Prostate lesion segmentation in MR images using radiomics based deeply supervised U-Net

• Autorzy: Hambarde Praful , Talbar Sanjay , Mahajan Abhishek , Chavan Satishkumar , Thakur Meenakshi , Sable Nilesh

Identyfikatory

DOI

10.1016/j.bbe.2020.07.011

• Języki publikacji: EN

Abstrakty

EN

Prostate lesion detection in an axial T2 weighted (T2W) MR images is a very challenging task due to heterogeneous and inconsistent pixel representation surrounding the prostate boundary. In this paper, a radiomics based deeply supervised U-Net is proposed for both prostate gland and prostate lesion segmentation. The proposed pipeline is trained and validated on 1174 and 2071 T2W MR images of 40 patients and tested on 250 and 415 T2W MR images of 10 patients for prostate capsule segmentation and prostate lesion segmentation, respectively. Effective segmentation of prostate lesions in various stages of prostate cancer (namely T1, T2, T3, and T4) is achieved using the proposed framework. The mean Dice Similarity Coefficient (DSC) for actual prostate capsule segmentation and prostate lesion segmentation is 0.8958 and 0.9176, respectively. The proposed framework is also tested on Promise12 public dataset for performance analysis in segmenting prostate gland. The segmentation results using proposed architecture are promising compared to state-of-the-art techniques. It also improves the accuracy of the prostate cancer diagnosis.

Słowa kluczowe

EN

prostate capsule segmentation; prostate lesion segmentation; radiomics; 2D U-Net; convolutional neural network; deep learning

PL

segmentacja prostaty; radiomika; sieć neuronowa konwolucyjna; uczenie głębokie

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 4
• Strony: 1421--1435
• Opis fizyczny: Bibliogr. 70 poz., rys., tab., wykr.

Twórcy

autor

Hambarde Praful

• SGGS Institute of Engineering and Technology, Nanded - 431606, Maharashtra, India

autor

Talbar Sanjay

• SGGS Institute of Engineering and Technology, Nanded - 431606, Maharashtra, India

autor

Mahajan Abhishek

• Tata Memorial Hospital, Parel, Mumbai - 400012, Maharastra, India

autor

Chavan Satishkumar

• Don Bosco Institute of Technology, Kurla (W), Mumbai - 400070, Maharashtra, India

