Submit your papersSubmit Now
For Enquiries: [email protected]
IIARD LogoIIARD

Enhanced Swin Transformer-Based Deep Learning Model for Prostate Cancer Segmentation in MP-MRI

Bawa, Mohammed Garba, Yusuf Musa Malgwi, Auta, Ismail Adamu, Habib, Muhammad Jidda

Abstract

Prostate cancer is the leading cause of cancer among men worldwide. Over the years, various machine learning algorithms have been deployed to help in the detection and segmentation of the cancerous regions from the prostate gland in mp-MRI. While CNNs, more specifically the UNet architecture, have become the standard for accurate medical image segmentation, the networks often struggle to capture long-range contextual relationships, which are necessary for differentiating cancerous cells from the anatomically complex prostate structure. This study aims to provide solutions to this limitation by proposing a new deep-learning architecture that combines the strength of the Swin Transformer encoder as a backbone of the network with a U-Net decoder. We trained and evaluated the performance of the model on the Prostate158 dataset. Results from the experiment, after training for 146 epochs, show that the proposed method achieves a mean Dice Similarity Coefficient of 91.21% for lesion segmentation with standard deviation of 0.0812, and a mean Hausdoff Distance (HD) of 6.42mm with a standard deviation of 0.0371. This result confirms that integrating a Swin transformer encoder with UNet decoder presents a powerful direction for medical image analysis, which offers an accurate tool for automated prostate delineation with implications to enhance patient outcomes.

Keywords

Swin transformerCancer segmentationmultiparametric Magnetic Resonance ImagingProstate cancerDeep learning. UNet architecture

References

Abbasi A. A., Hussain L., awan I. A., Abbasi I., Majid A., Nadeem M. S. A. and Chaudhary Q. (2020). Detecting Prostate Cancer using Deep Learning Convolutional Neural Network with Transfer Learning Approach. Cognitive Neurodynamics, https://doi.org/10.1007/s11571-020-09587-5 Agalliu I., Adebiyi A. O., Lounsbury D. W., Popoola O., Jinadu K., Amodu O., Paul S., Adedimeji A. Asuzu C., Asuzu M., Ogunbiyi O. J., Rohan T. and Shittu O. B. (2015). The feasibility of epidemiology research on prostate cancer in African men in Ibadan, Nigeria. BMC Public Health. http://doi.org/10.1186/s12889-015-1754-x Gader T.B.A., Bouslimi Y. and Echi A.K. (2023). Deep learning based localization and segmentation of prostate cancer from mp-MRI. https://doi.org/10.5565/rev/elcvia.1620 Gavade A. B., Nerli R., Kanwal N., Gavade P. A., Pol S. S. and reizvi S.T.V. (2023). Automatic Diagnosis of Prostate Cancer using mpMRI images: A Deep Learning Approach for Clinical Decision Support. Computers. 12,152. https://doi.org/10. 3390/computers12080152 Gavade A.B., Kanwal N., Gavade P.A. and Nerli R. (2024). Enhancing prostate cancer diagnosis with deep learning: A study using mpMRI segmentation and classification. Control Instrumentation System Conference .CISCON 2023. Vol. 1236. https://doi. org/10.1007/978-981-97-5866-1_40 Gillepie D., Kendrick C., Boon I., Rattay T., and Yap M. H. (2021). Deep learning in Magnetic Resonance prostate segmentation: A review and a new perspective. Preprint2021 Isaksson L.J., Pepea M., Summers P., Zafferoni M., Vincini M.G., Corrao G., Mazzola G.C., Rotandi M., Presti G.L., Raimondi S., Gandini S., Volpe S., Haron Z., Alessi S., Pricolo P., Mistretta F.A., Huzzago S., Cattani F., Musi G., Cobelli O.D., Cremonesi M., Orecchia R., Marvaso G., Petralia G. and Jereczek-fossa B.A. (2023). Comparison of automated segmentation techniques for magnetic resonance images of the prostate. BMC Medical Imaging. 23(32). https://doi.org/10.1186/s12880-023- 00974 Jasem J. S., Abdulazeez A. M., and Rasheed H. H. (2024). Prostate Cancer: MRI Image Detection Based on Deep Learning: A Review. Indonesian Journal of Computer Science, 13(3):4160-4179. Mbah-Omeje K. N., Iyake C. A., and Amugashe S. C. (2022). Prostate Cancer Screening in Nigerian Men: Perceived Barriers and Recommendations. International Journal of Advanced Academic Research. 8(10) Odette M. (2024). Segmentation of prostate cancer in MRI using deep learning. Rwnadan Journal of Engineering, Science, Technology and Environment. 6(1).https://doi. org/10.4315/rjeste.v6i1.4 Ren C., Guo Z., Re H., Jeon D., Kim D., Zhang S., Wang J. and Zhang G. (2023). Prostate segmentation in MRI using transformer encoder and decoder framework. IEEE Access. Vol. 11. https://doi.org/10.1109/ACCESS.2023.3313420 Shukla T.D., Kaplana K., Gupta R., Kalpanadevi D., Walid M.A.A. and Kumar K.K. (2023). A novel machine learning algorithm for prostate cancer image segmentation using mpMRI. International Conference on Sustainable Computing and Smart Systems (ICSCSS 2023). IEEE Xplore part number: CFP23DJ3-ART Siegel R. L., Miller K. D., Wagle N. S., and Jemal A. (2023). Cancer Statistics. Surveillance and Health Equity Science, American Cancer Society. https://doi.org /10.3322 /caac.21763 Singh S. K., Sinha A., Singh H., and Mahanti A. (2024). A Novel Deep Learning- based Technique for Detecting Prostate Cancer in MRI Images. Multimedia Tools and Applications, 83:14173-14187. https://doi.org/10.1007/s11042/023- 15793-0 Wen L., Wang S., Pan X. and Liu Y. (2023). iPCa-Net: A CNN-based framework for predicting incidental prostate cancer using mpMRI. Computerized Medical Imaging and Graphics. Vol. 110. https://doi.org/10.1016/j.compmedimag.2023.102309\ Cao H., Wang M., Chen J., Jiang D., and Wang M. (2023). Swin-UNet: UNet-like Pure Transformer for Medical Image Segmentation. European Conference on Computer Vision. pp: 205-218 Chen J., Lu Y., Yu Q., Luo X., Adeli E., Wang Y., Lu L., Yuille A.L., and Zhou Y. (2021). TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. IEEE Transactions on Medical Imaging. 40(10), 2765-2775. doi.org/10.48550/arXiv.2102.04306 Khan Z., Yahya N., Alsaih K., Alhiyali M. I., and Meriaudeu A. F. (2021). Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review. IEEE ACCESS. Volume 9. https://www.doi.org/10.1109/ACCESS.2021.3090825

More Articles from INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY

Advances in Algorithmic Contract Scoring for Pre-Negotiation Yield Optimization and Risk Retention

Author: Ngozi Samuel Uzougbo, Michael Ominyi, Cyril Chimelie Anichukwueze, Blessing, Chika Jones

DevTest flow: Designing a Scalable Continuous Testing Pipeline for High-Velocity Software Delivery

Author: Lawal Ahmed Oladimeji, Achori Busayo, Akeju BusayoZainab, Saka Samson, Damilare, Mbah Demian Chidi, Runsewe Similoluwa Mayowa, Oladiti Luqman, Abiodun