The grade and stage of bladder tumors is an essential key for diagnosing and treating bladder cancer. This study proposed an automated bladder tumor prediction system to automatically assess the bladder tumor grade and stage automatically on Magnetic Resonance Imaging (MRI) images. The system included three modules: tumor segmentation, feature extraction and prediction. We proposed a U-ResNet network that automatically extracts morphological and texture features for detecting tumor regions. These features were used in support vector machine (SVM) classifiers to predict the grade and stage. Our proposed method segmented the tumor area and predicted the grade and stage more accurately compared to different methods in our experiments on MRI images. The accuracy of bladder tumor grade prediction was about 70%, and the accuracy of the data set was about 77.5%. The extensive experiments demonstrated the usefulness and effectiveness of our method.
BrayF., FerlayJ., SoerjomataramI., SiegelR.L., TorreL.A. and JemalA., Global cancer statistics: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA: A Cancer Journal for Clinicians68(6) (2018), 394–424. doi: 10.3322/caac.21492
2.
MagersM.J., Lopez-BeltranA., MontironiR., WilliamsonS.R., KaimakliotisH.Z. and ChengL., Staging of bladder cancer, Histopathology74(1) (2019), 112–134. doi: 10.1111/his.13734
3.
RobinsonS., et al., A comparison between computerised tomography and magnetic resonance imaging in the primary staging of bladder cancer as compared to final histology, Journal of Clinical Urology12(1) (2019), 5–14. doi: 10.1177/2051415818785505
4.
RazzakM.I., NazS. and ZaibA., Deep Learning for Medical Image Processing: Overview, Challenges and the Future, in Classification in Bio Apps: Automation of Decision Making, N. Dey, A.S. Ashour and S. Borra Eds. Cham: Springer International Publishing (2018), 323–350.
5.
MasoodS., SharifM., MasoodA., YasminM. and RazaM., A Survey on Medical Image Segmentation, Current Medical Imaging Reviews (2015).
6.
LitjensG., et al., A survey on deep learning in medical image analysis, Medical Image Analysis42 (2017), 60–88. doi: https://doi.org/10.1016/j.media.2017.07.005.
7.
HuangW.B., YangY. and WangY.J., A Survey on Medical Image Segmentation Methods, Journal of Changchun Normal University (2013).
8.
XiaoD., et al., 3D detection and extraction of bladder tumors via MR virtual cystoscopy, International Journal of Computer Assisted Radiology and Surgery11(1) (2016), 89–97. doi: 10.1007/s11548-015-1234-x
9.
SimõesR., van Cappellen van WalsumA.-M. and SlumpC.H., Classification and localization of early-stage Alzheimer’s disease in magnetic resonance images using a patch-based classifier ensemble, Neuroradiology56(9) (2014), 709–721. doi: 10.1007/s00234-014-1385-4
KrizhevskyA., SutskeverI. and HintonG., ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems25(2) (2012).
12.
HwangI.-p., et al., Persistent Pure Ground-Glass Nodules Larger Than 5 mm: Differentiation of Invasive Pulmonary Adenocarcinomas From Preinvasive Lesions or Minimally Invasive Adenocarcinomas Using Texture Analysis, Investigative Radiology50(11) (2015), 798–804. doi: 10.1097/rli.0000000000000186
13.
CorreasJ.-M., et al., Prostate Cancer: Diagnostic Performance of Real-time Shear-Wave Elastography, Radiology275(1) (2015), 280–289. doi: 10.1148/radiol.14140567
14.
ChaK.H., HadjiiskiL., SamalaR.K., ChanH.-P., CaoiliE.M. and CohanR.H., Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets, Medical Physics43(4) (2016), 1882–1896. doi: 10.1118/1.4944498
15.
BadrinarayananV., KendallA. and CipollaR., SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence39(12) (2017), 2481–2495. doi: 10.1109/TPAMI.2016.2644615
16.
ChiJ.W., BradyM., MooreN.R. and SchnabelJ.A., Segmentation of the bladder wall using coupled level set methods, in, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro2011 (2011), 1653–1656. doi: 10.1109/ISBI.2011.5872721
17.
ZhouZ., Rahman SiddiqueeM.M., TajbakhshN. and LiangJ., UNet++: A Nested U-Net Architecture for Medical Image Segmentation, in, Springer International Publishing2018, 3–11.
18.
Zahangir AlomM., HasanM., YakopcicC., TahaT.M. and AsariV.K., Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation, arXiv e-prints, p. arXiv:1802.06955. [Online]. Available: https://ui.adsabs.harvard.edu/abs/2018arXiv180206955Z.
19.
VuolaA.O., AkramS.U. and KannalaJ., Mask-RCNN and U-Net Ensembled for Nuclei Segmentation, in 2019 IEEE 16th International SymposiumonBiomedical Imaging (ISBI 2019) (2019), 208–212. doi:10.1109/ISBI.2019.8759574
20.
RonnebergerO., FischerP. and BroxT., U-Net: Convolutional Networks for Biomedical Image Segmentation, in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Cham, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, Eds., 2015 Springer International Publishing 234–241.