autor

Thakur Meenakshi

• Tata Memorial Hospital, Parel, Mumbai - 400012, Maharastra, India

autor

Sable Nilesh

• Tata Memorial Hospital, Parel, Mumbai - 400012, Maharastra, India

Bibliografia

• [1] Bhavsar A, Verma S. Anatomic imaging of the prostate. BioMed Res Int 2014;2014:9.
• [2] Bray F, Ferlay J, Soerjomataram I, Siege RL, Torre LA, Jema A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J Clin 2018;68:394–424.
• [3] Culp MB, Soerjomataram I, Efstathiou JA, Bray F, Jemal A. Recent global patterns in prostate cancer incidence and mortality rates. Eur Urol 2020;17:38–52.
• [4] Ghose S, Oliver A, Martí R, Lladó X, Vilanova JC, Freixenet J, et al. A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images. Comput Methods Programs Biomed 2012;108:262–87.
• [5] Villeirs GM, Verstraete KL, De Neve WJ, De Meerleer GO. Magnetic resonance imaging anatomy of the prostate and periprostatic area: a guide for radiotherapists. Radiother Oncol 2005;76:99–106.
• [6] Ciresan D, Giusti A, Gambardella LM, Schmidhuber J. Deep neural networks segment neuronal membranes in electron microscopy images. Advances in neural information processing systems. 2012;2843–51.
• [7] Zhang W, Li R, Deng H, Wang L, Lin W, Ji S, et al. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 2015;108:214–24.
• [8] Suk H-I, Lee S-W, Shen D, Initiative ADN, et al. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage 2014;101:569–82.
• [9] Yoo S, Gujrathi I, Haider MA, Khalvati F. prostate cancer detection using deep convolutional neural networks. Sci Rep 2019;9.
• [10] Liao S, Gao Y, Oto A, Shen D. Representation learning: a unified deep learning framework for automatic prostate MR segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2013. p. 254–61.
• [11] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 2017;39:640–51.
• [12] Takeuchi T, Hattori-Kato M, Okuno Y, Iwai S, Mikami K. Prediction of prostate cancer by deep learning with multilayer artificial neural network. Can Urol Assoc J 2019;13:E145.
• [13] Liang F, Li M, Yao L, Wang X, Liu J, Li H, et al. Computer-aided detection for prostate cancer diagnosis based on magnetic resonance imaging: protocol for a systematic review and meta-analysis. Medicine 2019;98.
• [14] Ishioka J, Matsuoka Y, Uehara S, Yasuda Y, Kijima T, Yoshida S, et al. Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm. BJU Int 2018;122:411–7.
• [15] Song Y, Zhang Y-D, Yan X, Liu H, Zhou M, Hu B, et al. Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric mri. J Magn Reson Imaging 2018;48:1570–7.
• [16] Abraham B, Nair MS. Computer-aided diagnosis of clinically significant prostate cancer from mri images using sparse autoencoder and random forest classifier. Biocybern Biomed Eng 2018;38:733–44.
• [17] Abraham B, Nair MS. Automated grading of prostate cancer using convolutional neural network and ordinal class classifier. Inform Med Unlocked 2019;17:100256.
• [18] Yang X, Liu C, Wang Z, Yang J, Le Min H, Wang L, et al. Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric mri. Med Image Anal 2017;42:212–27.
• [19] Dhengre N, Sinha S, Chinni B, Dogra V, Rao N. Computer aided detection of prostate cancer using multiwavelength photoacoustic data with convolutional neural network. Biomed Signal Process Control 2020;60:101952.
• [20] Egevad L, Delahunt B, Berney DM, Bostwick DG, Cheville J, Comperat E, et al. Utility of pathology imagebase for standardisation of prostate cancer grading. Histopathology 2018;73:8–18.
• [21] Ing N, Ma Z, Li J, Salemi H, Arnold C, Knudsen BS, et al. Semantic segmentation for prostate cancer grading by convolutional neural networks. Medical Imaging 2018: Digital Pathology, vol. 10581. International Society for Optics and Photonics; 2018. p. 105811B.
• [22] Arvaniti E, Fricker K, Moret M, et al. Automated gleason grading of prostate cancer tissue microarrays via deep learning. Sci Rep 2018;8:1–11.
• [23] Tirumala S, Narayanan A. Classification and diagnostic prediction of prostate cancer using gene expression and artificial neural networks. Neural Comput Appl 2019;31:7539–48.
• [24] Duda D, Kretowski M, Mathieu R, de Crevoisier R, Bezy- Wendling J. Multi-sequence texture analysis in classification of in vivo mr images of the prostate. Biocybern Biomed Eng 2016;36:537–52.
• [25] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer- Assisted Intervention. Springer; 2015. p. 234–41.
