmetric convolution for real-time semantic segmentation, in: 1st ACM Int. Conf. Multimed. Asia, MMAsia 2019, Association for Computing Machinery, Inc, 2019. doi:10.1145/3338533.3366558.
  • Y. Wang, Q. Zhou, J. Xiong, X. Wu, X. Jin, ESNet: An efficient symmetric network for real-time semantic segmentation, in: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), Springer, 2019: pp. 41–52. doi:10.1007/978-3-030-31723-2_4.
  • A. Derin, C. Gurkan, A. Budak, H. Karatas, Pancreas Segmentation Using U-Net Based Segmentation Networks in CT Modality: A Comparative Analysis, Eur. J. Sci. Technol. 40 (2022) 94–98. doi:10.31590/EJOSAT.1171803.
  • H.R. Roth, A. Farag, L. Lu, E.B. Turkbey, R.M. Summers, Deep convolutional networks for pancreas segmentation in CT imaging, in: Med. Imaging 2015 Image Process., SPIE, 2015: p. 94131G. doi:10.1117/12.2081420.
  • W. Li, S. Qin, F. Li, L. Wang, MAD-UNet: A deep U-shaped network combined with an attention mechanism for pancreas segmentation in CT images, Med. Phys. 48 (2021) 329–341. doi:10.1002/mp.14617.
  • N. Zhao, N. Tong, D. Ruan, K. Sheng, Fully Automated Pancreas Segmentation with Two-Stage 3D Convolutional Neural Networks, in: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), Springer Science and Business Media Deutschland GmbH, 2019: pp. 201–209. doi:10.1007/978-3-030-32245-8_23.
  • E. Kurnaz, R. Ceylan, Pancreas Segmentation in Abdominal CT Images with U-Net Model, in: 2020 28th Signal Process. Commun. Appl. Conf. SIU 2020 - Proc., Institute of Electrical and Electronics Engineers Inc., 2020. doi:10.1109/SIU49456.2020.9302180.
  • G. Suman, A. Patra, P. Korfiatis, S. Majumder, S.T. Chari, M.J. Truty, J.G. Fletcher, A.H. Goenka, Quality gaps in public pancreas imaging datasets: Implications & challenges for AI applications, Pancreatology. 21 (2021) 1001–1008. doi:10.1016/j.pan.2021.03.016.
  • A.F. Bayram, A. Derin, C. Gurkan, A. Budak, H. Karatas, Analysis of the Effects of Segmentation Networks and Loss Functions in Ischemic Stroke Lesion Segmentation, Eur. J. Sci. Technol. 40 (2022) 82–87. doi:10.31590/EJOSAT.1173070.
  • A. Derin, A.F. Bayram, C. Gurkan, A. Budak, H. Karatas, Automatic Skull Stripping and Brain Segmentation with U-Net in MRI Database, Eur. J. Sci. Technol. 40 (2022) 75–81. doi:10.31590/EJOSAT.1173065.
  • F. Karakaya, C. Gurkan, A. Budak, H. Karatas, Classification and Segmentation of Alzheimer Disease in MRI Modality using the Deep Convolutional Neural Networks, Eur. J. Sci. Technol. 40 (2022) 99–105. doi:10.31590/EJOSAT.1171810.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Matematik
Bölüm Articles
Yazarlar

Ahmet Furkan Bayram 0000-0002-1304-9941

Caglar Gurkan 0000-0002-4652-3363

Abdulkadir Budak 0000-0002-0328-6783

Hakan Karataş 0000-0002-9497-5444

Yayımlanma Tarihi 31 Aralık 2022
Gönderilme Tarihi 17 Ekim 2022
Kabul Tarihi 6 Aralık 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 06 Sayı: 2

Kaynak Göster

APA Bayram, A. F., Gurkan, C., Budak, A., Karataş, H. (2022). Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality. Turkish Journal of Forecasting, 06(2), 67-72. https://doi.org/10.34110/forecasting.1190299
AMA Bayram AF, Gurkan C, Budak A, Karataş H. Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality. TJF. Aralık 2022;06(2):67-72. doi:10.34110/forecasting.1190299
Chicago Bayram, Ahmet Furkan, Caglar Gurkan, Abdulkadir Budak, ve Hakan Karataş. “Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality”. Turkish Journal of Forecasting 06, sy. 2 (Aralık 2022): 67-72. https://doi.org/10.34110/forecasting.1190299.
EndNote Bayram AF, Gurkan C, Budak A, Karataş H (01 Aralık 2022) Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality. Turkish Journal of Forecasting 06 2 67–72.
IEEE A. F. Bayram, C. Gurkan, A. Budak, ve H. Karataş, “Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality”, TJF, c. 06, sy. 2, ss. 67–72, 2022, doi: 10.34110/forecasting.1190299.
ISNAD Bayram, Ahmet Furkan vd. “Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality”. Turkish Journal of Forecasting 06/2 (Aralık 2022), 67-72. https://doi.org/10.34110/forecasting.1190299.
JAMA Bayram AF, Gurkan C, Budak A, Karataş H. Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality. TJF. 2022;06:67–72.
MLA Bayram, Ahmet Furkan vd. “Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality”. Turkish Journal of Forecasting, c. 06, sy. 2, 2022, ss. 67-72, doi:10.34110/forecasting.1190299.
Vancouver Bayram AF, Gurkan C, Budak A, Karataş H. Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality. TJF. 2022;06(2):67-72.

INDEXING

   16153                        16126   

  16127                       16128                       16129