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3D MEDICAL IMAGE SEGMENTATION WITH DEEP LEARNING METHODS

Year 2025, Volume: 9 Issue: 1, 73 - 91, 30.04.2025

Sezin Barın , Uçman Ergün , Gür Emre Güraksın

https://doi.org/10.46519/ij3dptdi.1571288

Abstract

With advancements in technology, three-dimensional (3D) medical imaging has become vital in modern medicine, contributing to more accurate diagnosis, treatment planning, and personalized medicine. However, segmenting abdominal organs remains a challenging task due to anatomical variations, limited labeled data, and image noise. This study investigates the impact of deep learning-based architectures and preprocessing techniques on 3D organ segmentation using the publicly available Multi-Atlas Labeling Beyond the Cranial Vault (BTCV) dataset. To achieve this, 3D U-Net, UNETR, and SwinUNETR models were employed, and the effects of various preprocessing techniques and loss functions, including Dice Loss, Focal Loss, and Cross-Entropy Loss, were systematically analyzed. The findings reveal that combining Dice Loss with Cross-Entropy Loss significantly enhances segmentation performance. Additionally, preprocessing techniques improved segmentation accuracy by 1.19%, further optimizing model performance. Among the evaluated models, 3D U-Net achieved the highest overall segmentation performance, with an average Dice score of 0.8397, outperforming SwinUNETR and UNETR. These findings underscore the importance of selecting appropriate preprocessing methods and loss functions in 3D medical image segmentation. The results contribute to more precise and efficient medical image analysis, with potential applications in clinical decision support systems. Future research should focus on optimizing hybrid architectures, integrating advanced augmentation strategies, and expanding evaluation across multiple datasets to improve the robustness and real-world applicability of automated segmentation methods.

Keywords

Deep Learning Image Processing 3D Image Segmentation Medical Image Analysis 3D U-Net UNETR SwinUNETR.

