{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T19:36:09Z","timestamp":1769283369635,"version":"3.49.0"},"reference-count":56,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2023,5,1]]},"DOI":"10.1587\/transinf.2022edp7193","type":"journal-article","created":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T22:25:12Z","timestamp":1682893512000},"page":"1027-1037","source":"Crossref","is-referenced-by-count":3,"title":["3D Multiple-Contextual ROI-Attention Network for Efficient and Accurate Volumetric Medical Image Segmentation"],"prefix":"10.1587","volume":"E106.D","author":[{"given":"He","family":"LI","sequence":"first","affiliation":[{"name":"Graduate School of Information Science and Engineering, Ritsumeikan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yutaro","family":"IWAMOTO","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Engineering, Ritsumeikan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianhua","family":"HAN","sequence":"additional","affiliation":[{"name":"Faculty of Science, Yamaguchi University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lanfen","family":"LIN","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akira","family":"FURUKAWA","sequence":"additional","affiliation":[{"name":"Tokyo Metropolitan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuzo","family":"KANASAKI","sequence":"additional","affiliation":[{"name":"Koseikai Takeda Hospital"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yen-Wei","family":"CHEN","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Engineering, Ritsumeikan University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] Y. Yu, P. Decazes, J. Lapuyade-Lahorgue, I. Gardin, P. Vera, and S. Ruan, \u201cSemi-automatic lymphoma detection and segmentation using fully conditional random fields,\u201d Computerized Medical Imaging and Graphics, vol.70, pp.1-7, 2018. 10.1016\/j.compmedimag.2018.09.001","DOI":"10.1016\/j.compmedimag.2018.09.001"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] L. Wang, D. Li, Y. Zhu, L. Tian, and Y. Shan, \u201cDual super-resolution learning for semantic segmentation,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.3774-3783, 2020. 10.1109\/cvpr42600.2020.00383","DOI":"10.1109\/CVPR42600.2020.00383"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] E. Soodmand, D. Kluess, P.A. Varady, R. Cichon, M. Schwarze, D. Gehweiler, F. Niemeyer, D. Pahr, and M. Woiczinski, \u201cInterlaboratory comparison of femur surface reconstruction from ct data compared to reference optical 3d scan,\u201d Biomedical engineering online, vol.17, no.1, pp.1-10, 2018. 10.1186\/s12938-018-0461-0","DOI":"10.1186\/s12938-018-0461-0"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] I. Mehmood, M. Sajjad, K. Muhammad, S.I.A. Shah, A.K. Sangaiah, M. Shoaib, and S.W. Baik, \u201cAn efficient computerized decision support system for the analysis and 3d visualization of brain tumor,\u201d Multimedia Tools and Applications, vol.78, no.10, pp.12723-12748, 2019. 10.1007\/s11042-018-6027-0","DOI":"10.1007\/s11042-018-6027-0"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] M. Van Eijnatten, R. van Dijk, J. Dobbe, G. Streekstra, J. Koivisto, and J. Wolff, \u201cCt image segmentation methods for bone used in medical additive manufacturing,\u201d Medical engineering &amp; physics, vol.51, pp.6-16, 2018. 10.1016\/j.medengphy.2017.10.008","DOI":"10.1016\/j.medengphy.2017.10.008"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] E. Abdulhay, M.A. Mohammed, D.A. Ibrahim, N. Arunkumar, and V. Venkatraman, \u201cComputer aided solution for automatic segmenting and measurements of blood leucocytes using static microscope images,\u201d J. Med. Syst., vol.42, no.4, pp.1-12, 2018. 10.1007\/s10916-018-0912-y","DOI":"10.1007\/s10916-018-0912-y"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] X. Song, Q. Huang, S. Chang, J. He, and H. Wang, \u201cLossless medical image compression using geometry-adaptive partitioning and least square-based prediction,\u201d Medical &amp; biological engineering &amp; computing, vol.56, no.6, pp.957-966, 2018. 