{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:06:19Z","timestamp":1779383179057,"version":"3.53.1"},"reference-count":38,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T00:00:00Z","timestamp":1719100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Plan Project of Hangzhou China"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The traditional methods for 3D reconstruction mainly involve using image processing techniques or deep learning segmentation models for rib extraction. After post-processing, voxel-based rib reconstruction is achieved. However, these methods suffer from limited reconstruction accuracy and low computational efficiency. To overcome these limitations, this paper proposes a 3D rib reconstruction method based on point cloud adaptive smoothing and denoising. We converted voxel data from CT images to multi-attribute point cloud data. Then, we applied point cloud adaptive smoothing and denoising methods to eliminate noise and non-rib points in the point cloud. Additionally, efficient 3D reconstruction and post-processing techniques were employed to achieve high-accuracy and comprehensive 3D rib reconstruction results. Experimental calculations demonstrated that compared to voxel-based 3D rib reconstruction methods, the 3D rib models generated by the proposed method achieved a 40% improvement in reconstruction accuracy and were twice as efficient as the former.<\/jats:p>","DOI":"10.3390\/s24134076","type":"journal-article","created":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T06:59:58Z","timestamp":1719212398000},"page":"4076","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Research on Three-Dimensional Reconstruction of Ribs Based on Point Cloud Adaptive Smoothing Denoising"],"prefix":"10.3390","volume":"24","author":[{"given":"Darong","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Affiliated Hangzhou First People\u2019s Hospital, School of Medicine, Westlake University, Hangzhou 310024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanjiao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6506-1021","authenticated-orcid":false,"given":"Zhe","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9868-1083","authenticated-orcid":false,"given":"Bishi","family":"He","sequence":"additional","affiliation":[{"name":"School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"891","DOI":"10.3348\/kjr.2019.0653","article-title":"Assessment of a Deep Learning Algorithm for the Detection of Rib Fractures on Whole-Body Trauma Computed Tomography","volume":"21","author":"Weikert","year":"2020","journal-title":"Korean J. Radiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3843","DOI":"10.1109\/TMI.2020.3006138","article-title":"Rectifying Supporting Regions With Mixed and Active Supervision for Rib Fracture Recognition","volume":"39","author":"Huang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1109\/TMI.2023.3313627","article-title":"RibSeg v2: A Large-Scale Benchmark for Rib Labeling and Anatomical Centerline Extraction","volume":"43","author":"Jin","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e013029","DOI":"10.1136\/bmjopen-2016-013029","article-title":"Risk of pneumonia in patients with isolated minor rib fractures: A nationwide cohort study","volume":"7","author":"Ho","year":"2017","journal-title":"BMJ Open"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jss.2018.03.025","article-title":"Quantification of rib fractures by different scoring systems","volume":"229","author":"Fokin","year":"2018","journal-title":"J. Surg. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"229","DOI":"10.5090\/kjtcs.2017.50.4.229","article-title":"Rib Fractures: To Fix or Not to Fix? An Evidence-Based Algorithm","volume":"50","author":"Bemelman","year":"2017","journal-title":"Korean J. Thorac. Cardiovasc. Surg."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1016\/j.annemergmed.2017.04.011","article-title":"Rib Fracture Diagnosis in the Panscan Era","volume":"70","author":"Murphy","year":"2017","journal-title":"Ann. Emerg. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9596518","DOI":"10.1155\/2021\/9596518","article-title":"The Value of CT 3D Reconstruction in the Classification and Nursing Effect Evaluation of Ankle Fracture","volume":"2021","author":"Xue","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s11831-022-09790-z","article-title":"2D-to-3D: A Review for Computational 3D Image Reconstruction from X-ray Images","volume":"30","author":"Maken","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s00586-021-07024-7","article-title":"Comparison of pelvic incidence measurement using lateral x-ray, standard ct versus ct with 3d reconstruction","volume":"31","author":"Lee","year":"2021","journal-title":"Eur. Spine J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1007\/s40846-022-00764-x","article-title":"Assessment and Improvement of a Novel ultrasound-based 3D Reconstruction Method: Registered for Lumbar Spine","volume":"42","author":"Effatparvar","year":"2022","journal-title":"J. Med. Biol. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1007\/s00586-018-5807-6","article-title":"Quasi-automatic 3D reconstruction of the full spine from low-dose biplanar X-rays based on statistical inferences and image analysis","volume":"28","author":"Gajny","year":"2018","journal-title":"Eur. Spine J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2746","DOI":"10.1049\/iet-ipr.2019.0800","article-title":"3D reconstruction of spine image from 2D MRI slices along one axis","volume":"14","author":"Ghoshal","year":"2020","journal-title":"IET Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"389","DOI":"10.7494\/csci.2019.20.4.3163","article-title":"Current research opportunities for image processing and computer vision","volume":"20","author":"Gupta","year":"2019","journal-title":"Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5515","DOI":"10.1007\/s11042-021-11807-x","article-title":"A lightweight deep learning architecture for the automatic detection of pneumonia using chest X-ray