References

  • 1. Rueckert D. and Schnabel J. A., “Model-Based and Data-Driven Strategies in Medical Image Computing,” Proceedings of the IEEE, Vol. 108, Issue 1, Pages 110–124, 2020.
  • 2. Wachinger C., Reuter M. and Klein T., “DeepNAT: Deep convolutional neural network for segmenting neuroanatomy,” NeuroImage, Vol. 170, Pages 434–445, 2018.
  • 3. Litjens G., Kooi T., Bejnordi B. E., Setio A. A. A., Ciompi F., Ghafoorian M., van der Laak J. A. W. M., van Ginneken B. and Sánchez C. I., “A survey on deep learning in medical image analysis,” Medical Image Analysis, Vol. 42, Pages 60–88, 2017.
  • 4. Lecun Y., Bengio Y. and Hinton G., “Deep learning,” Nature , Vol. 521, Issue 7553, Pages 436–444, 2015.
  • 5. Shen D., Wu G. and Suk H. Il, “Deep Learning in Medical Image Analysis,” Annual Review of Biomedical Engineering, Vol. 19, Issue Volume 19, 2017, Pages 221–248, 2017.
  • 6. Greenspan H., Van Ginneken B. and Summers R. M., “Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique,” IEEE Transactions on Medical Imaging, Vol. 35, Issue 5, Pages 1153–1159, 2016.
  • 7. Tajbakhsh N., Jeyaseelan L., Li Q., Chiang J. N., Wu Z. and Ding X., “Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation,” Medical Image Analysis, Vol. 63, Page 101693, 2020.
  • 8. Taha A. A. and Hanbury A., “Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool,” BMC Medical Imaging, Vol. 15, Issue 1, Pages 1–28, 2015.
  • 9. Asgari Taghanaki S., Abhishek K., Cohen J. P., Cohen-Adad J. and Hamarneh G., “Deep semantic segmentation of natural and medical images: a review,” Artificial Intelligence Review, Vol. 54, Issue 1, Pages 137–178, 2021.
  • 10. Pham D. L., Xu C. and Prince J. L., “Current methods in medical image segmentation,” Annual Review of Biomedical Engineering, Vol. 2, Issue 2000, Pages 315–337, 2000.
  • 11. Sharma N., Ray A. K., Shukla K. K., Sharma S., Pradhan S., Srivastva A. and Aggarwal L., “Automated medical image segmentation techniques,” Journal of Medical Physics, Vol. 35, Issue 1, Pages 3–14, 2010.
  • 12. Norouzi A., Rahim M. S. M., Altameem A., Saba T., Rad A. E., Rehman A. and Uddin M., “Medical Image Segmentation Methods, Algorithms, and Applications,” IETE Technical Review, Vol. 31, Issue 3, Pages 199–213, 2014.
  • 13. Ronneberger O., Fischer P. and Brox T., “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9351, Pages 234–241, 2015.
  • 14. Roth H. R., Shen C., Oda H., Sugino T., Oda M., Hayashi Y., Misawa K. and Mori K., “A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11073 LNCS, Pages 417–425, 2018.
  • 15. Hesamian M. H., Jia W., He X. and Kennedy P., “Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges,” Journal of Digital Imaging, Vol. 32, Issue 4, Pages 582–596, 2019.
  • 16. Çiçek Ö., Abdulkadir A., Lienkamp S. S., Brox T. and Ronneberger O., “3D U-net: Learning dense volumetric segmentation from sparse annotation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9901 LNCS, Pages 424–432, 2016.
  • 17. Chen S., Roth H., Dorn S., May M., Cavallaro A., Lell M. M., Kachelrieß M., Oda H., Mori K. and Maier A., “Towards Automatic Abdominal Multi-Organ Segmentation in Dual Energy CT using Cascaded 3D Fully Convolutional Network,” 2017.
  • 18. Isensee F., Jaeger P. F., Kohl S. A. A., Petersen J. and Maier-Hein K. H., “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation,” Nature Methods, Vol. 18, Issue 2, Pages 203–211, 2021.
  • 19. Hatamizadeh A., Tang Y., Nath V., Yang D., Myronenko A., Landman B., Roth H. R. and Xu D., “UNETR: Transformers for 3D Medical Image Segmentation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, Pages 574–584.
  • 20. Cao H., Wang Y., Chen J., Jiang D., Zhang X., Tian Q. and Wang M., “Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation,” in European Conference on Computer Vision, 2023, Vol. 13803 LNCS, Pages 205–218.
  • 21. Milletari F., Navab N. and Ahmadi S. A., “V-Net: Fully convolutional neural networks for volumetric medical image segmentation,” Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016, Pages 565–571, 2016.
  • 22. Litjens G., Toth R., van de Ven W., Hoeks C., Kerkstra S., van Ginneken B., Vincent G., Guillard G., Birbeck N., Zhang J., Strand R., Malmberg F., Ou Y., Davatzikos C., Kirschner M., Jung F., Yuan J., Qiu W., Gao Q. et al., “Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge,” Medical Image Analysis, Vol. 18, Issue 2, Pages 359–373, 2014.
  • 23. Chen J., Lu Y., Yu Q., Luo X., Adeli E., Wang Y., Lu L., Yuille A. L. and Zhou Y., “TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation,” 2021.
  • 24. Ma J., Li F. and Wang B., “U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation,” 2024.
  • 25. Xing Z., Ye T., Yang Y., Liu G. and Zhu L., “SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation,” 2024.
  • 26. Zhao MIT A., Balakrishnan MIT G., Durand MIT F., Guttag MIT J. V and Dalca MIT A. V, “Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, Pages 8543–8553.
  • 27. Landman B., Xu Z., Igelsias J., Styner M., Langerak T. and Klein A., “MICCAI multi-atlas labeling beyond the cranial vault-workshop and challenge,” 2015.
  • 28. “MONAI - Home,” Available: https://monai.io/index.html [Accessed: 20 September 2024]
  • 29. Azad R., Heidary M., Yilmaz K., Hüttemann M., Karimijafarbigloo S., Wu Y., Schmeink A. and Merhof D., “Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook,” 2023.
  • 30. Kline D. M. and Berardi V. L., “Revisiting squared-error and cross-entropy functions for training neural network classifiers,” Neural Computing and Applications, Vol. 14, Issue 4, Pages 310–318, 2005.
  • 31. Lin T.-Y., Goyal P., Girshick R., He K. and Dollar P., “Focal Loss for Dense Object Detection,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, Pages 2980–2988.
  • 32. Karimi Monsefi A., Karisani P., Zhou M., Choi S., Doble N., Ji H., Parthasarathy S. and Ramnath R., “Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain,” Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Pages 1348–1359, 2024.
  • 33. Zheng S., Lu J., Zhao H., Zhu X., Luo Z., Wang Y., Fu Y., Feng J., Xiang T., Torr P. H. S. and Zhang L., “Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, Pages 6881–6890.
  • 34. Chen L.-C., Zhu Y., Papandreou G., Schroff F. and Adam H., “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,” arxiv, 2018.

