3D kidney and tumor segmentation with Multi Depth V-Net model

Year 2020, Volume: 12 Issue: 3, 35 - 41, 31.12.2020

Fuat Türk , Murat Lüy , Necaattin Barışçı

https://doi.org/10.29137/umagd.831506

Abstract

Kidney cancer is an important type of cancer that spreads rapidly today. Although many treatment methods for kidney cancer have been developed in recent years, current studies are still ongoing. These studies enable treatment information that offers new hope to the lives of kidney cancer patients. When the studies are examined, it seems to be an important alternative in medical segmentation. Although the disease can progress insidiously, sometimes patients may not even have a serious complaint until the last stage. Therefore, segmentation is important for early diagnosis and diagnosis. In this study, it has been prepared in mind in order to help physicians. Here, successful results were obtained by making improvements on the Multi Depth V-Net model. The membrane coefficient of 0.949 and 0.944 for Multi Depth V-Net model and V-Net model kidney segmentation, and 0.841 and 0.830 for tumor segmentation, respectively. In line with the data obtained, we can say that V-Net models for kidney and tumor segmentation can be applied and give accurate results.

Keywords

v-net model, kidney tumor, kidney cancer, deep learning

Multi Depth V-Net model ile 3 boyutlu böbrek ve tümör segmentasyonu

Abstract

Böbrek kanseri günümüzde hızla yayılan önemli bir kanser türüdür. Son yıllarda, böbrek kanseri için birçok tedavi yöntemi geliştirilmekle birlikte mevcut çalışmalar halen devam etmektedir. Bu çalışmalar, böbrek kanseri hastalarının hayatlarına yeni bir umut sunan tedavi bilgilerini mümkün kılmaktadır. Çalışmalar incelendiğinde tıbbi segmentasyonda önemli bir alternatif gibi gözükmektedir. Hastalık sinsi ilerleyebilmekle beraber bazen son evreye kadar hastalarda ciddi bir şikâyet bile olmayabilir. Bu yüzden segmentasyon erken tanı ve teşhis için önem arz etmektedir. Bu çalışmada da hekimlere yardımcı olabilmek amacıyla düşünülerek hazırlanmıştır. Burada Multi Depth V-Net modeli üzerinde iyileştirmeler yapılarak başarılı sonuçlar elde edilmiştir. Multi Depth V-Net model ve V-Net model böbrek segmentasyonu için sırasıyla 0,949 ve 0,944 zar katsayısı, tümör segmentasyonu için de 0,841 ve 0,830 zar katsayısına ulaşmıştır. Elde edilen veriler doğrultusunda böbrek ve tümör segmentasyonu için V-Net modellerin uygulanabilir ve doğru sonuçlar verebildiğini söyleyebiliriz.

Keywords

derin öğrenme, böbrek kanseri, böbrek tümörü, v-net model

References

• Cancer Imaging Archive. Retrieved from.https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=61081171 , Ağustos, 2020.
• Chen,S., Holger,R. Hirohisa,O., Masahiro, H., Yuichiro,,M., Kazunari, M.(2018). “On the influence of Dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks”. Open access: arXiv:1801.05912v1.
• Chengqin Ye; Wei Wang; Shanzhuo Zhang; Kuanquan Wang. (2019). Multi-Depth Fusion Network for Whole Heart CT Image Segmentation.(IEEE).
• Chow, WH., Devesa, SS., Warren, JL., et al. (1999). Rising incidence of renal cell cancer in the United States”, JAMA ,281:1628-31.
• G. Larsson, M. Maire, and G. Shakhnarovich. (May 2016). ‘‘FractalNet: Ultra-deep neural networks without residuals.’’ [Online]. Available: https://arxiv.org/abs/1605.07648.
• Heller N., Isensee, F.,Klaus, H., et al., “The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge”, 2019.
• Heller, N., Sathianathen, N., Kalapara, A., et al., (2019).” The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context”, CT Semantic Segmentations, and Surgical Outcomes, Minnesota University.
• Isensee, F.; Jäger, P.F.; Kohl, S. A.; Petersen, J.; Maier-Hein, K.H.(2019). Automated Design of Deep Learning Methods for Biomedical Image Segmentation, Open Access: arXiv preprint arXiv:1904.08128.
• Jemal, A., Siegel, R., Xu, J, Ward, E. (2010). “Cancer statistics”, CA Cancer J Clin. Vol. 60: pp. 277–300.
• Li, C.; Tan, Y.; Chan, W.; Luo, X.; Yulin, H.; Gao, Y.; Li, F. (2020). ANU-Net: Attention- based Nested U-Net To exploit full resolution features for medical image segmentation. Computers & Graphics, 90, 11-20. [CroosRef]
• Milletari F., Navab, N., Ahmadi, S.A.(2016). “V-net: fully convolutional neural networks for volumetric medical image segmentation”. In: 2016 Fourth International Conference on 3D Vision (3DV), IEEE, pp. 565–571.
• Nithya,A.; Appathurai, A.; Venkatadric, N.; Ramjia, D.R.; Anna Palagan, C, (2020). Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images, [CroosRef]
• Ronneberger O., Fischer P., Brox T. (2015).” U-net: Convolutional networks for biomedical image segmentation”, In: Medical Image Computing and Computer-Assisted Intervention–MICCAI, pp. 234–241.
• Sudre, C.H., Li W., Vercauteren, T., Ourselin, S., Cardoso, M.J. (2017). “Generalized Dice overlap as a deep learning loss function for highly unbalanced segmentations”, Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. DLMIA, Lecture Notes in Computer Science, vol.10553, pp.240–248.
• Turk F., Lüy M., Barişçi N. (2020). Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model. Mathematics 2020, 8(10), 1772. [CroosRef]
• Zeiler M.D., Fergus R., (2014). “Visualizing and understanding convolutional networks”. In: Computer vision–ECCV, pp. 818–833.
• Zhao,W.; Jiang, D.; Queralta, J.P.; Westerlund, T. (2020). MSS U-Net: 3D segmentation of kidneys and tumors from CT images with a multi-scale supervised U-Net Informatics in Medicine, 19, 100357. [CroosRef]