• [26] Vu C, Zamdborg L, Siddiqui Z, Gustafson G, Krauss D, Guerrero T. Automatic segmentation using convolutional neural networks in prostate cancer. Int J Radiat Oncol 2018;102:S61.
• [27] Milletari F, Navab N, Ahmadi S-A. V-net: fully convolutional neural networks for volumetric medical image segmentation. 2016 Fourth International Conference on 3D Vision (3DV); 2016. pp. 565–71.
• [28] Zhu Q, Du B, Turkbey B, Choyke PL, Yan P. Deeply-supervised cnn for prostate segmentation. 2017 International Joint Conference on Neural Networks (IJCNN); 2017. pp. 178–84.
• [29] Yu L, Yang X, Chen H, Qin J, Heng P-A. Volumetric convnets with mixed residual connections for automated prostate segmentation from 3D MR images. Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). 2017. pp. 66–72.
• [30] Meyer A, Mehrtash A, Rak M, Schindele D, Schostak M, Tempany C, et al. Automatic high resolution segmentation of the prostate from multi-planar mri. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). 2018. pp. 177–81. http://dx.doi.org/10.1109/ISBI.2018.8363549.
• [31] Hambarde P, Talbar SN, Sable N, Mahajan A, Chavan SS, Thakur M. Radiomics for peripheral zone and intra-prostatic urethra segmentation in mr imaging. Biomed Signal Process Control 2019;51:19–29.
• [32] Rundo L, Han C, Zhang J, Hataya R, Nagano Y, Militello C, et al. Cnn-based prostate zonal segmentation on T2- weighted MR images: a cross-dataset study; 2019, arXiv:1903.12571 (arXiv preprint).
• [33] Jensen C, Sørensen KS, Jørgensen CK, Nielsen CW, Høy PC, Langkilde NC, et al. Prostate zonal segmentation in 1.5 T and 3T T2W MRI using a convolutional neural network. J Med Imaging 2019;6:014501.
• [34] Zhu Y, Wei R, Gao G, Ding L, Zhang X, Wang X, et al. Fully automatic segmentation on prostate mr images based on cascaded fully convolution network. J Magn Reson Imaging 2019;49:1149–56.
• [35] Kohl S, Bonekamp D, Schlemmer H-P, Yaqubi K, Hohenfellner M, Hadaschik B, et al. Adversarial networks for the detection of aggressive prostate cancer; 2017, arXiv:1702.08014 (arXiv preprint).
• [36] Zhang G, Wang W, Yang D, Luo J, He P, Wang Y, et al. A bi-attention adversarial network for prostate cancer segmentation. IEEE Access 2019;7:131448–58.
• [37] Alkadi R, Taher F, El-baz A, Werghi N. A deep learning-based approach for the detection and localization of prostate cancer in T2 magnetic resonance images. J Digit Imaging 2019;32:793–807.
• [38] Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42:60–88.
• [39] Ghavami N, Hu Y, Gibson E, Bonmati E, Emberton M, Moore CM, et al. Automatic segmentation of prostate MRI using convolutional neural networks: investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration. Med Image Anal 2019;58:101558.
• [40] Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3d u-net: learning dense volumetric segmentation from sparse annotation. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2016. p. 424–32.
• [41] Li W, Wang G, Fidon L, Ourselin S, Cardoso MJ, Vercauteren T. On the compactness, efficiency, and representation of 3d convolutional networks: brain parcellation as a pretext task. International Conference on Information Processing in Medical Imaging. Springer; 2017. p. 348–60.
• [42] Fidon L, Li W, Garcia-Peraza-Herrera LC, Ekanayake J, Kitchen N, Ourselin S, et al. Generalised wasserstein dice score for imbalanced multi-class segmentation using holistic convolutional networks. International MICCAI Brainlesion Workshop. Springer; 2017. p. 64–76.
• [43] Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, et al. Automatic multi-organ segmentation on abdominal ct with dense v-networks. IEEE Trans Med Imaging 2018;37:1822–34.
• [44] Gibson E, Li W, Sudre C, Fidon L, Shakir DI, Wang G, et al. Niftynet: a deep-learning platform for medical imaging. Comput Methods Programs Biomed 2018;158:113–22.
• [45] Ghavami N, Hu Y, Bonmati E, Rodell R, Gibson E, Moore C, et al. Automatic slice segmentation of intraoperative transrectal ultrasound images using convolutional neural networks. Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 10576. International Society for Optics and Photonics; 2018. p. 1057603.
• [46] Ghavami N, Hu Y, Bonmati E, Rodell R, Gibson E, Moore C, et al. Integration of spatial information in convolutional neural networks for automatic segmentation of intraoperative transrectal ultrasound images. J Med Imaging 2018;6:011003.