3D MEDICAL IMAGE SEGMENTATION WITH DEEP LEARNING METHODS

Year 2025, Volume: 9 Issue: 1, 73 - 91, 30.04.2025

Sezin Barın , Uçman Ergün , Gür Emre Güraksın

https://doi.org/10.46519/ij3dptdi.1571288

Abstract

With advancements in technology, three-dimensional (3D) medical imaging has become vital in modern medicine, contributing to more accurate diagnosis, treatment planning, and personalized medicine. However, segmenting abdominal organs remains a challenging task due to anatomical variations, limited labeled data, and image noise. This study investigates the impact of deep learning-based architectures and preprocessing techniques on 3D organ segmentation using the publicly available Multi-Atlas Labeling Beyond the Cranial Vault (BTCV) dataset. To achieve this, 3D U-Net, UNETR, and SwinUNETR models were employed, and the effects of various preprocessing techniques and loss functions, including Dice Loss, Focal Loss, and Cross-Entropy Loss, were systematically analyzed. The findings reveal that combining Dice Loss with Cross-Entropy Loss significantly enhances segmentation performance. Additionally, preprocessing techniques improved segmentation accuracy by 1.19%, further optimizing model performance. Among the evaluated models, 3D U-Net achieved the highest overall segmentation performance, with an average Dice score of 0.8397, outperforming SwinUNETR and UNETR. These findings underscore the importance of selecting appropriate preprocessing methods and loss functions in 3D medical image segmentation. The results contribute to more precise and efficient medical image analysis, with potential applications in clinical decision support systems. Future research should focus on optimizing hybrid architectures, integrating advanced augmentation strategies, and expanding evaluation across multiple datasets to improve the robustness and real-world applicability of automated segmentation methods.

Keywords

Deep Learning Image Processing 3D Image Segmentation Medical Image Analysis U-Net UNETR Swin-Unet

