{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:32:34Z","timestamp":1774369954551,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T00:00:00Z","timestamp":1664755200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T00:00:00Z","timestamp":1664755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["Grant No. ZR2019MF003"],"award-info":[{"award-number":["Grant No. ZR2019MF003"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["Grant No. ZR2017MF054"],"award-info":[{"award-number":["Grant No. ZR2017MF054"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant No. 61976126"],"award-info":[{"award-number":["Grant No. 61976126"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s11517-022-02673-2","type":"journal-article","created":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T12:02:46Z","timestamp":1664798566000},"page":"3377-3395","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["MLRD-Net: 3D multiscale local cross-channel residual denoising network for MRI-based brain tumor segmentation"],"prefix":"10.1007","volume":"60","author":[{"given":"Xue","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanjun","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanfei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jindong","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dapeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianming","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,3]]},"reference":[{"issue":"6","key":"2673_CR1","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1097\/00019052-200112000-00002","volume":"14","author":"EC Holland","year":"2001","unstructured":"Holland EC (2001) Progenitor cells and glioma formation. Curr Opin Neurol 14(6):683\u2013688","journal-title":"Curr Opin Neurol"},{"key":"2673_CR2","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.neucom.2020.05.070","volume":"409","author":"L Liu","year":"2020","unstructured":"Liu L, Cheng J, Quan Q, Wu F-X, Wang Yu-Ping, Wang J (2020) A survey on u-shaped networks in medical image segmentations. Neurocomputing 409:244\u2013258","journal-title":"Neurocomputing"},{"issue":"4","key":"2673_CR3","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1002\/jemt.22994","volume":"81","author":"S Iqbal","year":"2018","unstructured":"Iqbal S, Usman Ghani M, Saba T, Rehman A (2018) Brain tumor segmentation in multi-spectral mri using convolutional neural networks (cnn). Microsc Res Tech 81(4):419\u2013427","journal-title":"Microsc Res Tech"},{"issue":"4","key":"2673_CR4","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1007\/s11517-018-1935-8","volume":"57","author":"R Hou","year":"2019","unstructured":"Hou R, Zhou D, Nie R, Liu D, Ruan X (2019) Brain ct and mri medical image fusion using convolutional neural networks and a dual-channel spiking cortical model. Med Biol Eng Compu 57(4):887\u2013900","journal-title":"Med Biol Eng Compu"},{"issue":"9","key":"2673_CR5","doi-asserted-by":"publisher","first-page":"2027","DOI":"10.1007\/s11517-019-02008-8","volume":"57","author":"Y Cui","year":"2019","unstructured":"Cui Y, Zhang G, Liu Z, Xiong Z, Hu J (2019) A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images. Med Biol Eng Compu 57(9):2027\u20132043","journal-title":"Med Biol Eng Compu"},{"issue":"6","key":"2673_CR6","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1007\/s11517-020-02163-3","volume":"58","author":"L Ma","year":"2020","unstructured":"Ma L, Shuai R, Ran X, Liu W, Ye C (2020) Combining dc-gan with resnet for blood cell image classification. Med Biol Eng Compu 58(6):1251\u20131264","journal-title":"Med Biol Eng Compu"},{"issue":"1","key":"2673_CR7","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s11517-019-02066-y","volume":"58","author":"AM Alqudah","year":"2020","unstructured":"Alqudah AM (2020) Aoct-net: A convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images. Med Biol Eng Compu 58(1):41\u201353","journal-title":"Med Biol Eng Compu"},{"issue":"7","key":"2673_CR8","doi-asserted-by":"publisher","first-page":"1495","DOI":"10.1007\/s11517-021-02370-6","volume":"59","author":"A Bal","year":"2021","unstructured":"Bal A, Banerjee M, Chaki R, Sharma P (2021) An efficient brain tumor image classifier by combining multi-pathway cascaded deep neural network and handcrafted features in mr images. Med