{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T00:25:25Z","timestamp":1720052725629},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T00:00:00Z","timestamp":1719964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior\u2014Brasil","award":["001"]},{"name":"Novartis"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"Precise annotations for large medical image datasets can be time-consuming. Additionally, when dealing with volumetric regions of interest, it is typical to apply segmentation techniques on 2D slices, compromising important information for accurately segmenting 3D structures. This study presents a deep learning pipeline that simultaneously tackles both challenges. Firstly, to streamline the annotation process, we employ a semi-automatic segmentation approach using bounding boxes as masks, which is less time-consuming than pixel-level delineation. Subsequently, recursive self-training is utilized to enhance annotation quality. Finally, a 2.5D segmentation technique is adopted, wherein a slice of a volumetric image is segmented using a pseudo-RGB image. The pipeline was applied to segment the carotid artery tree in T1-weighted brain magnetic resonance images. Utilizing 42 volumetric non-contrast T1-weighted brain scans from four datasets, we delineated bounding boxes around the carotid arteries in the axial slices. Pseudo-RGB images were generated from these slices, and recursive segmentation was conducted using a Res-Unet-based neural network architecture. The model\u2019s performance was tested on a separate dataset, with ground truth annotations provided by a radiologist. After recursive training, we achieved an Intersection over Union (IoU) score of (0.68 \u00b1 0.08) on the unseen dataset, demonstrating commendable qualitative results.<\/jats:p>","DOI":"10.3390\/jimaging10070161","type":"journal-article","created":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T12:45:34Z","timestamp":1720010734000},"page":"161","source":"Crossref","is-referenced-by-count":0,"title":["A 2.5D Self-Training Strategy for Carotid Artery Segmentation in T1-Weighted Brain Magnetic Resonance Images"],"prefix":"10.3390","volume":"10","author":[{"given":"Adriel Silva","family":"de Ara\u00fajo","sequence":"first","affiliation":[{"name":"School of Technology, Pontif\u00edcia Universidade Cat\u00f3lica do Rio Grande do Sul, Porto Alegre 90619-900, Brazil"}]},{"given":"M\u00e1rcio Sarroglia","family":"Pinho","sequence":"additional","affiliation":[{"name":"School of Technology, Pontif\u00edcia Universidade Cat\u00f3lica do Rio Grande do Sul, Porto Alegre 90619-900, Brazil"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5924-6852","authenticated-orcid":false,"given":"Ana Maria","family":"Marques da Silva","sequence":"additional","affiliation":[{"name":"Hospital das Cl\u00ednicas, Faculdade de Medicina, Universidade de S\u00e3o Paulo, S\u00e3o Paulo 05403-010, Brazil"}]},{"given":"Luis Felipe","family":"Fiorentini","sequence":"additional","affiliation":[{"name":"Centro de Diagn\u00f3stico por Imagem, Santa Casa de Miseric\u00f3rdia de Porto Alegre, Porto Alegre 90020-090, Brazil"},{"name":"Grupo Hospitalar Concei\u00e7\u00e3o, Porto Alegre 91350-200, Brazil"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9981-3620","authenticated-orcid":false,"given":"Jefferson","family":"Becker","sequence":"additional","affiliation":[{"name":"Hospital S\u00e3o Lucas, Pontif\u00edcia Universidade Cat\u00f3lica do Rio Grande do Sul, Porto Alegre 90610-000, Brazil"},{"name":"Brain Institute, Pontif\u00edcia Universidade Cat\u00f3lica do Rio Grande do Sul, Porto Alegre 90619-900, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhu, K., Xiong, N.N., and Lu, M. (2023, January 6\u20138). A Survey of Weakly-supervised Semantic Segmentation. Proceedings of the 2023 IEEE 9th International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance and Smart Computing, and IEEE International Conference on Intelligent Data and Security, BigDataSecurity-HPSC-IDS, New York, NY, USA.","DOI":"10.1109\/BigDataSecurity-HPSC-IDS58521.2023.00013"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s11263-020-01373-4","article-title":"A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains","volume":"129","author":"Chan","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_3","unstructured":"Kumar, A., Jiang, H., Imran, M., Valdes, C., Leon, G., Kang, D., Nataraj, P., Zhou, Y., Weiss, M.D., and Shao, W. (2024). A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention. arXiv."},{"key":"ref_4","unstructured":"Carmo, D., Rittner, L., and Lotufo, R. (2022). Open-source tool for Airway Segmentation in Computed Tomography using 2.5D Modified EfficientDet: Contribution to the ATM22 Challenge. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Avesta, A., Hossain, S., Lin, M., de Aboian, M., Krumholz, H.M., and Aneja, S. (2023). Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation. Bioengineering, 10.","DOI":"10.3390\/bioengineering10020181"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ou, Y., Yuan, Y., Huang, X., Wong, K., Volpi, J., Wang, J.Z., and Wong, S.T.C. (October, January 27). LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-Weighted MR Images. Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021, Strasbourg, France. Proceedings, Part I 24.","DOI":"10.1007\/978-3-030-87193-2_69"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"126298","DOI":"10.1016\/j.neucom.2023.126298","article-title":"A review of deep learning segmentation methods for carotid artery ultrasound images","volume":"545","author":"Huang","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13846","DOI":"10.1109\/ACCESS.2023.3243162","article-title":"Application of Artificial Intelligence Methods in Carotid Artery Segmentation: A Review","volume":"11","author":"Wang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1177\/0271678X16656197","article-title":"Estimation of an image derived input function with MR-defined carotid arteries in FDG-PET human studies using a novel partial volume correction method","volume":"37","author":"Sari","year":"2017","journal-title":"J. Cereb. Blood Flow Metab."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"118194","DOI":"10.1016\/j.neuroimage.2021.118194","article-title":"Validation of a combined image derived input function and venous sampling approach for the quantification of [18F]GE-179 PET binding in the brain","volume":"237","author":"Galovic","year":"2021","journal-title":"NeuroImage"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xu, W., Yang, X., Li, Y., Jiang, G., Jia, S., Gong, Z., Mao, Y., Zhang, S., Teng, Y., and Zhu, J. (2022). Deep Learning-Based Automated Detection of Arterial Vessel Wall and Plaque on Magnetic Resonance Vessel Wall Images. Front. Neurosci., 16.","DOI":"10.3389\/fnins.2022.888814"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, Y.-F., Chen, Z.-J., Lin, Y.-Y., Lin, Z.-Q., Chen, C.-N., Yang, M.-L., Zhang, J.-Y., Li, Y.-Z., Wang, Y., and Huang, Y.-H. (2023). Stroke risk study based on deep learning-based magnetic resonance imaging carotid plaque automatic segmentation algorithm. Front. Cardiovasc. Med., 10.","DOI":"10.3389\/fcvm.2023.1101765"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"171","DOI":"10.3171\/2019.9.JNS191949","article-title":"An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI","volume":"134","author":"Shapey","year":"2021","journal-title":"J. Neurosurg."},{"key":"ref_14","first-page":"264","article-title":"Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss","volume":"Volume 11765","author":"Shen","year":"2019","journal-title":"Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2019"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.3174\/ajnr.A7700","article-title":"Intracranial Vessel Segmentation in 3D High-Resolution T1 Black-Blood MRI","volume":"43","author":"Elsheikh","year":"2022","journal-title":"Am. J. Neuroradiol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Quon, J.L., Chen, L.C., Kim, L., Grant, G.A., Edwards, M.S.B., Cheshier, S.H., and Yeom, K.W. (2020). Deep Learning for Automated Delineation of Pediatric Cerebral Arteries on Pre-operative Brain Magnetic Resonance Imaging. Front. Surg., 7.","DOI":"10.3389\/fsurg.2020.517375"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2840","DOI":"10.1109\/TBME.2019.2896972","article-title":"Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks","volume":"66","author":"Shi","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4329\/wjr.v12.i1.1","article-title":"Segmentation of carotid arterial walls using neural networks","volume":"12","author":"Samber","year":"2020","journal-title":"World J. Radiol."},{"key":"ref_19","unstructured":"Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., and Li, S. (2022, January 18\u201322). Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via Scribble Annotations. Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2022, Singapore."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107956","DOI":"10.1016\/j.dib.2022.107956","article-title":"Neuroimaging of chronotype, sleep quality and daytime sleepiness: Structural T1-weighted magnetic resonance brain imaging data from 136 young adults","volume":"41","author":"Zareba","year":"2022","journal-title":"Data Brief"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Van Schuerbeek, P., Baeken, C., and de Mey, J. (2016). The Heterogeneity in Retrieved Relations between the Personality Trait \u201cHarm Avoidance\u201d and Gray Matter Volumes Due to Variations in the VBM and ROI Labeling Processing Settings. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0153865"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Koenders, L., Cousijn, J., Vingerhoets, W.A.M., van den Brink, W., Wiers, R.W., Meijer, C.J., Machielsen, M.W.J., Veltman, D.J., Goudriaan, A.E., and de Haan, L. (2016). Grey matter changes associated with heavy cannabis use: A longitudinal sMRI study. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0152482"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"LaMontagne, P.J., Benzinger, T.L., Morris, J.C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., and Vlassenko, A.G. (2019). OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease. medRxiv.","DOI":"10.1101\/2019.12.13.19014902"},{"key":"ref_24","first-page":"425","article-title":"Segments of the internal carotid artery: A new classification","volume":"38","author":"Bouthillier","year":"1996","journal-title":"Neurosurgery"},{"key":"ref_25","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 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 11\u201318). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the International Conference on Computer Vision, Las Condes, Chile. Available online: http:\/\/arxiv.org\/abs\/1502.01852.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_27","unstructured":"Ioffe, S., and Szegedy, C. (July, January 6). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on International Conference on Machine Learning, Lille, France."},{"key":"ref_28","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road Extraction by Deep Residual U-Net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data","volume":"162","author":"Diakogiannis","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015\u2014Conference Track Proceedings, San Diego, CA, USA. Available online: https:\/\/arxiv.org\/abs\/1412.6980v9."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the Amount of Ecologic Association Between Species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., and Jorge Cardoso, M. (2017). Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. arXiv.","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"M\u00fcller, D., Soto-Rey, I., and Kramer, F. (2022). Towards a guideline for evaluation metrics in medical image segmentation. BMC Res. Notes, 15.","DOI":"10.1186\/s13104-022-06096-y"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ni, Z.-L., Bian, G.-B., Zhou, X.-H., Hou, Z.-G., Xie, X.-L., Wang, C., Zhou, Y.-J., Li, R.-Q., and Li, Z. (2019, January 12\u201315). RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments. Proceedings of the International Conference on Neural Information Processing, Sydney, NSW, Australia.","DOI":"10.1007\/978-3-030-36711-4_13"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1007\/s10278-022-00629-4","article-title":"Nested U-Net for Segmentation of Red Lesions in Retinal Fundus Images and Sub-image Classification for Removal of False Positives","volume":"35","author":"Kundu","year":"2022","journal-title":"J. Digit. Imaging"},{"key":"ref_37","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 3\u20137). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. Proceedings of the ICLR 2021\u20149th International Conference on Learning Representations, Virtual."},{"key":"ref_38","unstructured":"Huang, Z., Wang, H., Deng, Z., Ye, J., Su, Y., Sun, H., He, J., Gu, Y., Gu, L., and Zhang, S. (2023). STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Qin, Z., Chen, Y., Zhu, G., Zhou, E., Zhou, Y., Zhou, Y., and Zhu, C. (2024). Enhanced Pseudo-Label Generation with Self-supervised Training for Weakly-supervised Semantic Segmentation. IEEE Trans. Circuits Syst. Video Technol., early access.","DOI":"10.1109\/TCSVT.2024.3364764"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"012014","DOI":"10.1088\/1742-6596\/2547\/1\/012014","article-title":"CAM-TMIL: A Weakly-Supervised Segmentation Framework for Histopathology based on CAMs and MIL","volume":"2547","author":"Feng","year":"2023","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"012001","DOI":"10.1088\/1742-6596\/1487\/1\/012001","article-title":"Weakly-Supervised Semantic Segmentation via Self-training","volume":"1487","author":"Cheng","year":"2020","journal-title":"J. Phys. Conf. Ser."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/7\/161\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T14:43:00Z","timestamp":1720017780000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/7\/161"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,3]]},"references-count":41,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["jimaging10070161"],"URL":"https:\/\/doi.org\/10.3390\/jimaging10070161","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,3]]}}}