References

  • 1. Rueckert D. and Schnabel J. A., “Model-Based and Data-Driven Strategies in Medical Image Computing,” Proceedings of the IEEE, Vol. 108, Issue 1, Pages 110–124, 2020.
  • 2. Wachinger C., Reuter M. and Klein T., “DeepNAT: Deep convolutional neural network for segmenting neuroanatomy,” NeuroImage, Vol. 170, Pages 434–445, 2018.
  • 3. Litjens G., Kooi T., Bejnordi B. E., Setio A. A. A., Ciompi F., Ghafoorian M., van der Laak J. A. W. M., van Ginneken B. and Sánchez C. I., “A survey on deep learning in medical image analysis,” Medical Image Analysis, Vol. 42, Pages 60–88, 2017.
  • 4. Lecun Y., Bengio Y. and Hinton G., “Deep learning,” Nature , Vol. 521, Issue 7553, Pages 436–444, 2015.
  • 5. Shen D., Wu G. and Suk H. Il, “Deep Learning in Medical Image Analysis,” Annual Review of Biomedical Engineering, Vol. 19, Issue Volume 19, 2017, Pages 221–248, 2017.
  • 6. Greenspan H., Van Ginneken B. and Summers R. M., “Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique,” IEEE Transactions on Medical Imaging, Vol. 35, Issue 5, Pages 1153–1159, 2016.
  • 7. Tajbakhsh N., Jeyaseelan L., Li Q., Chiang J. N., Wu Z. and Ding X., “Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation,” Medical Image Analysis, Vol. 63, Page 101693, 2020.
  • 8. Taha A. A. and Hanbury A., “Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool,” BMC Medical Imaging, Vol. 15, Issue 1, Pages 1–28, 2015.
  • 9. Asgari Taghanaki S., Abhishek K., Cohen J. P., Cohen-Adad J. and Hamarneh G., “Deep semantic segmentation of natural and medical images: a review,” Artificial Intelligence Review, Vol. 54, Issue 1, Pages 137–178, 2021.
  • 10. Pham D. L., Xu C. and Prince J. L., “Current methods in medical image segmentation,” Annual Review of Biomedical Engineering, Vol. 2, Issue 2000, Pages 315–337, 2000.
  • 11. Sharma N., Ray A. K., Shukla K. K., Sharma S., Pradhan S., Srivastva A. and Aggarwal L., “Automated medical image segmentation techniques,” Journal of Medical Physics, Vol. 35, Issue 1, Pages 3–14, 2010.
  • 12. Norouzi A., Rahim M. S. M., Altameem A., Saba T., Rad A. E., Rehman A. and Uddin M., “Medical Image Segmentation Methods, Algorithms, and Applications,” IETE Technical Review, Vol. 31, Issue 3, Pages 199–213, 2014.
  • 13. Ronneberger O., Fischer P. and Brox T., “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9351, Pages 234–241, 2015.
  • 14. Roth H. R., Shen C., Oda H., Sugino T., Oda M., Hayashi Y., Misawa K. and Mori K., “A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11073 LNCS, Pages 417–425, 2018.
  • 15. Hesamian M. H., Jia W., He X. and Kennedy P., “Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges,” Journal of Digital Imaging, Vol. 32, Issue 4, Pages 582–596, 2019.
  • 16. Çiçek Ö., Abdulkadir A., Lienkamp S. S., Brox T. and Ronneberger O., “3D U-net: Learning dense volumetric segmentation from sparse annotation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9901 LNCS, Pages 424–432, 2016.
  • 17. Chen S., Roth H., Dorn S., May M., Cavallaro A., Lell M. M., Kachelrieß M., Oda H., Mori K. and Maier A., “Towards Automatic Abdominal Multi-Organ Segmentation in Dual Energy CT using Cascaded 3D Fully Convolutional Network,” 2017.
  • 18. Isensee F., Jaeger P. F., Kohl S. A. A., Petersen J. and Maier-Hein K. H., “nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation,” Nature Methods, Vol. 18, Issue 2, Pages 203–211, 2021.
  • 19. Hatamizadeh A., Tang Y., Nath V., Yang D., Myronenko A., Landman B., Roth H. R. and Xu D., “UNETR: Transformers for 3D Medical Image Segmentation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022, Pages 574–584.
  • 20. Cao H., Wang Y., Chen J., Jiang D., Zhang X., Tian Q. and Wang M., “Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation,” in European Conference on Computer Vision, 2023, Vol. 13803 LNCS, Pages 205–218.
  • 21. Milletari F., Navab N. and Ahmadi S. A., “V-Net: Fully convolutional neural networks for volumetric medical image segmentation,” Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016, Pages 565–571, 2016.
  • 22. Litjens G., Toth R., van de Ven W., Hoeks C., Kerkstra S., van Ginneken B., Vincent G., Guillard G., Birbeck N., Zhang J., Strand R., Malmberg F., Ou Y., Davatzikos C., Kirschner M., Jung F., Yuan J., Qiu W., Gao Q. et al., “Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge,” Medical Image Analysis, Vol. 18, Issue 2, Pages 359–373, 2014.
  • 23. Chen J., Lu Y., Yu Q., Luo X., Adeli E., Wang Y., Lu L., Yuille A. L. and Zhou Y., “TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation,” 2021.
  • 24. Ma J., Li F. and Wang B., “U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation,” 2024.
  • 25. Xing Z., Ye T., Yang Y., Liu G. and Zhu L., “SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation,” 2024.
  • 26. Zhao MIT A., Balakrishnan MIT G., Durand MIT F., Guttag MIT J. V and Dalca MIT A. V, “Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, Pages 8543–8553.
  • 27. Landman B., Xu Z., Igelsias J., Styner M., Langerak T. and Klein A., “MICCAI multi-atlas labeling beyond the cranial vault-workshop and challenge,” 2015.
  • 28. “MONAI - Home,” Available: https://monai.io/index.html [Accessed: 20 September 2024]
  • 29. Azad R., Heidary M., Yilmaz K., Hüttemann M., Karimijafarbigloo S., Wu Y., Schmeink A. and Merhof D., “Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook,” 2023.
  • 30. Kline D. M. and Berardi V. L., “Revisiting squared-error and cross-entropy functions for training neural network classifiers,” Neural Computing and Applications, Vol. 14, Issue 4, Pages 310–318, 2005.
  • 31. Lin T.-Y., Goyal P., Girshick R., He K. and Dollar P., “Focal Loss for Dense Object Detection,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, Pages 2980–2988.
  • 32. Karimi Monsefi A., Karisani P., Zhou M., Choi S., Doble N., Ji H., Parthasarathy S. and Ramnath R., “Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain,” Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Pages 1348–1359, 2024.
  • 33. Zheng S., Lu J., Zhao H., Zhu X., Luo Z., Wang Y., Fu Y., Feng J., Xiang T., Torr P. H. S. and Zhang L., “Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, Pages 6881–6890.
  • 34. Chen L.-C., Zhu Y., Papandreou G., Schroff F. and Adam H., “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,” arxiv, 2018.
There are 34 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Sezin Barın 0000-0002-0394-2779