Biol Eng Compu 59(7):1495\u20131527","journal-title":"Med Biol Eng Compu"},{"key":"2673_CR9","doi-asserted-by":"publisher","first-page":"116459","DOI":"10.1016\/j.neuroimage.2019.116459","volume":"208","author":"M Liu","year":"2020","unstructured":"Liu M, Li F, Yan H, Wang K, Ma Y, Shen L, Xu M, Alzheimer\u2019s D. N. I. et al (2020) A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in alzheimer\u2019s disease. Neuroimage 208:116459","journal-title":"Neuroimage"},{"issue":"10","key":"2673_CR10","doi-asserted-by":"publisher","first-page":"2271","DOI":"10.1109\/TMI.2019.2906727","volume":"38","author":"S Pang","year":"2019","unstructured":"Pang S, Lu Z, Jiang J, Zhao L, Lin L, Li X, Lian T, Huang M, Yang W, Feng Q (2019) Hippocampus segmentation based on iterative local linear mapping with representative and local structure-preserved feature embedding. IEEE Trans Med Imaging 38(10):2271\u20132280","journal-title":"IEEE Trans Med Imaging"},{"issue":"7","key":"2673_CR11","doi-asserted-by":"publisher","first-page":"1597","DOI":"10.1109\/TMI.2018.2791488","volume":"37","author":"H Fu","year":"2018","unstructured":"Fu H, Cheng J, Xu Y, Wong DWK, Liu J, Cao X (2018) Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans Med Imaging 37(7):1597\u20131605","journal-title":"IEEE Trans Med Imaging"},{"key":"2673_CR12","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.patcog.2018.11.009","volume":"88","author":"S Chen","year":"2019","unstructured":"Chen S, Ding C, Liu M (2019) Dual-force convolutional neural networks for accurate brain tumor segmentation. Pattern Recogn 88:90\u2013100","journal-title":"Pattern Recogn"},{"key":"2673_CR13","doi-asserted-by":"publisher","first-page":"101537","DOI":"10.1016\/j.media.2019.101537","volume":"58","author":"X Zhuang","year":"2019","unstructured":"Zhuang X, Li L, Payer C, \u0160tern D, Urschler M, Heinrich MP, Oster J, Wang C, Smedby \u00d6, Bian C et al (2019) Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge. Med Image Anal 58:101537","journal-title":"Med Image Anal"},{"key":"2673_CR14","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.media.2018.01.004","volume":"45","author":"X Li","year":"2018","unstructured":"Li X, Dou Q, Chen H, Fu C-W, Qi X, Belavy\u0300 DL, Armbrecht G, Felsenberg D, Zheng G, Heng P-A (2018) 3d multi-scale fcn with random modality voxel dropout learning for intervertebral disc localization and segmentation from multi-modality mr images. Med Image Anal 45:41\u201354","journal-title":"Med Image Anal"},{"key":"2673_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2018.07.002","volume":"49","author":"Y Hu","year":"2018","unstructured":"Hu Y, Modat M, Gibson E, Li W, Ghavami N, Bonmati E, Wang G, Bandula S, Moore CM, Emberton M et al (2018) Weakly-supervised convolutional neural networks for multimodal image registration. Med Image Anal 49:1\u201313","journal-title":"Med Image Anal"},{"issue":"5","key":"2673_CR16","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1007\/s11517-021-02355-5","volume":"59","author":"H Zanddizari","year":"2021","unstructured":"Zanddizari H, Nguyen N, Zeinali B, Morris Chang J (2021) A new preprocessing approach to improve the performance of cnn-based skin lesion classification. Med Biol Eng Compu 59(5):1123\u20131131","journal-title":"Med Biol Eng Compu"},{"key":"2673_CR17","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.media.2017.10.002","volume":"43","author":"X Zhao","year":"2018","unstructured":"Zhao X, Wu Y, Song G, Li Z, Zhang Y, Fan Y (2018) A deep learning model integrating fcnns and crfs for brain tumor segmentation. Med Image Anal 43:98\u2013111","journal-title":"Med Image Anal"},{"key":"2673_CR18","doi-asserted-by":"crossref","unstructured":"Russo C, Liu S, Di Ieva A (2021) Spherical coordinates transformation pre-processing in deep convolution neural networks for brain tumor segmentation in mri. Medical & Biological Engineering & Computing, pp 1\u201314","DOI":"10.1007\/s11517-021-02464-1"},{"issue":"9","key":"2673_CR19","doi-asserted-by":"publisher","first-page":"1683","DOI":"10.1007\/s11517-018-1809-0","volume":"56","author":"M Papadogiorgaki","year":"2018","unstructured":"Papadogiorgaki M, Koliou P, Zervakis ME (2018) Glioma growth modeling based on the effect of vital nutrients and metabolic products. Med Biol