{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T03:32:33Z","timestamp":1771039953338,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>To train an automatic brain tumor segmentation model, a large amount of data is required. In this paper, we proposed a strategy to overcome the limited amount of clinically collected magnetic resonance image (MRI) data regarding meningiomas by pre-training a model using a larger public dataset of MRIs of gliomas and augmenting our meningioma training set with normal brain MRIs. Pre-operative MRIs of 91 meningioma patients (171 MRIs) and 10 non-meningioma patients (normal brains) were collected between 2016 and 2019. Three-dimensional (3D) U-Net was used as the base architecture. The model was pre-trained with BraTS 2019 data, then fine-tuned with our datasets consisting of 154 meningioma MRIs and 10 normal brain MRIs. To increase the utility of the normal brain MRIs, a novel balanced Dice loss (BDL) function was used instead of the conventional soft Dice loss function. The model performance was evaluated using the Dice scores across the remaining 17 meningioma MRIs. The segmentation performance of the model was sequentially improved via the pre-training and inclusion of normal brain images. The Dice scores improved from 0.72 to 0.76 when the model was pre-trained. The inclusion of normal brain MRIs to fine-tune the model improved the Dice score; it increased to 0.79. When employing BDL as the loss function, the Dice score reached 0.84. The proposed learning strategy for U-net showed potential for use in segmenting meningioma lesions.<\/jats:p>","DOI":"10.3390\/jimaging8120327","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T04:25:26Z","timestamp":1671078326000},"page":"327","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Fully Automated Segmentation Models of Supratentorial Meningiomas Assisted by Inclusion of Normal Brain Images"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7211-729X","authenticated-orcid":false,"given":"Kihwan","family":"Hwang","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si 13620, Gyeonggi-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juntae","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Soongsil University, Seoul 06978, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Young-Jae","family":"Kwon","sequence":"additional","affiliation":[{"name":"Seoul National University College of Medicine, Seoul 03080, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Se Jin","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si 13620, Gyeonggi-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6310-1798","authenticated-orcid":false,"given":"Byung Se","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si 13620, Gyeonggi-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7515-0837","authenticated-orcid":false,"given":"Jiwon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Soongsil University, Seoul 06978, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eunchong","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Soongsil University, Seoul 06978, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1399-1363","authenticated-orcid":false,"given":"Jongha","family":"Jang","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Soongsil University, Seoul 06978, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4698-8686","authenticated-orcid":false,"given":"Kwang-Sung","family":"Ahn","sequence":"additional","affiliation":[{"name":"Department of Functional Genome Institute, PDXen Co., Daejeon 34027, Republic of Korea"},{"name":"Cancer Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9836-9823","authenticated-orcid":false,"given":"Sangsoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Soongsil University, Seoul 06978, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9773-5553","authenticated-orcid":false,"given":"Chae-Yong","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si 13620, Gyeonggi-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Springer International Publishing. Lecture Notes in Computer Science, 9351.","DOI":"10.1007\/978-3-319-24553-9"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104815","DOI":"10.1016\/j.compbiomed.2021.104815","article-title":"Focus U-Net: A Novel Dual Attention-Gated CNN for Polyp Segmentation during Colonoscopy","volume":"137","author":"Yeung","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","article-title":"U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications","volume":"9","author":"Siddique","year":"2021","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5845","DOI":"10.1007\/s10462-022-10152-1","article-title":"Modality Specific U-Net Variants for Biomedical Image Segmentation: A Survey","volume":"55","author":"Punn","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_5","unstructured":"Torfi, A., Shirvani, R.A., Keneshloo, Y., Tavaf, N., and Fox, E.A. (2021). Natural Language Processing Advancements by Deep Learning: A Survey. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4291","DOI":"10.1109\/TNNLS.2020.3019893","article-title":"Attention in Natural Language Processing","volume":"32","author":"Galassi","year":"2021","journal-title":"IEEE Trans. Neural. Netw. Learning Syst."},{"key":"ref_7","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention U-Net: Learning Where to Look for the Pancreas. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","article-title":"Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images","volume":"53","author":"Schlemper","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"102035","DOI":"10.1016\/j.media.2021.102035","article-title":"Loss Odyssey in Medical Image Segmentation","volume":"71","author":"Ma","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The Multimodal Brain Tumor Image Segmentation Benchmark (BraTS)","volume":"34","author":"Menze","year":"2015","journal-title":"IEEE Trans Med. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"170117","DOI":"10.1038\/sdata.2017.117","article-title":"Advancing The Cancer Genome Atlas Glioma MRI Collections with Expert Segmentation Labels and Radiomic Features","volume":"4","author":"Bakas","year":"2017","journal-title":"Sci. Data"},{"key":"ref_12","unstructured":"Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., and Rozycki, M. (2018). Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kamnitsas, K., Bai, W., Ferrante, E., McDonagh, S., Sinclair, M., Pawlowski, N., Rajchl, M., Lee, M., Kainz, B., and Rueckert, D. (2017). Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation. arXiv.","DOI":"10.1007\/978-3-319-75238-9_38"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Myronenko, A. (2018). 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization. arXiv.","DOI":"10.1007\/978-3-030-11726-9_28"},{"key":"ref_15","unstructured":"(2021, December 22). Two-Stage Cascaded U-Net: 1st Place Solution to BraTS Challenge 2019 Segmentation Task. Available online: https:\/\/www.springerprofessional.de\/en\/two-stage-cascaded-u-net-1st-place-solution-to-brats-challenge-2\/17993490."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wacker, J., Ladeira, M., and Nascimento, J.E.V. (2020). Transfer Learning for Brain Tumor Segmentation. arXiv.","DOI":"10.1007\/978-3-030-72084-1_22"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ouyang, C., Kamnitsas, K., Biffi, C., Duan, J., and Rueckert, D. (2019). Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation. arXiv.","DOI":"10.1007\/978-3-030-32245-8_74"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1007\/s00330-018-5595-8","article-title":"Fully Automated Detection and Segmentation of Meningiomas Using Deep Learning on Routine Multiparametric MRI","volume":"29","author":"Laukamp","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s00062-020-00884-4","article-title":"Automated Meningioma Segmentation in Multiparametric MRI: Comparable Effectiveness of a Deep Learning Model and Manual Segmentation","volume":"31","author":"Laukamp","year":"2021","journal-title":"Clin. Neuroradiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"024002","DOI":"10.1117\/1.JMI.8.2.024002","article-title":"Fast Meningioma Segmentation in T1-Weighted MRI Volumes Using a Lightweight 3D Deep Learning Architecture","volume":"8","author":"Bouget","year":"2021","journal-title":"J. Med. Imag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.-A. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. arXiv.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","article-title":"FSL","volume":"62","author":"Jenkinson","year":"2012","journal-title":"Neuroimage"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1016\/j.neuroimage.2010.09.025","article-title":"A Reproducible Evaluation of ANTs Similarity Metric Performance in Brain Image Registration","volume":"54","author":"Avants","year":"2011","journal-title":"Neuroimage"},{"key":"ref_26","unstructured":"Kayalibay, B., Jensen, G., and van der Smagt, P. (2017). CNN-Based Segmentation of Medical Imaging Data. arXiv."},{"key":"ref_27","unstructured":"Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2017). Instance Normalization: The Missing Ingredient for Fast Stylization. arXiv."},{"key":"ref_28","unstructured":"Maas, A.L. (2013, January 16\u201321). Rectifier Nonlinearities Improve Neural Network Acoustic Models. Proceedings of the 30th International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_29","unstructured":"(2021, January 10). Keras: Deep Learning for Humans. Available online: https:\/\/github.com\/keras-team\/keras."},{"key":"ref_30","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016). TensorFlow: A System for Large-Scale Machine Learning. arXiv."},{"key":"ref_31","unstructured":"Ellis, D.G. (2021, January 10). 3D U-Net Convolution Neural Network. Available online: https:\/\/github.com\/ellisdg\/3DUnetCNN."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rojas, I., Joya, G., and Gabestany, J. (2013). F-Measure as the Error Function to Train Neural Networks. Advances in Computational Intelligence\u2014IWANN 2013, Springer. Lecture Notes in Computer Science, 7902.","DOI":"10.1007\/978-3-642-38679-4"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","article-title":"A Survey of Transfer Learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"ref_34","unstructured":"Kingma, D.P., and Ba, J. (2017). Adam. A Method for Stochastic Optimization. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"i44","DOI":"10.1093\/neuonc\/noy143","article-title":"International Consortium on Meningiomas. Imaging and Diagnostic Advances for Intracranial Meningiomas","volume":"21","author":"Huang","year":"2019","journal-title":"Neuro. Oncol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103751","DOI":"10.1016\/j.compbiomed.2020.103751","article-title":"Tissue-Specific and Interpretable Sub-Segmentation of Whole Tumour Burden on CT Images by Unsupervised Fuzzy Clustering","volume":"120","author":"Rundo","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_37","unstructured":"Heo, B., Chun, S., Oh, S.J., Han, D., Yun, S., Kim, G., Uh, Y., and Ha, J.-W. (2021). AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-Invariant Weights. arXiv."},{"key":"ref_38","unstructured":"Yu, T., and Zhu, H. (2020). Hyper-Parameter Optimization: A Review of Algorithms and Applications. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"102026","DOI":"10.1016\/j.compmedimag.2021.102026","article-title":"Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation","volume":"95","author":"Yeung","year":"2022","journal-title":"Comput. Med. Imaging Graph."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/12\/327\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:41:50Z","timestamp":1760146910000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/12\/327"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,15]]},"references-count":39,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["jimaging8120327"],"URL":"https:\/\/doi.org\/10.3390\/jimaging8120327","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,15]]}}}