Brain tumor segmentation based on region of interest-aided localization and segmentation U-Net

Shidong Li1, Jianwei Liu2,3, Zhanjie Song2,3
1Department of Mathematics, San Francisco University, San Francisco, USA
2School of Mathematics, Tianjin University, Tianjin, China
3Tianjin Key Laboratory of Brain-Inspired Intelligence Technology, Tianjin, China

Tóm tắt

Since magnetic resonance imaging (MRI) has superior soft tissue contrast, contouring (brain) tumor accurately by MRI images is essential in medical image processing. Segmenting tumor accurately is immensely challenging, since tumor and normal tissues are often inextricably intertwined in the brain. It is also extremely time consuming manually. Late deep learning techniques start to show reasonable success in brain tumor segmentation automatically. The purpose of this study is to develop a new region-of-interest-aided (ROI-aided) deep learning technique for automatic brain tumor MRI segmentation. The method consists of two major steps. Step one is to use a 2D network with U-Net architecture to localize the tumor ROI, which is to reduce the impact of normal tissue’s disturbance. Then a 3D U-Net is performed in step 2 for tumor segmentation within identified ROI. The proposed method is validated on MICCAI BraTS 2015 Challenge with 220 high Gliomas grade (HGG) and 54 low Gliomas grade (LGG) patients’ data. The Dice similarity coefficient and the Hausdorff distance between the manual tumor contour and that segmented by the proposed method are 0.876 ±0.068 and 3.594±1.347 mm, respectively. These numbers are indications that our proposed method is an effective ROI-aided deep learning strategy for brain MRI tumor segmentation, and a valid and useful tool in medical image processing.

