NEPHROBLASTOMA ANALYSIS IN MRI IMAGES
DOI:
https://doi.org/10.5566/ias.2000Keywords:
Continuous Max-Flow, Graph Segmentation, Kernel Induced Space, MRI images, Nephroblastoma, Wilms tumourAbstract
The annotation of the tumour from medical scans is a crucial step in nephroblastoma treatment. Therefore, an accurate and reliable segmentation method is needed to facilitate the evaluation and the treatments of the tumour. The proposed method serves this purpose by performing the segmentation of nephroblastoma in MRI scans. The segmentation is performed by adapting and a 2D free hand drawing tool to select a region of interest in the scan slices. Results from 24 patients show a mean root-mean-square error of 0.0481 ± 0.0309, an average Dice coefficient of 0.9060 ± 0.0549 and an average accuracy of 99.59% ± 0.0039. Thus the proposed method demonstrated an effective agreement with manual annotations.
References
Adams R, Bischof L (1994). Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16:641–7.
Amiri S, Rekik I, Mahjoub MA (2016). Deep random forest-based learning transfer to svm for brain tumor segmentation. In: 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP).
Bauer S, Fejes T, Slotboom J, Wiest R, Nolte LP, Reyes M (2012). Segmentation of brain tumor images based on integrated hierarchical classification and regularization. In: MICCAI BraTS Workshop. Nice: Miccai Society.
Bauer S, Wiest R, Nolte LP, Reyes M (2013a). A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58:R97–129.
Bauer S, Wiest R, Nolte LP, Reyes M (2013b). A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58:R97–129.
Boykov Y, Funka-Lea G (2006). Graph cuts and efficient nd image segmentation. Int J Comput Vis 70:109–31.
Boykov YY, Jolly MP (2001). Interactive graph cuts for optimal boundary & region segmentation of objects in nd images. In: Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, vol. 1. IEEE.
Chen X, Udupa JK, Bagci U, Zhuge Y, Yao J (2012). Medical image segmentation by combining graph cuts and oriented active appearance models. IEEE Trans Image Process 21:2035–46.
Conze P, Noblet V, Rousseau F, Heitz F, Memeo R, Pessaux P (2016). Random forests on hierarchical multi-scale supervoxels for liver tumor segmentation in dynamic contrast-enhanced ct scans. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).
David R, Graf N, Karatzanis I, Stenzhorn H, Manikis G, Sakkalis V, Stamatakos G, Marias K (2012). Clinical evaluation of DoctorEye platform in nephroblastoma. Proceedings of the 2012 5th International Advanced Research Workshop on In Silico Oncology and Cancer Investigation The TUMOR Project Workshop IARWISOCI 2012.
Esneault S, Hraiech N, Delabrousse E ́, Dillenseger JL (2007). Graph cut liver segmentation for interstitial ultrasound therapy. Annual International Conference of the IEEE Engineering in Medicine and Biology Proceedings :5247–50.
Farmaki C, Marias K, Sakkalis V, Graf N (2010). Spatially Adaptive Active Contours: A Semi- Automatic Tumor Segmentation Framework. Int J Comput Assist Radiol Surg 5:369–84.
Freedman D, Zhang T (2005). Interactive graph cut based segmentation with shape priors. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1.
Goceri N, Goceri E (2015). A neural network based kidney segmentation from mr images. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).
Gordillo N, Montseny E, Sobrevilla P (2013). State of the art survey on MRI brain tumor segmentation. Magn. Reson. Imaging 31:1426–38.
Gu L, Xu J, Peters TM (2006). Novel multistage three-dimensional medical image segmentation: methodology and validation. IEEE transactions on information technology in biomedicine a publication of the IEEE Engineering in Medicine and Biology Society 10:740–8.
Ju W, Xiang D, Zhang B, Wang L, Kopriva I, Chen X (2015). Random walk and graph cut for co- segmentation of lung tumor on pet-ct images. IEEE Transactions on Image Processing 24:5854– 67.
Kaba D, Wang Y, Wang C, Liu X, Zhu H, Salazar-Gonzalez aG, Li Y (2015). Retina layer segmentation using kernel graph cuts and continuous max-flow. Opt. Express 23:7366–84.