10.1007\/s11517-017-1741-8","DOI":"10.1007\/s11517-017-1741-8"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] Z. Fan, L. Sun, X. Ding, Y. Huang, C. Cai, and J. Paisley, \u201cA segmentation-aware deep fusion network for compressed sensing mri,\u201d Proceedings of the European Conference on Computer Vision (ECCV), vol.11210, pp.55-70, 2018. 10.1007\/978-3-030-01231-1_4","DOI":"10.1007\/978-3-030-01231-1_4"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] J. Long, E. Shelhamer, and T. Darrell, \u201cFully convolutional networks for semantic segmentation,\u201d Proceedings of the IEEE conference on computer vision and pattern recognition, pp.3431-3440, 2015. 10.1109\/cvpr.2015.7298965","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] O. Ronneberger, P. Fischer, and T. Brox, \u201cU-net: Convolutional networks for biomedical image segmentation,\u201d International Conference on Medical image computing and computer-assisted intervention, vol.9351, pp.234-241, Springer, 2015. 10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] Z. Zhou, M.M.R. Siddiquee, N. Tajbakhsh, and J. Liang, \u201cUnet++: A nested u-net architecture for medical image segmentation,\u201d in Deep learning in medical image analysis and multimodal learning for clinical decision support, vol.11045, pp.3-11, Springer, 2018. 10.1007\/978-3-030-00889-5_1","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] H. Huang, L. Lin, R. Tong, H. Hu, Q. Zhang, Y. Iwamoto, X. Han, Y.-W. Chen, and J. Wu, \u201cUnet 3+: A full-scale connected unet for medical image segmentation,\u201d ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.1055-1059, IEEE, 2020. 10.1109\/icassp40776.2020.9053405","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"13","unstructured":"[13] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A.L. Yuille, \u201cSemantic image segmentation with deep convolutional nets and fully connected crfs,\u201d arXiv preprint arXiv:1412.7062, 2014."},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A.L. Yuille, \u201cDeeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,\u201d IEEE transactions on pattern analysis and machine intelligence, vol.40, no.4, pp.834-848, 2017. 10.1109\/tpami.2017.2699184","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"15","unstructured":"[15] L.C. Chen, G. Papandreou, F. Schroff, and H. Adam, \u201cRethinking atrous convolution for semantic image segmentation,\u201d arXiv preprint arXiv:1706.05587, 2017."},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, \u201cEncoder-decoder with atrous separable convolution for semantic image segmentation,\u201d Proc. European conference on computer vision (ECCV), pp.801-818, 2018. 10.1007\/978-3-030-01234-2_49","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] Y. Huo, Z. Xu, H. Moon, S. Bao, A. Assad, T.K. Moyo, M.R. Savona, R.G. Abramson, and B.A. Landman, \u201cSynseg-net: Synthetic segmentation without target modality ground truth,\u201d IEEE Trans. Med. Imag., vol.38, no.4, pp.1016-1025, 2018.","DOI":"10.1109\/TMI.2018.2876633"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] J.-Y. Zhu, T. Park, P. Isola, and A.A. Efros, \u201cUnpaired image-to-image translation using cycle-consistent adversarial networks,\u201d Proceedings of the IEEE international conference on computer vision, pp.2223-2232, 2017. 10.1109\/iccv.2017.244","DOI":"10.1109\/ICCV.2017.244"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] T.-Y. Su and Y.-H. Fang, \u201cAutomatic liver and spleen segmentation with ct images using multi-channel u-net deep learning approach,\u201d International Conference on Biomedical and Health Informatics, vol.74, pp.33-41, Springer, 2020. 10.1007\/978-3-030-30636-6_5","DOI":"10.1007\/978-3-030-30636-6_5"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] P.-H. Conze, A.E. Kavur, E.C.