Uçman Ergün 0000-0002-9218-2192

Gür Emre Güraksın 0000-0002-1935-2781

Publication Date April 30, 2025
Submission Date October 21, 2024
Acceptance Date March 19, 2025
Published in Issue Year 2025 Volume: 9 Issue: 1

Cite

APA Barın, S., Ergün, U., & Güraksın, G. E. (2025). 3D MEDICAL IMAGE SEGMENTATION WITH DEEP LEARNING METHODS. International Journal of 3D Printing Technologies and Digital Industry, 9(1), 73-91. https://doi.org/10.46519/ij3dptdi.1571288
AMA Barın S, Ergün U, Güraksın GE. 3D MEDICAL IMAGE SEGMENTATION WITH DEEP LEARNING METHODS. IJ3DPTDI. April 2025;9(1):73-91. doi:10.46519/ij3dptdi.1571288
Chicago Barın, Sezin, Uçman Ergün, and Gür Emre Güraksın. “3D MEDICAL IMAGE SEGMENTATION WITH DEEP LEARNING METHODS”. International Journal of 3D Printing Technologies and Digital Industry 9, no. 1 (April 2025): 73-91. https://doi.org/10.46519/ij3dptdi.1571288.
EndNote Barın S, Ergün U, Güraksın GE (April 1, 2025) 3D MEDICAL IMAGE SEGMENTATION WITH DEEP LEARNING METHODS. International Journal of 3D Printing Technologies and Digital Industry 9 1 73–91.
IEEE S. Barın, U. Ergün, and G. E. Güraksın, “3D MEDICAL IMAGE SEGMENTATION WITH DEEP LEARNING METHODS”, IJ3DPTDI, vol. 9, no. 1, pp. 73–91, 2025, doi: 10.46519/ij3dptdi.1571288.
ISNAD Barın, Sezin et al. “3D MEDICAL IMAGE SEGMENTATION WITH DEEP LEARNING METHODS”. International Journal of 3D Printing Technologies and Digital Industry 9/1 (April 2025), 73-91. https://doi.org/10.46519/ij3dptdi.1571288.
JAMA Barın S, Ergün U, Güraksın GE. 3D MEDICAL IMAGE SEGMENTATION WITH DEEP LEARNING METHODS. IJ3DPTDI. 2025;9:73–91.
MLA Barın, Sezin et al. “3D MEDICAL IMAGE SEGMENTATION WITH DEEP LEARNING METHODS”. International Journal of 3D Printing Technologies and Digital Industry, vol. 9, no. 1, 2025, pp. 73-91, doi:10.46519/ij3dptdi.1571288.
Vancouver Barın S, Ergün U, Güraksın GE. 3D MEDICAL IMAGE SEGMENTATION WITH DEEP LEARNING METHODS. IJ3DPTDI. 2025;9(1):73-91.
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