Tài liệu tham khảo

Jemal A, Ward Elizabeth M, Johnson Christopher J et al (2017) Annual report to the nation on the status of cancer, 1975–2007, Featuring tumors of the brain and other nervous system. J Natl Cancer Inst 103(9):714–736 Ray S, Bonafede MM, Mohile NA (2014) Treatment patterns, survival, and healthcare costs of patients with malignant gliomas in a large us commercially insured population. Am Health Drug Benefits 7(3):140–149 Ostrom Quinn T, Gittleman H, Truitt G et al (2018) CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2011–2015. Neuro-Oncol 20(4):1–86 Urbanska K, Sokolowska J, Szmidt M et al (2014) Glioblastoma multiforme—an overview. Contemporary Oncol-Termedia 18(5):307–312 Menze BH, Jakab A, Bauer S et al (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993–2024 Angulakshmi M, Lakshmi Priya GG (2017) Automated brain tumour segmentation techniques–a review. Int J Imaging Syst Technol 27(1):66–77 Işιn A, Direkoğlu C, Şah M, (2016) Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Comput Sci 102:317–324 Saman S, Jamjala Narayanan S (2018) Survey on brain tumor segmentation and feature extraction of MR images. Int J Multimed Inf Retriev 8:79–99 Gordillo N, Montseny E, Sobrevilla P (2013) State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging 31(8):1426–1438 Murthy TSD, Sadashivappa G (2014) Brain tumor segmentation using thresholding, morphological operations and extraction of features of tumor. In: 2014 International Conference on Advances in Electronics Computers and Communications, pp 1–6. https://doi.org/10.1109/ICAECC.2014.7002427 Ilhan U, Ilhan A (2017) Brain tumor segmentation based on a new threshold approach. Procedia Comput Sci 120:580–587 Zotin A, Simonov K, Kurako M et al (2018) Edge detection in MRI brain tumor images based on fuzzy C-means clustering. Procedia Comput Sci 126:1261–1270 Aslam A, Khan E, Beg MMS (2015) Improved edge detection algorithm for brain tumor segmentation. Procedia Comput Sci 58:430–437 Wȩgliński T, Fabijańska A (2011) Brain tumor segmentation from MRI data sets using region growing approach, Perspective Technologies and Methods in MEMS Design, pp 185–188 Charutha S, Jayashree MJ (2014) An efficient brain tumor detection by integrating modified texture based region growing and cellular automata edge detection. In: 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp 1193–1199. https://doi.org/10.1109/ICCICCT.2014.6993142 Shanthakumar P, Kumar PG (2015) Computer aided brain tumor detection system using watershed segmentation techniques. Int J Imaging Syst Technol 25(4):297–301 Khan MA, Lali IU, Rehman A et al (2019) Brain tumor detection and classification: a framework of marker-based watershed algorithm and multilevel priority features selection. Microsc Res Tech 82(6):909–922 Bauer S, Nolte L, Reyes M (2011) Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp 2018–2021. https://doi.org/10.1109/ISBI.2011.5872808 Arakeri MP, Reddy GRM (2011) Efficient fuzzy clustering based approach to brain tumor segmentation on MR images. Comput Intell Inf Technol 250:790–795 Vijay J, Subhashini J (2013) An efficient brain tumor detection methodology using K-means clustering algoriftnn. In: 2013 International Conference on Communication and Signal Processing, pp 653–657. https://doi.org/10.1109/iccsp.2013.6577136 Hotelling H (1933) Analysis of a complex of statistical variables into principal components. Br J Educ Psychol 24(6):417–520 Yang L, Xu Z (2019) Feature extraction by PCA and diagnosis of breast tumors using SVM with DE-based parameter tuning. Int J Mach Learn Cybernet 10:591–601 Kapas Z, Lefkovits L, Szilagyi L (2016) Automatic detection and segmentation of brain tumor using random forest approach. Model Decis Artif Intell 9880:301–312 Abdulbaqi HS, Mohd Zubir M, Omar AF et al (2014) Detecting brain tumor in Magnetic Resonance Images using Hidden Markov Random Fields and Threshold techniques. In: 2014 IEEE Student Conference on Research and Development, pp 1–5. https://doi.org/10.1109/SCORED.2014.7072963 Kumar TS, Rashmi K, Ramadoss S et al (2017) Brain tumor detection using SVM classifier. In: 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS), pp 318–323. https://doi.org/10.1109/SSPS.2017.8071613 Luo Y, Yang B, Xu L et al (2018) Segmentation of the left ventricle in cardiac MRI using a hierarchical extreme learning machine model. Int J Mach Learn Cybernet 9:1741–1751 Liu X, Zhu T, Zhai L et al (2019) Mass classification of benign and malignant with a new twin support vector machine joint \(l_{2,1}\)-norm. Int J Mach Learn Cybernet 10:155–171 Milletari F, Navab N, Ahmadi SA (2016) V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp 565–571. https://doi.org/10.1109/3DV.2016.79 Zhang J, Shen X, Zhuo T et al (2017) Brain tumor segmentation based on refined fully convolutional neural networks with a hierarchical dice loss. arXiv:1712.09093 Ben Naceur M, Saouli R, Akil M et al (2018) Fully automatic brain tumor segmentation using end-to-end incremental deep neural networks in MRI images. Comput Method Program Biomed 166:39–49 Chen L, Wu Y, DSouza Adora M et al (2018) MRI tumor segmentation with densely connected 3D CNN. Medical Imaging 2018: Image Processing. vol. 10574. International Society for Optics and Photonics Liu C, Gardner MS, Stephen J, Wen N et al (2019) Automatic segmentation of the prostate on CT images using deep neural networks (DNN). Int J Radiat Oncol Biol Phys 104(4):924–932 Yan K, Wang X, Kim J et al (2019) A propagation-DNN: deep combination learning of multi-level features for MR prostate segmentation. Comput Methods Programs Biomed 170:11–21 Ito R, Nakae K, Hata J et al (2019) Semi-supervised deep learning of brain tissue segmentation. Neural Netw 116:25–34 Feng X, Qing K, Tustison NJ et al (2019) Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images. Med Phys 46(4):2169–2180 Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. Int Conf Med Image Comput Comput-Assist Interv 9351:234–241 Zhou C, Ding C, Wang X et al (2020) One-pass multi-task networks with cross-task guided attention for brain tumor segmentation. IEEE Trans Image Process 29:4516–4529 Kayalibay B, Jensen G, van der Smagt P (2017) CNN-based segmentation of medical imaging data. arXiv:1701.03056 Isensee F, Kickingereder P, Wick W et al (2018) Brain tumor segmentation and radiomics survival prediction: contribution to the brats 2017 challenge. International MICCAI Brainlesion Workshop. Springer, Cham, pp 287–297 Kamnitsas K, Ledig C, Newcombe VFJ et al (2017) Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 36:61–78 Qin Y, Kamnitsas K, Ancha S et al (2018) Autofocus layer for semantic segmentation. International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 603–611 Ranjbarzadeh R, Ghoushchi SJ, Bendechache M et al (2021) Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images. BioMed Res Int 5544742:16. https://doi.org/10.1155/2021/5544742 Zhao W, Vernekohl D, Zhu J et al (2016) A model-based scatter artifacts correction for cone beam CT. Med Phys 43(4):1736–1753 Altunbas C, Lai CJ, Zhong Y et al (2014) Reduction of ring artifacts in CBCT: Detection and correction of pixel gain variations in flat panel detectors. Medical Phys 41(9):091913 Gai S, Zhang B, Yang C et al (2018) Speckle noise reduction in medical ultrasound image using monogenic wavelet and Laplace mixture distribution. Digit Signal Process 72:192–207 Lin JS, Fuentes DT, Chandler A et al (2017) Performance Assessment for Brain MR Imaging Registration Methods. Am J Neuroradiol 38(5):973–980 Padgett KR, Stoyanova R, Pirozzi S et al (2018) Validation of a deformable MRI to CT registration algorithm employing same day planning MRI for surrogate analysis. J Appl Clin Med Phys 19(2):258–264 Wang H, Balter J, Cao Y (2013) Patient-induced susceptibility effect on geometric distortion of clinical brain MRI for radiation treatment planning on a 3T scanner. Phys Med Biol 58(3):465–477 Togo H, Rokicki J, Yoshinaga K et al (2017) Effects of field-map distortion correction on resting state functional connectivity MRI. Front Neurosci 11:656–656 Dou Q, Lequan Yu, Chen H et al (2017) 3D deeply supervised network for automated segmentation of volumetric medical images. Med Image Anal 41:40–54 He K, Gkioxari G, Dollár P et al (2017) Mask R-CNN, 2017 IEEE international conference on computer vision (ICCV), pp 2980–2988