• [47] Lee DK, Sung DJ, Kim C-S, Heo Y, Lee JY, Park BJ, et al. Three-dimensional convolutional neural network for prostate mri segmentation and comparison of prostate volume measurements by use of artificial neural network and ellipsoid formula. Am J Roentgenol 2020;214:1229–38.
• [48] Wang B, Lei Y, Tian S, Wang T, Liu Y, Patel P, et al. Deeply supervised 3d fully convolutional networks with group dilated convolution for automatic mri prostate segmentation. Med Phys 2019;46:1707–18.
• [49] Sekou TB, Hidane M, Olivier J, Cardot H. From patch to image segmentation using fully convolutional networks-application to retinal images; 2019, arXiv:1904.03892 (arXiv preprint).
• [50] Maggiori E, Tarabalka Y, Charpiat G, Alliez P. Fully convolutional neural networks for remote sensing image classification. IEEE International Geoscience and Remote Sensing Symposium (IGARSS); 2016. pp. 5071–4.
• [51] Tank P, Gest T, Burkel WLWW. Lippincott Williams & Wilkins atlas of anatomy, point (Lippincott Williams and Wilkins) series. Wolters Kluwer Health/Lippincott Williams & Wilkins; 2009.
• [52] Moore KL, Dalley AF, Agur AM. Clinically oriented anatomy. Lippincott Williams & Wilkins; 2013.
• [53] Shaikhibrahim Z, Lindstrot A, Ellinger J, Rogenhofer S, Buettner R, Perner S, et al. The peripheral zone of the prostate is more prone to tumor development than the transitional zone: is the ETS family the key? Mol Med Rep 2012;5:313–6.
• [54] Vargas HA, Akin O, Franiel T, Goldman DA, Udo K, Touijer KA, et al. Normal central zone of the prostate and central zone involvement by prostate cancer: clinical and MR imaging implications. Radiology 2012;262:894–902.
• [55] Lee JJ, Thomas IC, Nolley R, Ferrari M, et al. Biologic differences between peripheral and transition zone prostate cancer. Prostate 2015;75:183–90.
• [56] Barentsz JO, Richenberg J, Clements R, Choyke P, Verma S, Villeirs G, et al. ESUR prostate MR guidelines 2012. Eur Radiol 2012;22:746–57.
• [57] Oto A, Kayhan A, Jiang Y, Tretiakova M, Yang C, Antic T, et al. Prostate cancer: differentiation of central gland cancer from benign prostatic hyperplasia by using diffusion-weighted and dynamic contrast-enhanced MR imaging. Radiology 2010;257:715–23.
• [58] Gillies RJ, Anderson AR, Gatenby RA, Morse DL. The biology underlying molecular imaging in oncology: from genome to anatome and back again. Clin Radiol 2010;65:517–21.
• [59] Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, Van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012;48:441–6.
• [60] Toivonen J, Perez IM, Movahedi P, Merisaari H, Pesola M, Taimen P, et al. Radiomics and machine learning of multisequence multiparametric prostate mri: towards improved non-invasive prostate cancer characterization. Eur J Cancer 2019;14:1–23.
• [61] Shan Yao BS, Hanyu Jiang. Radiomics in prostate cancer: basic concepts and current state-of-the-art. Chin J Acad Radiol 2020;2:47–55.
• [62] Wang J, Perez L. The effectiveness of data augmentation in image classification using deep learning; 2017, Technical report.
• [63] Litjens G, Debats O, Barentsz J, Karssemeijer N, Huisman H. Computer-aided detection of prostate cancer in MRI. IEEE Trans Med Imaging 2014;33:1083–92.
• [64] Barentsz JO, Weinreb JC, Verma S, Thoeny HC, Tempany CM, Shtern F, et al. Synopsis of the PI-RADS v2 guidelines for multiparametric prostate magnetic resonance imaging and recommendations for use. Eur Urol 2016;69:41–9.
• [65] Kingma DP, Ba J. Adam: a method for stochastic optimization. 3rd International Conference for Learning Representations; 2014, arxiv:1412.6980.
• [66] LeCun Y, Bottou L, Orr GB, Müller K-R. Efficient backprop. Neural networks: tricks of the trade. Springer; 1998. p. 9–50.
• [67] Zou KH, Warfield SK, Bharatha A, Tempany CM, Kaus MR, Haker SJ, et al. Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports. Acad Radiol 2004;11:178–89.
• [68] Badrinarayanan V, Kendall A, Cipolla R. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 2017;39:2481–95.
• [69] Litjens G, Toth R, van de Ven W, Hoeks C, Kerkstra S, van Ginneken B, et al. Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge. Med Image Anal 2014;18:359–73.
• [70] Litjens G, et al. The promise12 segmentation challenge; 2012, https://promise12.grand-challenge.org/.