-L. Gall, N.S. Gezer, Y.L. Meur, M.A. Selver, and F. Rousseau, \u201cAbdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks,\u201d Artificial Intelligence in Medicine, vol.117, 102109, 2021. 10.1016\/j.artmed.2021.102109","DOI":"10.1016\/j.artmed.2021.102109"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] P. Isola, J.-Y. Zhu, T. Zhou, and A.A. Efros, \u201cImage-to-image translation with conditional adversarial networks,\u201d Proc. IEEE conference on computer vision and pattern recognition, pp.1125-1134, 2017. 10.1109\/cvpr.2017.632","DOI":"10.1109\/CVPR.2017.632"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] H. Li, Y. Iwamoto, X. Han, A. Furukawa, S. Kanasaki, and Y.-W. Chen, \u201cAn efficient and accurate 3d multiple-contextual semantic segmentation network for medical volumetric images,\u201d 2021 43rd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC), pp.3309-3312, IEEE, 2021. 10.1109\/embc46164.2021.9629671","DOI":"10.1109\/EMBC46164.2021.9629671"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] F. Milletari, N. Navab, and S.-A. Ahmadi, \u201cV-net: Fully convolutional neural networks for volumetric medical image segmentation,\u201d 2016 fourth international conference on 3D vision (3DV), pp.565-571, IEEE, 2016. 10.1109\/3dv.2016.79","DOI":"10.1109\/3DV.2016.79"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] \u00d6. \u00c7i\u00e7ek, A. Abdulkadir, S.S. Lienkamp, T. Brox, and O. Ronneberger, \u201c3d u-net: learning dense volumetric segmentation from sparse annotation,\u201d International conference on medical image computing and computer-assisted intervention, vol.9901, pp.424-432, Springer, 2016. 10.1007\/978-3-319-46723-8_49","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] K. Kamnitsas, C. Ledig, V.F.J. Newcombe, J.P. Simpson, A.D. Kane, D.K. Menon, D. Rueckert, and B. Glocker, \u201cEfficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,\u201d Medical image analysis, vol.36, pp.61-78, 2017. 10.1016\/j.media.2016.10.004","DOI":"10.1016\/j.media.2016.10.004"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] R. Br\u00fcgger, C.F. Baumgartner, and E. Konukoglu, \u201cA partially reversible u-net for memory-efficient volumetric image segmentation,\u201d International conference on medical image computing and computer-assisted intervention, vol.11766, pp.429-437, Springer, 2019. 10.1007\/978-3-030-32248-9_48","DOI":"10.1007\/978-3-030-32248-9_48"},{"key":"27","unstructured":"[27] A.N. Gomez, M. Ren, R. Urtasun, and R.B. Grosse, \u201cThe reversible residual network: Backpropagation without storing activations,\u201d Advances in neural information processing systems, vol.30, 2017."},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] M. Pendse, V. Thangarasa, V. Chiley, R. Holmdahl, J. Hestness, and D. DeCoste, \u201cMemory efficient 3d u-net with reversible mobile inverted bottlenecks for brain tumor segmentation,\u201d International MICCAI Brainlesion Workshop, vol.12659, pp.388-397, Springer, 2021. 10.1007\/978-3-030-72087-2_34","DOI":"10.1007\/978-3-030-72087-2_34"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, \u201cMobilenetv2: Inverted residuals and linear bottlenecks,\u201d Proc. IEEE conference on computer vision and pattern recognition, pp.4510-4520, 2018. 10.1109\/cvpr.2018.00474","DOI":"10.1109\/CVPR.2018.00474"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] H.R. Roth, L. Lu, A. Farag, H.-C. Shin, J. Liu, E.B. Turkbey, and R.M. Summers, \u201cDeeporgan: Multi-level deep convolutional networks for automated pancreas segmentation,\u201d International conference on medical image computing and computer-assisted intervention, vol.9349, pp.556-564, Springer, 2015. 10.1007\/978-3-319-24553-9_68","DOI":"10.1007\/978-3-319-24553-9_68"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] H.R. Roth, L. Lu, A. Seff, K.M. Cherry, J. Hoffman, S. Wang, J. Liu, E. Turkbey, and R.M. Summers, \u201cA new 2.5 d representation for lymph node detection using random sets of deep convolutional neural network observations,\u201d International conference on medical image computing and computer-assisted intervention, vol.8673, pp.520-527, Springer, 2014. 