21.
IsenseeF., et al., Abstract: nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation, in Bildverarbeitung für dieMedizin 2019, Wiesbaden, H. Handels, T. M. Deserno, A. Maier, K. H. Maier-Hein, C. Palm, and T. Tolxdorff, Eds., 2019: Springer Fachmedien Wiesbaden 22–22.
22.
IbtehazN. and RahmanM.S., MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation, Neural Networks121 (2020), 74–87. doi: https://doi.org/10.1016/j.neunet.2019.08.025
23.
LiQ., FanS. and ChenC., An Intelligent Segmentation and Diagnosis Method for Diabetic Retinopathy Based on Improved U-NET Network, Journal of Medical Systems43(9) (2019), 304. doi: 10.1007/s10916-019-1432-0
24.
DongX., et al., Automatic multiorgan segmentation in thorax CT images using U-net-GAN, (in eng), Med Phys46(5) (2019), 2157–2168. doi: 10.1002/mp.13458
25.
LiX., ChenH., QiX., DouQ., FuC.W. and HengP.A., H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes, IEEE Transactions on Medical Imaging37(12) (2018), 2663–2674. doi: 10.1109/TMI.2018.2845918
26.
HeK., ZhangX., RenS. and SunJ., Deep Residual Learning for Image Recognition, in, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2016 (2016), 770–778. doi: 10.1109/CVPR.2016.90
27.
RanjanR., PatelV.M. and ChellappaR., HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence41(1) (2019), 121–135. doi: 10.1109/TPAMI.2017.2781233
28.
WangF., et al., Residual Attention Network for Image Classification, in, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2017 (2017), 6450–6458. doi: 10.1109/CVPR.2017.683
29.
TaiY., YangJ. and LiuX., Image Super-Resolution via Deep Recursive Residual Network, in, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2017 (2017), 2790–2798. doi: 10.1109/CVPR.2017.298
30.
XiL., YunbinC. and RadiologyD.o., Research Progress of CT Image Texture Analysis of Lung Cancer, Medical Recapitulate (2019).
31.
MuratH., ZhouJ., YanC., LiL.I., ChenJ. and YantingH.U., Feature Extraction and Analysis on CT Image of Xinjiang Local Liver Hydatid by Using Gray-scale Histograms, Science & Technology Review30(6) (2012), 66–70.
32.
JiaoP.P., GuoY.Z., LiuL.J. and WeiX., Implementation of Gray Level Co-occurrence Matrix Texture Feature Extraction Using Matlab, Computer Technology & Development (2012).
33.
ChenX., ZargariA., HollingsworthA.B., LiuH., ZhengB. and QiuY., Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer, Computer Methods and Programs in Biomedicine179 (2019), 104995. doi: https://doi.org/10.1016/j.cmpb.2019.104995.
34.
SandrasegaranK., LinY., Asare-SawiriM., TaiyiniT. and TannM., CT texture analysis of pancreatic cancer, European Radiology29(3) (2019), 1067–1073. doi: 10.1007/s00330-018-5662-1
35.
TianQ., et al., Radiomics strategy for glioma grading using texture features from multiparametric MRI, Journal of Magnetic Resonance Imaging48(6) (2018), 1518–1528. doi: https://doi.org/10.1002/jmri.26010
36.
DeepakS. and AmeerP.M., Automated Categorization of Brain Tumor from MRI Using CNN features and SVM, Journal of Ambient Intelligence and Humanized Computing (2020). doi:10.1007/s12652-020-02568-w
37.
VinothR. and VenkateshC., Segmentation and Detection of Tumor in MRI images Using CNN and SVM Classification, in 2018 Conference on Emerging Devices and Smart Systems (ICEDSS) (2018), 21–25. doi: 10.1109/ICEDSS.2018.8544306
38.
YanF., et al., Feature extraction and analysis on X-ray image of Xinjiang Kazak Esophageal cancer by using gray-level histograms, in, 2013 IEEE International Conference on Medical Imaging Physics and Engineering2013 (2013), 61–65. doi: 10.1109/ICMIPE.2013.6864504
39.
GarapatiS.S., et al., Urinary bladder cancer staging in CT urography using machine learning, Medical Physics44(11) (2017), 5814–5823. doi: 10.1002/mp.12510
40.
BertelsJ., et al., Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice, in Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, Cham, D. Shen et al., Eds., 2019// 2019: Springer International Publishing, 92–100.
41.
ChaK.H., et al., Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network-A Pilot Study, (in eng), Tomography2(4) (2016), 421–429. doi: 10.18383/j.tom.2016.00184
42.
HadjiiskiL., ChanH.-P., CaoiliE.M., CohanR.H., WeiJ. and ZhouC., Auto-Initialized Cascaded Level Set (AI-CALS) Segmentation of Bladder Lesions on Multidetector Row CT Urography, Academic Radiology20(2) (2013), 148–155. doi: https://doi.org/10.1016/j.acra.2012.08.012