Kainmu ̈ller D, Lange T, Lamecker H (2007). Shape constrained automatic segmentation of the liver based on a heuristic intensity model. In: Proc. MICCAI Workshop 3D Segmentation in the Clinic: A Grand Challenge.
Kim DY, Park JW (2004). Computer-Aided Detection of Kidney Tumor on Abdominal Computed Tomography Scans. Acta Radiol. 45:791–5.
Lee CH, Wang S, Murtha A, Brown MR, Greiner R (2008). Segmenting brain tumors using pseudo— conditional random fields. In: Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention - Part I, MICCAI ’08. Berlin, Heidelberg: Springer- Verlag.
Linguraru MG, Yao J, Gautam R, Peterson J, Li Z, Linehan WM, Summers RM (2009). Renal Tumor Quantification and Classification in Contrast- Enhanced Abdominal CT. Pattern recognition 42:1149–61.
Liu B, Cheng HD, Huang J, Tian J, Tang X, Liu J (2010). Fully automatic and segmentation- robust classification of breast tumors based on local texture analysis of ultrasound images. Pattern Recognition 43:280–98.
Mancas M, Gosselin B (2003). Fuzzy tumor segmentation based on iterative watersheds. Proc of the 14th ProRISC workshop on Circuits Systems and Signal Processing ProRISC 2003 Veldhoven Netherland :5.
McInerney T, Terzopoulos D (1996). Deformable models in medical image analysis: a survey. Med. Image Anal. 1:91–108.
Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber M, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp , Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SMS, Ryan M, Sarikaya D, Schwartz L, Shin H, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Leemput KV (2015). The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans Med Imaging 34:1993–2024.
Pham DL, Xu C, Prince JL (2000). Current Methods in Medical Image Segmentation. Annu Rev Biomed Eng 2:315–37.
Ramrez I, Martn A, Schiavi E (2018). Optimization of a variational model using deep learning: An application to brain tumor segmentation. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
Sachdeva J, Kumar V, Gupta I, Khandelwal N, Ahuja CK (2012). A novel content-based active contour model for brain tumor segmentation. Magn. Reson. Imaging 30:694–715.
Salah MB, Ayed IB, Yuan J, Zhang H (2014). Convex- relaxed kernel mapping for image segmentation. IEEE Transactions on Image Processing 23:1143– 53.
Salah MB, Mitiche A, Ayed IB (2011). Multiregion image segmentation by parametric kernel graph cuts. IEEE Transactions on Image Processing 20:545–57.
Salazar-Gonzalez A, Kaba D, Li Y, Liu X (2014). Segmentation of Blood Vessels and Optic Disc in Retinal Images. EEE J Biomed Health Inform 2194:1–.
Sauwen N, Sima DM, Acou M, Achten E, Maes F, Himmelreich U, Huffel SV (2016). A semi-automated segmentation framework for mri based brain tumor segmentation using regularized nonnegative matrix factorization. In: 2016 12th International Conference on Signal-Image Technology Internet-Based Systems (SITIS).
Shah N, Ziauddin S, Shahid AR (2017). Brain tumor segmentation and classification using cascaded random decision forests. In: 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON).
Somaskandan S, Mahesan S (2012).
based deformable model for segmenting tumors in medical images. In: International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012).
Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010). N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–20.
van der Lijn F, den Heijer T, Breteler MMB, Niessen WJ (2008). Hippocampus segmentation in mr images using atlas registration, voxel classification, and graph cuts. NeuroImage 43:708–20.
Wang T, Cheng I, Basu A (2009). Fluid vector flow and applications in brain tumor segmentation. IEEE Trans. Biomed. Eng. 56:781–9.
Yuan J, Bae E, Tai XC (2010). A study on continuous max-flow and min-cut approaches. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition :2217– 24.
Zhan Y, Shen D (2006). Deformable segmentation of 3-d ultrasound prostate images using statistical texture matching method. IEEE Trans Med Imaging 25:256–72.
Zhong Z, Kim Y, Zhou L, Plichta K, Allen B, Buatti J, Wu X (2018). 3d fully convolutional networks for co-segmentation of tumors on pet-ct images. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
Downloads
Published
Issue
Section
License
Copyright (c) 2019 Image Analysis & Stereology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