Eng Compu 56(9):1683\u20131697","journal-title":"Med Biol Eng Compu"},{"issue":"5","key":"2673_CR20","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1007\/s11517-020-02147-3","volume":"58","author":"AZ Shirazi","year":"2020","unstructured":"Shirazi AZ, Fornaciari E, Bagherian NS, Ebert LM, Koszyca B, Gomez GA (2020) Deepsurvnet: Deep survival convolutional network for brain cancer survival rate classification based on histopathological images. Med Biol Eng Compu 58(5):1031\u20131045","journal-title":"Med Biol Eng Compu"},{"key":"2673_CR21","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.patcog.2019.01.006","volume":"90","author":"Z Wu","year":"2019","unstructured":"Wu Z, Shen C, Hengel AVD (2019) Wider or deeper: Revisiting the resnet model for visual recognition. Pattern Recogn 90:119\u2013133","journal-title":"Pattern Recogn"},{"issue":"6","key":"2673_CR22","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2019","unstructured":"Zhou Z, Siddiquee MdMR, Tajbakhsh N, Liang J (2019) Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging 39(6):1856\u20131867","journal-title":"IEEE Trans Med Imaging"},{"key":"2673_CR23","doi-asserted-by":"publisher","first-page":"101692","DOI":"10.1016\/j.media.2020.101692","volume":"63","author":"M Akil","year":"2020","unstructured":"Akil M, Saouli R, Kachouri R et al (2020) Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy. Med Image Anal 63:101692","journal-title":"Med Image Anal"},{"issue":"4","key":"2673_CR24","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1007\/s11517-019-02111-w","volume":"58","author":"S Huang","year":"2020","unstructured":"Huang S, Lee F, Miao R, Si Q, Lu C, Chen Q (2020) A deep convolutional neural network architecture for interstitial lung disease pattern classification. Med Biol Eng Compu 58(4):725\u2013737","journal-title":"Med Biol Eng Compu"},{"key":"2673_CR25","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2020.10.031","volume":"423","author":"J Sun","year":"2021","unstructured":"Sun J, Peng Y, Guo Y, Li D (2021) Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3d fcn. Neurocomputing 423:34\u201345","journal-title":"Neurocomputing"},{"issue":"3","key":"2673_CR26","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1109\/JBHI.2020.2998146","volume":"25","author":"Z Luo","year":"2020","unstructured":"Luo Z, Jia Z, Yuan Z, Peng J (2020) Hdc-net: Hierarchical decoupled convolution network for brain tumor segmentation. IEEE J Biomed Health Inform 25(3):737\u2013745","journal-title":"IEEE J Biomed Health Inform"},{"key":"2673_CR27","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.neucom.2020.06.078","volume":"412","author":"D Yi","year":"2020","unstructured":"Yi D, Gong L, Zhang M, Li C, Qin Z (2020) A multi-path adaptive fusion network for multimodal brain tumor segmentation. Neurocomputing 412:19\u201330","journal-title":"Neurocomputing"},{"issue":"5","key":"2673_CR28","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1109\/TMI.2018.2878669","volume":"38","author":"J Dolz","year":"2018","unstructured":"Dolz J, Gopinath K, Yuan J, Lombaert H, Desrosiers C, Ayed IB (2018) Hyperdense-net: A hyper-densely connected cnn for multi-modal image segmentation. IEEE Trans Medical Imaging 38(5):1116\u20131126","journal-title":"IEEE Trans Medical Imaging"},{"key":"2673_CR29","doi-asserted-by":"publisher","first-page":"101688","DOI":"10.1016\/j.media.2020.101688","volume":"62","author":"D Bontempi","year":"2020","unstructured":"Bontempi D, Benini S, Signoroni A, Svanera M, Muckli L (2020) Cerebrum: A fast and fully-volumetric convolutional encoder-decoder for weakly-supervised segmentation of brain structures from out-of-the-scanner mri. Med Image Anal 62:101688","journal-title":"Med Image Anal"},{"key":"2673_CR30","doi-asserted-by":"crossref","unstructured":"He H, Yang G, Zhang W, Xiaomei X u, Yang W, Jiang W, Lai X (2021) A deep multi-task learning framework for brain tumor segmentation. Frontiers in Oncology, 11","DOI":"10.3389\/fonc.2021.690244"},{"key":"2673_CR31","doi-asserted-by":"publisher","first-page":"107562","DOI":"10.1016\/j.patcog.2020.107562","volume":"110","author":"D Zhang","year":"2021","unstructured":"Zhang D, Huang G, Zhang Q, Han J, Han J, Yu Y (2021) Cross-modality deep feature learning for brain tumor segmentation. Pattern