{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:38:37Z","timestamp":1787027917980,"version":"build-2736575974"},"reference-count":36,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T00:00:00Z","timestamp":1624579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"IMPACT","award":["2018-02230"],"award-info":[{"award-number":["2018-02230"]}]},{"name":"Linkoping University Cancer organization","award":["2020-2021"],"award-info":[{"award-number":["2020-2021"]}]},{"name":"Analytic Imaging Diagnostics Arena","award":["2017-02447"],"award-info":[{"award-number":["2017-02447"]}]},{"DOI":"10.13039\/100007435","name":"\u00c5ke Wiberg Stiftelse","doi-asserted-by":"publisher","award":["M20-0031"],"award-info":[{"award-number":["M20-0031"]}],"id":[{"id":"10.13039\/100007435","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004359","name":"Swedish Research Council","doi-asserted-by":"publisher","award":["2018-05250"],"award-info":[{"award-number":["2018-05250"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Effective, robust, and automatic tools for brain tumor segmentation are needed for the extraction of information useful in treatment planning. Recently, convolutional neural networks have shown remarkable performance in the identification of tumor regions in magnetic resonance (MR) images. Context-aware artificial intelligence is an emerging concept for the development of deep learning applications for computer-aided medical image analysis. A large portion of the current research is devoted to the development of new network architectures to improve segmentation accuracy by using context-aware mechanisms. In this work, it is investigated whether or not the addition of contextual information from the brain anatomy in the form of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) masks and probability maps improves U-Net-based brain tumor segmentation. The BraTS2020 dataset was used to train and test two standard 3D U-Net (nnU-Net) models that, in addition to the conventional MR image modalities, used the anatomical contextual information as extra channels in the form of binary masks (CIM) or probability maps (CIP). For comparison, a baseline model (BLM) that only used the conventional MR image modalities was also trained. The impact of adding contextual information was investigated in terms of overall segmentation accuracy, model training time, domain generalization, and compensation for fewer MR modalities available for each subject. Median (mean) Dice scores of 90.2 (81.9), 90.2 (81.9), and 90.0 (82.1) were obtained on the official BraTS2020 validation dataset (125 subjects) for BLM, CIM, and CIP, respectively. Results show that there is no statistically significant difference when comparing Dice scores between the baseline model and the contextual information models (p &gt; 0.05), even when comparing performances for high and low grade tumors independently. In a few low grade cases where improvement was seen, the number of false positives was reduced. Moreover, no improvements were found when considering model training time or domain generalization. Only in the case of compensation for fewer MR modalities available for each subject did the addition of anatomical contextual information significantly improve (p &lt; 0.05) the segmentation of the whole tumor. In conclusion, there is no overall significant improvement in segmentation performance when using anatomical contextual information in the form of either binary WM, GM, and CSF masks or probability maps as extra channels.<\/jats:p>","DOI":"10.3390\/diagnostics11071159","type":"journal-article","created":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T04:46:57Z","timestamp":1624596417000},"page":"1159","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Does Anatomical Contextual Information Improve 3D U-Net-Based Brain Tumor Segmentation?"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7582-1706","authenticated-orcid":false,"given":"Iulian Emil","family":"Tampu","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Link\u00f6ping University, 581 83 Link\u00f6ping, Sweden"},{"name":"Center for Medical Image Science and Visualization, Link\u00f6ping University, 581 83 Link\u00f6ping, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0555-8877","authenticated-orcid":false,"given":"Neda","family":"Haj-Hosseini","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Link\u00f6ping University, 581 83 Link\u00f6ping, Sweden"},{"name":"Center for Medical Image Science and Visualization, Link\u00f6ping University, 581 83 Link\u00f6ping, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7061-7995","authenticated-orcid":false,"given":"Anders","family":"Eklund","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Link\u00f6ping University, 581 83 Link\u00f6ping, Sweden"},{"name":"Center for Medical Image Science and Visualization, Link\u00f6ping University, 581 83 Link\u00f6ping, Sweden"},{"name":"Department of Computer and Information Science, Link\u00f6ping University, 581 83 Link\u00f6ping, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S2","DOI":"10.1188\/16.CJON.S1.2-8","article-title":"Glioblastoma: Overview of disease and treatment","volume":"20","author":"Davis","year":"2016","journal-title":"Clin. J. Oncol. Nurs."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/j.wneu.2017.04.041","article-title":"Extent of resection in glioma\u2013a review of the cutting edge","volume":"103","author":"Englander","year":"2017","journal-title":"World Neurosurg."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/s11060-018-03034-6","article-title":"Radiographic assessment of contrast enhancement and T2\/FLAIR mismatch sign in lower grade gliomas: Correlation with molecular groups","volume":"141","author":"Juratli","year":"2019","journal-title":"J. Neurooncol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101727","DOI":"10.1016\/j.nicl.2019.101727","article-title":"Inter-rater agreement in glioma segmentations on longitudinal MRI","volume":"22","author":"Visser","year":"2019","journal-title":"NeuroImage Clin."