10.1007\/978-3-319-10404-1_65","DOI":"10.1007\/978-3-319-10404-1_65"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] D. Nie, L. Wang, E. Adeli, C. Lao, W. Lin, and D. Shen, \u201c3-d fully convolutional networks for multimodal isointense infant brain image segmentation,\u201d IEEE Trans. Cybern., vol.49, no.3, pp.1123-1136, 2019. 10.1109\/tcyb.2018.2797905","DOI":"10.1109\/TCYB.2018.2797905"},{"key":"33","doi-asserted-by":"publisher","unstructured":"[33] F.V. Lijn, T.D. Heijer, M.M.B. Breteler, and W.J. Niessen, \u201cHippocampus segmentation in mr images using atlas registration, voxel classification, and graph cuts,\u201d Neuroimage, vol.43, no.4, pp.708-720, 2008. 10.1016\/j.neuroimage.2008.07.058","DOI":"10.1016\/j.neuroimage.2008.07.058"},{"key":"34","doi-asserted-by":"publisher","unstructured":"[34] P. Aljabar, R.A. Heckemann, A. Hammers, J.V. Hajnal, and D. Rueckert, \u201cMulti-atlas based segmentation of brain images: atlas selection and its effect on accuracy,\u201d Neuroimage, vol.46, no.3, pp.726-738, 2009. 10.1016\/j.neuroimage.2009.02.018","DOI":"10.1016\/j.neuroimage.2009.02.018"},{"key":"35","doi-asserted-by":"publisher","unstructured":"[35] J.R.R. Uijlings, K.E.A. van de Sande, T. Gevers, and A.W.M. Smeulders, \u201cSelective search for object recognition,\u201d International journal of computer vision, vol.104, no.2, pp.154-171, 2013. 10.1007\/s11263-013-0620-5","DOI":"10.1007\/s11263-013-0620-5"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] P. Arbel\u00e1ez, J. Pont-Tuset, J.T. Barron, F. Marques, and J. Malik, \u201cMultiscale combinatorial grouping,\u201d Proceedings of the IEEE conference on computer vision and pattern recognition, pp.328-335, 2014. 10.1109\/cvpr.2014.49","DOI":"10.1109\/CVPR.2014.49"},{"key":"37","doi-asserted-by":"crossref","unstructured":"[37] B. Hariharan, P. Arbel\u00e1ez, R. Girshick, and J. Malik, \u201cHypercolumns for object segmentation and fine-grained localization,\u201d Proceedings of the IEEE conference on computer vision and pattern recognition, pp.447-456, 2015. 10.1109\/cvpr.2015.7298642","DOI":"10.1109\/CVPR.2015.7298642"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] M. Tang, Z. Zhang, D. Cobzas, M. Jagersand, and J.L. Jaremko, \u201cSegmentation-by-detection: a cascade network for volumetric medical image segmentation,\u201d 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp.1356-1359, IEEE, 2018. 10.1109\/isbi.2018.8363823","DOI":"10.1109\/ISBI.2018.8363823"},{"key":"39","doi-asserted-by":"publisher","unstructured":"[39] X. Li, H. Chen, X. Qi, Q. Dou, C.-W. Fu, and P.-A. Heng, \u201cH-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes,\u201d IEEE Trans. Med. Imag., vol.37, no.12, pp.2663-2674, 2018. 10.1109\/tmi.2018.2845918","DOI":"10.1109\/TMI.2018.2845918"},{"key":"40","doi-asserted-by":"publisher","unstructured":"[40] L.R. Dice, \u201cMeasures of the amount of ecologic association between species,\u201d Ecology, vol.26, no.3, pp.297-302, 1945. 10.2307\/1932409","DOI":"10.2307\/1932409"},{"key":"41","doi-asserted-by":"publisher","unstructured":"[41] H. Chen, X. Qi, L. Yu, Q. Dou, J. Qin, and P.-A. Heng, \u201cDcan: Deep contour-aware networks for object instance segmentation from histology images,\u201d Medical image analysis, vol.36, pp.135-146, 2017. 10.1016\/j.media.2016.11.004","DOI":"10.1016\/j.media.2016.11.004"},{"key":"42","unstructured":"[42] O. Oktay, J. Schlemper, L.L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N.Y. Hammerla, B. Kainz, et al., \u201cAttention u-net: Learning where to look for the pancreas,\u201d arXiv preprint arXiv:1804.03999, 2018."},{"key":"43","doi-asserted-by":"publisher","unstructured":"[43] A. Sinha and J. Dolz, \u201cMulti-scale self-guided attention for medical image segmentation,\u201d IEEE J. Biomed. Health Inform., vol.25, no.1, pp.121-130, Jan. 2021. 