images","volume":"81","author":"Trivedi","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1016\/j.bbe.2021.10.006","article-title":"A systematic review of the automatic kidney segmentation methods in abdominal images","volume":"41","author":"Pandey","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3245","DOI":"10.1007\/s11831-020-09497-z","article-title":"A Systematic Review of the Techniques for the Automatic Segmentation of Organs-at-Risk in Thoracic Computed Tomography Images","volume":"28","author":"Ashok","year":"2020","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"24573","DOI":"10.1007\/s11042-021-10837-9","article-title":"A method for automatic classification of gender based on text- independent handwriting","volume":"80","author":"Maken","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_19","first-page":"51","article-title":"A Study on Various Techniques Involved in Gender Prediction System: A Comprehensive Review","volume":"19","author":"Maken","year":"2019","journal-title":"Cybern. Inf. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105262","DOI":"10.1016\/j.autcon.2023.105262","article-title":"3D vision technologies for a self-developed structural external crack damage recognition robot","volume":"159","author":"Hu","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Altharan, Y.M., Shamsudin, S., Lajis, M.A., Al-Alimi, S., Yusuf, N.K., Alduais, N.A.M., Ghaleb, A.M., and Zhou, W. (2024). Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0300504"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kim, H., Ko, S., Bum, J., Le, D.T., and Choo, H. (2024, January 3\u20135). Rib Segmentation and Sequence Labeling via Biaxial Slicing and 3D Reconstruction. Proceedings of the 2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IMCOM60618.2024.10418333"},{"key":"ref_23","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 Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015, Munich, Germany. Part III 18.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2796","DOI":"10.1109\/TMI.2019.2914400","article-title":"Toward Automated 3D Spine Reconstruction from Biplanar Radiographs Using CNN for Statistical Spine Model Fitting","volume":"38","author":"Aubert","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1007\/s10278-017-9945-x","article-title":"Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data","volume":"30","author":"Forsberg","year":"2017","journal-title":"J. Digit. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.aei.2018.05.005","article-title":"A review of 3D reconstruction techniques in civil engineering and their applications","volume":"37","author":"Ma","year":"2018","journal-title":"Adv. Eng. Inform."},{"key":"ref_27","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). Pointnet: Deep learning on point sets for 3D classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., and Li, F.P.C. (2019, January 16\u201320). Deep convolutional networks on 3D point clouds. Proceedings of the CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00985"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1109\/TMM.2021.3073265","article-title":"Voxel structure-based mesh reconstruction from a 3D point cloud","volume":"24","author":"Lv","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wu, F., Qian, Y., Zheng, H., Zhang, Y., and Zheng, X. (2023). A Novel Neighbor Aggregation Function for Medical Point Cloud Analysis. Proceedings of the Computer Graphics International Conference, Springer Nature.","DOI":"10.1007\/978-3-031-50078-7_24"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, C.W., and Peng, C.C. (2021). 3D face point cloud reconstruction and recognition using depth sensor. Sensors, 21.","DOI":"10.3390\/s21082587"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dixit, S., Pai, V.G., Rodrigues, V.C., Agnani, K., and Priyan, S.R.V. (2019, January 20\u201321). 3D reconstruction of 2D X-ray images. Proceedings of the 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), Bengaluru, India.","DOI":"10.1109\/CSITSS47250.2019.9031045"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xiong, Z., Stiles, M.K., Yao, Y., Shi, R., Nalar, A., Hawson, J., Lee, G., and Zhao, J. (2022). Automatic 3D surface reconstruction of the left atrium from clinically mapped point clouds using convolutional neural networks. Front. Physiol., 13.","DOI":"10.3389\/fphys.2022.880260"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"102975","DOI":"10.1016\/j.media.2023.102975","article-title":"Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images","volume":"90","author":"Beetz","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., and Ronneberger, O. (2016, January 17\u201321). 3D U-Net: Learning dense volumetric segmentation from sparse annotation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016: 19th International Conference, Athens, Greece. Part II 19.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yang, J., Gu, S., Wei, D., Pfister, H., and Ni, B. (October, January 27). Ribseg dataset and strong point cloud baselines for rib segmentation from CT scans. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France.","DOI":"10.1007\/978-3-030-87193-2_58"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jiang, L., Jia, J., Torr, P.H., and Koltun, V. (2021, January 11\u201317). Point transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Virtual.","DOI":"10.1109\/ICCV48922.2021.01595"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jin, L., Yang, J., Kuang, K., Ni, B., Gao, Y., Sun, Y., Gao, P., Ma, W., Tan, M., and Kang, H. (2020). Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet. EBioMedicine, 62.","DOI":"10.1016\/j.ebiom.2020.103106"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4076\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:03:09Z","timestamp":1760108589000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4076"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,23]]},"references-count":38,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["s24134076"],"URL":"https:\/\/doi.org\/10.3390\/s24134076","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,23]]}}}