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1227\/NEU.0b013e3181efbb08","article-title":"Intraobserver and interobserver agreement in volumetric assessment of glioblastoma multiforme resection","volume":"67","author":"Kubben","year":"2010","journal-title":"Neurosurgery"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.patrec.2019.11.020","article-title":"Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019","volume":"131","author":"Tiwari","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.procs.2016.09.407","article-title":"Review of MRI-based brain tumor image segmentation using deep learning methods","volume":"102","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Noori, M., Bahri, A., and Mohammadi, K. (2019, January 24\u201325). Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation. Proceedings of the 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran.","DOI":"10.1109\/ICCKE48569.2019.8964956"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., and Maier-Hein, K.H. (2018). No new-net. International MICCAI Brainlesion Workshop, Springer.","DOI":"10.1007\/978-3-030-11726-9_21"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/JPROC.2019.2946993","article-title":"Cai4cai: The rise of contextual artificial intelligence in computer-assisted interventions","volume":"108","author":"Vercauteren","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_14","first-page":"1744","article-title":"Auto-context and its application to high-level vision tasks and 3d brain image segmentation","volume":"32","author":"Tu","year":"2009","journal-title":"IEEE PAMI"},{"key":"ref_15","first-page":"1","article-title":"IOUC-3DSFCNN: Segmentation of brain tumors via IOU constraint 3D symmetric full convolution network with multimodal auto-context","volume":"10","author":"Liu","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ahmad, P., Qamar, S., Shen, L., and Saeed, A. (2020). Context Aware 3D UNet for Brain Tumor Segmentation. arXiv.","DOI":"10.1109\/MIPR49039.2020.00029"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chandra, S., Vakalopoulou, M., Fidon, L., Battistella, E., Estienne, T., Sun, R., Robert, C., Deutsch, E., and Paragios, N. (2018). Context aware 3D CNNs for brain tumor segmentation. International MICCAI Brainlesion Workshop, Springer.","DOI":"10.1007\/978-3-030-11726-9_27"},{"key":"ref_18","unstructured":"Liu, Z., Tong, L., Chen, L., Zhou, F., Jiang, Z., Zhang, Q., Wang, Y., Shan, C., Li, L., and Zhou, H. (2020). CANet: Context Aware Network for 3D Brain Tumor Segmentation. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-74419-9","article-title":"Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images","volume":"10","author":"Pei","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_20","unstructured":"Le, N., Yamazaki, K., Truong, D., Quach, K.G., and Savvides, M. (2020). A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.neuroimage.2017.02.035","article-title":"DeepNAT: Deep convolutional neural network for segmenting neuroanatomy","volume":"170","author":"Wachinger","year":"2018","journal-title":"NeuroImage"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shen, H., Wang, R., Zhang, J., and McKenna, S.J. (2017, January 10\u201314). Boundary-aware fully convolutional network for brain tumor segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-66185-8_49"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shen, H., Zhang, J., and Zheng, W. (2017, January 17\u201320). Efficient symmetry-driven fully convolutional network for multimodal brain tumor segmentation. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8297006"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kao, P.Y., Ngo, T., Zhang, A., Chen, J.W., and Manjunath, B. (2018, January 16). Brain tumor segmentation and tractographic feature extraction from structural MR images for overall survival prediction. Proceedings of the International MICCAI Brainlesion Workshop, Granada, Spain.","DOI":"10.1007\/978-3-030-11726-9_12"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","article-title":"An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest","volume":"31","author":"Desikan","year":"2006","journal-title":"Neuroimage"},{"key":"ref_26","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":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/42.906424","article-title":"Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm","volume":"20","author":"Zhang","year":"2001","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"503","DOI":"10.3389\/fnins.2016.00503","article-title":"Reproducibility and bias in healthy brain segmentation: Comparison of two popular neuroimaging platforms","volume":"10","author":"Tudorascu","year":"2016","journal-title":"Front. Neurosci."},{"key":"ref_30","unstructured":"Ashburner, J., Barnes, G., Chen, C.C., Daunizeau, J., Flandin, G., Friston, K., Kiebel, S., Kilner, J., Litvak, V., and Moran, R. (2014). SPM12 Manual, Wellcome Trust Centre for Neuroimaging."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","article-title":"FreeSurfer","volume":"62","author":"Bruce","year":"2012","journal-title":"NeuroImage"},{"key":"ref_32","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":"2021","journal-title":"Nat. Methods"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Isensee, F., Jaeger, P.F., Full, P.M., Vollmuth, P., and Maier-Hein, K.H. (2020). nnU-Net for Brain Tumor Segmentation. arXiv.","DOI":"10.1007\/978-3-030-72087-2_11"},{"key":"ref_34","unstructured":"Tofts, P. (2005). Quantitative MRI of the Brain: Measuring Changes Caused by Disease, John Wiley & Sons."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"R297","DOI":"10.1088\/0031-9155\/60\/21\/R297","article-title":"Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction","volume":"60","author":"Hollingsworth","year":"2015","journal-title":"Phys. Med. Biol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhou, K., Liu, Z., Qiao, Y., Xiang, T., and Loy, C.C. (2021). Domain generalization: A survey. arXiv.","DOI":"10.1109\/TPAMI.2022.3195549"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/7\/1159\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:23:40Z","timestamp":1760163820000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/7\/1159"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,25]]},"references-count":36,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["diagnostics11071159"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics11071159","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,25]]}}}