10.1109\/jbhi.2020.2986926","DOI":"10.1109\/JBHI.2020.2986926"},{"key":"44","doi-asserted-by":"publisher","unstructured":"[44] N. Otsu, \u201cA threshold selection method from gray-level histograms,\u201d IEEE transactions on systems, man, and cybernetics, vol.9, no.1, pp.62-66, 1979. 10.1109\/tsmc.1979.4310076","DOI":"10.1109\/TSMC.1979.4310076"},{"key":"45","doi-asserted-by":"publisher","unstructured":"[45] H. Samet and M. Tamminen, \u201cEfficient component labeling of images of arbitrary dimension represented by linear bintrees,\u201d IEEE transactions on pattern analysis and machine intelligence, vol.10, no.4, pp.579-586, 1988. 10.1109\/34.3918","DOI":"10.1109\/34.3918"},{"key":"46","unstructured":"[46] A. Choromanska, M. Henaff, M. Mathieu, G.B. Arous, and Y. LeCun, \u201cThe loss surfaces of multilayer networks,\u201d Artificial intelligence and statistics, pp.192-204, PMLR, 2015."},{"key":"47","doi-asserted-by":"crossref","unstructured":"[47] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016. 10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"48","doi-asserted-by":"publisher","unstructured":"[48] N. Ibtehaz and M.S. Rahman, \u201cMultiresunet: Rethinking the u-net architecture for multimodal biomedical image segmentation,\u201d Neural Networks, vol.121, pp.74-87, 2020. 10.1016\/j.neunet.2019.08.025","DOI":"10.1016\/j.neunet.2019.08.025"},{"key":"49","unstructured":"[49] D.P. Kingma and J. Ba, \u201cAdam: A method for stochastic optimization,\u201d arXiv preprint arXiv:1412.6980, 2014."},{"key":"50","doi-asserted-by":"publisher","unstructured":"[50] C. Dong, Y.w. Chen, A.H. Foruzan, L. Lin, X.h. Han, T. Tateyama, X. Wu, G. Xu, and H. Jiang, \u201cSegmentation of liver and spleen based on computational anatomy models,\u201d Computers in biology and medicine, vol.67, pp.146-160, 2015. 10.1016\/j.compbiomed.2015.10.007","DOI":"10.1016\/j.compbiomed.2015.10.007"},{"key":"51","doi-asserted-by":"publisher","unstructured":"[51] H. Huang, H. Zheng, L. Lin, M. Cai, H. Hu, Q. Zhang, Q. Chen, Y. Iwamoto, X. Han, Y.-W. Chen, et al., \u201cMedical image segmentation with deep atlas prior,\u201d IEEE Trans. Med. Imag., vol.40, no.12, pp.3519-3530, 2021. 10.1109\/tmi.2021.3089661","DOI":"10.1109\/TMI.2021.3089661"},{"key":"52","unstructured":"[52] P. Bilic, P.F. Christ, E. Vorontsov, G. Chlebus, H. Chen, Q. Dou, C.W. Fu, X. Han, P.A. Heng, J. Hesser, et al., \u201cThe liver tumor segmentation benchmark (lits),\u201d arXiv preprint arXiv:1901.04056, 2019."},{"key":"53","doi-asserted-by":"publisher","unstructured":"[53] K. McGuinness and N.E. O&apos;connor, \u201cA comparative evaluation of interactive segmentation algorithms,\u201d Pattern Recognition, vol.43, no.2, pp.434-444, 2010. 10.1016\/j.patcog.2009.03.008","DOI":"10.1016\/j.patcog.2009.03.008"},{"key":"54","doi-asserted-by":"publisher","unstructured":"[54] H. Chen, Q. Dou, L. Yu, J. Qin, and P.A. Heng, \u201cVoxresnet: Deep voxelwise residual networks for brain segmentation from 3d mr images,\u201d NeuroImage, vol.170, pp.446-455, 2018. 10.1016\/j.neuroimage.2017.04.041","DOI":"10.1016\/j.neuroimage.2017.04.041"},{"key":"55","doi-asserted-by":"publisher","unstructured":"[55] B. Wang, Y. Lei, S. Tian, T. Wang, Y. Liu, P. Patel, A.B. Jani, H. Mao, W.J. Curran, T. Liu, and X. Yang, \u201cDeeply supervised 3d fully convolutional networks with group dilated convolution for automatic mri prostate segmentation,\u201d Medical physics, vol.46, no.4, pp.1707-1718, 2019. 10.1002\/mp.13416","DOI":"10.1002\/mp.13416"},{"key":"56","unstructured":"[56] K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d arXiv preprint arXiv:1409.1556, 2014."}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/5\/E106.D_2022EDP7193\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T04:18:16Z","timestamp":1683346696000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/5\/E106.D_2022EDP7193\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,1]]},"references-count":56,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2022edp7193","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,1]]},"article-number":"2022EDP7193"}}