{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T14:11:54Z","timestamp":1774275114773,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,6,3]],"date-time":"2020-06-03T00:00:00Z","timestamp":1591142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100016152","name":"YUTP","doi-asserted-by":"publisher","award":["YUTP-FRG 015LC0-031"],"award-info":[{"award-number":["YUTP-FRG 015LC0-031"]}],"id":[{"id":"10.13039\/501100016152","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we present an evaluation of four encoder\u2013decoder CNNs in the segmentation of the prostate gland in T2W magnetic resonance imaging (MRI) image. The four selected CNNs are FCN, SegNet, U-Net, and DeepLabV3+, which was originally proposed for the segmentation of road scene, biomedical, and natural images. Segmentation of prostate in T2W MRI images is an important step in the automatic diagnosis of prostate cancer to enable better lesion detection and staging of prostate cancer. Therefore, many research efforts have been conducted to improve the segmentation of the prostate gland in MRI images. The main challenges of prostate gland segmentation are blurry prostate boundary and variability in prostate anatomical structure. In this work, we investigated the performance of encoder\u2013decoder CNNs for segmentation of prostate gland in T2W MRI. Image pre-processing techniques including image resizing, center-cropping and intensity normalization are applied to address the issues of inter-patient and inter-scanner variability as well as the issue of dominating background pixels over prostate pixels. In addition, to enrich the network with more data, to increase data variation, and to improve its accuracy, patch extraction and data augmentation are applied prior to training the networks. Furthermore, class weight balancing is used to avoid having biased networks since the number of background pixels is much higher than the prostate pixels. The class imbalance problem is solved by utilizing weighted cross-entropy loss function during the training of the CNN model. The performance of the CNNs is evaluated in terms of the Dice similarity coefficient (DSC) and our experimental results show that patch-wise DeepLabV3+ gives the best performance with DSC equal to     92.8 %    . This value is the highest DSC score compared to the FCN, SegNet, and U-Net that also competed the recently published state-of-the-art method of prostate segmentation.<\/jats:p>","DOI":"10.3390\/s20113183","type":"journal-article","created":{"date-parts":[[2020,6,4]],"date-time":"2020-06-04T04:36:09Z","timestamp":1591245369000},"page":"3183","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2515-4220","authenticated-orcid":false,"given":"Zia","family":"Khan","sequence":"first","affiliation":[{"name":"Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9812-0435","authenticated-orcid":false,"given":"Norashikin","family":"Yahya","sequence":"additional","affiliation":[{"name":"Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6180-1691","authenticated-orcid":false,"given":"Khaled","family":"Alsaih","sequence":"additional","affiliation":[{"name":"Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5615-4629","authenticated-orcid":false,"given":"Syed Saad Azhar","family":"Ali","sequence":"additional","affiliation":[{"name":"Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia"}]},{"given":"Fabrice","family":"Meriaudeau","sequence":"additional","affiliation":[{"name":"ImViA\/ITFIM, University of Burgundy, 21078 Dijon, France"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1002\/ijc.31937","article-title":"Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods","volume":"144","author":"Ferlay","year":"2019","journal-title":"Int. J. Cancer"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"63","DOI":"10.14740\/wjon1191","article-title":"Epidemiology of Prostate Cancer","volume":"10","author":"Rawla","year":"2019","journal-title":"World J. Oncol."},{"key":"ref_3","first-page":"212","article-title":"Emerging role of microRNAs in prostate cancer: Implications for personalized medicine","volume":"9","author":"Gandellini","year":"2010","journal-title":"Discov. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1634\/theoncologist.2007-0139","article-title":"What to do with an abnormal PSA test","volume":"13","author":"Loeb","year":"2008","journal-title":"The Oncologist"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/0009-8981(92)90181-O","article-title":"Development of a method for the purification of human trypsin by single step affinity chromatography suitable for human isotope incorporation studies","volume":"212","author":"Ogden","year":"1992","journal-title":"Clin. Chim. Acta"},{"key":"ref_6","first-page":"330","article-title":"Prostate cancer screening: Exploring the debate","volume":"3","author":"Backer","year":"1999","journal-title":"Permanente J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1097\/MOU.0b013e32835481c2","article-title":"Multiparametric MRI and prostate cancer diagnosis and risk stratification","volume":"22","author":"Turkbey","year":"2012","journal-title":"Curr. Opin. Urol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1007\/s00330-011-2377-y","article-title":"ESUR prostate MR guidelines 2012","volume":"22","author":"Barentsz","year":"2012","journal-title":"Eur. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1097\/00000478-198812000-00001","article-title":"Zonal distribution of prostatic adenocarcinoma. Correlation with histologic pattern and direction of spread","volume":"12","author":"McNeal","year":"1988","journal-title":"Am. J. Surg. Pathol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1148\/radiol.2015142818","article-title":"Prostate cancer: Interobserver agreement and accuracy with the revised prostate imaging reporting and data system at multiparametric MR imaging","volume":"277","author":"Muller","year":"2015","journal-title":"Radiology"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1016\/j.neucom.2017.09.084","article-title":"Atlas registration and ensemble deep convolutional neural network-based prostate segmentation using magnetic resonance imaging","volume":"275","author":"Jia","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fasihi, M.S., and Mikhael, W.B. (2016, January 15\u201317). Overview of current biomedical image segmentation methods. Proceedings of the 2016 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.","DOI":"10.1109\/CSCI.2016.0156"},{"key":"ref_14","first-page":"2","article-title":"Fully automatic segmentation of the prostate using active appearance models","volume":"2012","author":"Vincent","year":"2012","journal-title":"MICCAI Grand Chall. Prostate MR Image Segmentation"},{"key":"ref_15","unstructured":"Kirschner, M., Jung, F., and Wesarg, S. (2012, January 1). Automatic prostate segmentation in MR images with a probabilistic active shape model. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice, France."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cheng, R., Roth, H.R., Lu, L., Wang, S., Turkbey, B., Gandler, W., McCreedy, E.S., Agarwal, H.K., Choyke, P., and Summers, R.M. (2016). Active appearance model and deep learning for more accurate prostate segmentation on MRI. Proc. SPIE, 9784.","DOI":"10.1117\/12.2216286"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1118\/1.3315367","article-title":"Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model","volume":"37","author":"Martin","year":"2010","journal-title":"Med. Phys."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, J., Baig, S., Wong, A., Haider, M.A., and Khalvati, F. (2017, January 2). Segmentation of prostate in diffusion MR images via clustering. Proceedings of the International Conference on Image Analysis and Recognition (ICIAR), Springer, At Montreal, QC, Canada.","DOI":"10.1007\/978-3-319-59876-5_52"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1109\/TMI.2015.2508280","article-title":"Deformable MR prostate segmentation via deep feature learning and sparse patch matching","volume":"35","author":"Guo","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1118\/1.2842076","article-title":"Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information","volume":"35","author":"Klein","year":"2008","journal-title":"Med. Phys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2000","DOI":"10.1109\/TMI.2010.2057442","article-title":"Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (SIMPLE)","volume":"29","author":"Langerak","year":"2010","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Dowling, J.A., Fripp, J., Chandra, S., Pluim, J.P.W., Lambert, J., Parker, J., Denham, J., Greer, P.B., and Salvado, O. (2011, January 22). Fast automatic multi-atlas segmentation of the prostate from 3D MR images. Proceedings of the International Workshop on Prostate Cancer Imaging, Toronto, ON, Canada.","DOI":"10.1007\/978-3-642-23944-1_2"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Litjens, G., Debats, O., van de Ven, W., Karssemeijer, N., and Huisman, H. (2012, January 1\u20135). A pattern recognition approach to zonal segmentation of the prostate on MRI. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Nice, France.","DOI":"10.1007\/978-3-642-33418-4_51"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"021208","DOI":"10.1117\/1.JMI.5.2.021208","article-title":"PSNet: Prostate segmentation on MRI based on a convolutional neural network","volume":"5","author":"Tian","year":"2018","journal-title":"J. Med. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (VOC) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014). How transferable are features in deep neural networks?. Adv. Neural Inf. Process. Syst., 3320\u20133328."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and understanding convolutional networks. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-Net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yu, L., Yang, X., Chen, H., Qin, J., and Heng, P.A. (2017, January 4\u20139). Volumetric convnets with mixed residual connections for automated prostate segmentation from 3D MRI images. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10510"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.neucom.2019.07.006","article-title":"USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets","volume":"365","author":"Rundo","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.neuroimage.2014.12.061","article-title":"Deep convolutional neural networks for multi-modality isointense infant brain image segmentation","volume":"108","author":"Zhang","year":"2015","journal-title":"NeuroImage"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","article-title":"Convolutional neural networks for medical image analysis: Full training or fine tuning?","volume":"35","author":"Tajbakhsh","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","article-title":"Brain tumor segmentation using convolutional neural networks in MRI images","volume":"35","author":"Pereira","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1109\/TMI.2016.2548501","article-title":"Automatic segmentation of MR brain images with a convolutional neural network","volume":"35","author":"Moeskops","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","article-title":"Large scale deep learning for computer aided detection of mammographic lesions","volume":"35","author":"Kooi","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.cviu.2017.04.002","article-title":"Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound","volume":"164","author":"Milletari","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, Y., Ren, Q., Geng, J., Ding, M., and Li, J. (2018). Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images. Sensors, 18.","DOI":"10.3390\/s18103232"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chun, C., and Ryu, S.K. (2019). Road Surface Damage Detection Using Fully Convolutional Neural Networks and Semi-Supervised Learning. Sensors, 19.","DOI":"10.3390\/s19245501"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Islam, M.M.M., and Kim, J.M. (2019). Vision-Based Autonomous Crack Detection of Concrete Structures Using a Fully Convolutional Encoder Decoder Network. Sensors, 19.","DOI":"10.3390\/s19194251"},{"key":"ref_44","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst., 1097\u20131105."},{"key":"ref_45","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhao, X., Yuan, Y., Song, M., Ding, Y., Lin, F., Liang, D., and Zhang, D. (2019). Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Extract Rice Lodging. Sensors, 19.","DOI":"10.3390\/s19183859"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Luo, K., Ma, C., Liu, Q., and Jin, B. (2018). Superpixel Segmentation Based Synthetic Classifications with Clear Boundary Information for a Legged Robot. Sensors, 18.","DOI":"10.3390\/s18092808"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yao, X., Yang, H., Wu, Y., Wu, P., Wang, B., Zhou, X., and Wang, S. (2019). Land Use Classification of the Deep Convolutional Neural Network Method Reducing the Loss of Spatial Features. Sensors, 19.","DOI":"10.3390\/s19122792"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lobo Torres, D., Queiroz Feitosa, R., Nigri Happ, P., Elena Cu La Rosa, L., Marcato Junior, J., Martins, J., Ol Bressan, P., Gonalves, W.N., and Liesenberg, V. (2020). Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery. Sensors, 20.","DOI":"10.3390\/s20020563"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"170124","DOI":"10.1038\/sdata.2017.124","article-title":"The public cancer radiology imaging collections of The Cancer Imaging Archive","volume":"4","author":"Prior","year":"2017","journal-title":"Sci. Data"},{"key":"ref_53","unstructured":"Bloch, N., Madabhushi, A., Huisman, H., Freymann, J., Kirby, J., Grauer, M., Enquobahrie, A., Jaffe, C., Clarke, L., and Farahani, K. (2015). NCI-ISBI 2013 challenge: Automated segmentation of prostate structures. Cancer Imaging Arch., 370."},{"key":"ref_54","unstructured":"Bovik, A.C. (2009). The Essential Guide to Image Processing, Elsevier."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.1002\/mp.13466","article-title":"Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images","volume":"46","author":"Feng","year":"2019","journal-title":"Med. Phys."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). Going deeper with convolutions. arXiv.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1109\/LRA.2019.2896518","article-title":"Normalization in training U-Net for 2-D biomedical semantic segmentation","volume":"4","author":"Zhou","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Du, B., Turkbey, B., Choyke, P.L., and Yan, P. (2017, January 4\u20139). Deeply-supervised CNN for prostate segmentation. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), San Francisco, CA, USA.","DOI":"10.1109\/IJCNN.2017.7965852"},{"key":"ref_59","unstructured":"Sekou, T.B., Hidane, M., Olivier, J., and Cardot, H. (2019). From patch to image segmentation using fully convolutional networks-application to retinal images. arXiv."},{"key":"ref_60","unstructured":"Dhivya, J.J., and Ramaswami, M. (2018, January 6\u20138). A Perusal Analysis on Hybrid Spectrum Handoff Schemes in Cognitive Radio Networks. Proceedings of the International Conference on Intelligent Systems Design and Applications, Vellore, India."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Christ, P.F., Elshaer, M.E.A., Ettlinger, F., Tatavarty, S., Bickel, M., Bilic, P., Rempfler, M., Armbruster, M., Hofmann, F., and D Anastasi, M. (2016, January 17\u201321). Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece.","DOI":"10.1007\/978-3-319-46723-8_48"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.media.2013.12.002","article-title":"Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge","volume":"18","author":"Litjens","year":"2014","journal-title":"Med. Image Anal."},{"key":"ref_63","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3513","DOI":"10.1016\/j.eswa.2009.10.036","article-title":"Expert model for detection of epileptic activity in EEG signature","volume":"37","author":"Gandhi","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_65","unstructured":"Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI), San Francisco, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/11\/3183\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:35:27Z","timestamp":1760175327000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/11\/3183"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,3]]},"references-count":65,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20113183"],"URL":"https:\/\/doi.org\/10.3390\/s20113183","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,3]]}}}