Recogn 110:107562","journal-title":"Pattern Recogn"},{"key":"2673_CR32","unstructured":"Jie H u, Li S, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141"},{"key":"2673_CR33","doi-asserted-by":"crossref","unstructured":"Wang Q, Banggu W u, Zhu P, Li P, Zuo W, Qinghua H u (2020) Eca-net: efficient channel attention for deep convolutional neural networks, 2020 ieee. In: CVF Conference on computer vision and pattern recognition (CVPR). IEEE","DOI":"10.1109\/CVPR42600.2020.01155"},{"issue":"1","key":"2673_CR34","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/JBHI.2020.2986926","volume":"25","author":"A Sinha","year":"2020","unstructured":"Sinha A, Dolz J (2020) Multi-scale self-guided attention for medical image segmentation. IEEE J Biomed Health Inform 25(1):121\u2013130","journal-title":"IEEE J Biomed Health Inform"},{"issue":"7","key":"2673_CR35","doi-asserted-by":"publisher","first-page":"4681","DOI":"10.1109\/TII.2019.2943898","volume":"16","author":"M Zhao","year":"2019","unstructured":"Zhao M, Zhong S, Fu X, Tang B, Pecht M (2019) Deep residual shrinkage networks for fault diagnosis. IEEE Trans Industr Inf 16(7):4681\u20134690","journal-title":"IEEE Trans Industr Inf"},{"key":"2673_CR36","doi-asserted-by":"publisher","first-page":"114566","DOI":"10.1016\/j.eswa.2021.114566","volume":"170","author":"X Zhou","year":"2021","unstructured":"Zhou X, Li X, Hu K, Zhang Y, Chen Z, Gao X (2021) Erv-net: An efficient 3d residual neural network for brain tumor segmentation. Expert Syst Appl 170:114566","journal-title":"Expert Syst Appl"},{"issue":"12","key":"2673_CR37","doi-asserted-by":"publisher","first-page":"2270","DOI":"10.1016\/j.patcog.2005.01.012","volume":"38","author":"A Jain","year":"2005","unstructured":"Jain A, Nandakumar K, Ross A (2005) Score normalization in multimodal biometric systems. Pattern Recogn 38(12):2270\u20132285","journal-title":"Pattern Recogn"},{"issue":"24","key":"2673_CR38","doi-asserted-by":"publisher","first-page":"7177","DOI":"10.1364\/AO.428465","volume":"60","author":"H Zhao","year":"2021","unstructured":"Zhao H, An J, Mengjie Yu, Lv D, Kuang K, Zhang T (2021) Nesterov-accelerated adaptive momentum estimation-based wavefront distortion correction algorithm. Appl Opt 60(24):7177\u20137185","journal-title":"Appl Opt"},{"issue":"1","key":"2673_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS, Freymann JB, Farahani K, Davatzikos C (2017) Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific Data 4(1):1\u201313","journal-title":"Scientific Data"},{"key":"2673_CR40","unstructured":"Bakas S, Reyes M, Jakab A, Bauer S, Rempfler M, Crimi A, Shinohara RT, Berger C, Ha SM, Rozycki M et al (2018) Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv:1811.02629"},{"issue":"10","key":"2673_CR41","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R et al (2014) The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans Medical Imaging 34(10):1993\u20132024","journal-title":"IEEE Trans Medical Imaging"},{"issue":"3","key":"2673_CR42","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1109\/TCYB.2018.2797905","volume":"49","author":"D Nie","year":"2018","unstructured":"Nie D, Wang L, Adeli E, Lao C, Lin W, Shen D (2018) 3-d fully convolutional networks for multimodal isointense infant brain image segmentation. IEEE Trans Cybern 49(3):1123\u20131136","journal-title":"IEEE Trans Cybern"},{"key":"2673_CR43","doi-asserted-by":"crossref","unstructured":"Zhang W, Yang G, He H, Yang W, Xu X, Liu Y, Lai X (2021) Me-net: Multi-encoder net framework for brain tumor segmentation. International Journal of Imaging Systems and Technology","DOI":"10.1002\/ima.22571"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02673-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02673-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02673-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T10:16:46Z","timestamp":1701166606000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02673-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,3]]},"references-count":43,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["2673"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02673-2","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,3]]},"assertion":[{"value":"26 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}