{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T02:42:31Z","timestamp":1784169751276,"version":"3.55.0"},"reference-count":41,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T00:00:00Z","timestamp":1733702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The convolutional neural network (CNN) algorithm in deep learning has been widely applied in petroleum geology research both domestically and internationally. Automated and accurate segmentation of thin-section images of rocks is foundational for in-depth analysis. However, traditional segmentation methods for reservoir rock thin sections often suffer from low accuracy and high cost. To address these issues, this paper proposes a novel segmentation algorithm based on an improved UNet network, integrating residual networks and the CBAM attention mechanism. By incorporating residual modules, the network depth is expanded, and the CBAM attention mechanism enhances the feature weighting capability during learning. Experimental results demonstrate that this method outperforms traditional approaches in both segmentation accuracy and efficiency, representing significant advancements in reservoir rock thin-section image segmentation.<\/jats:p>","DOI":"10.3390\/info15120788","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T11:16:48Z","timestamp":1733743008000},"page":"788","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Application of ResUNet-CBAM in Thin-Section Image Segmentation of Rocks"],"prefix":"10.3390","volume":"15","author":[{"given":"Ling","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4222-1285","authenticated-orcid":false,"given":"Huili","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianda","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Continental Shale Oil, Northeast Petroleum University, Daging 163318, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaozhuo","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Shenyang Center, China Geological Survey (Northeast Geological S&T Innovation Center), Shenyang 110034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengwu","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Continental Shale Oil, Northeast Petroleum University, Daging 163318, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7523-0736","authenticated-orcid":false,"given":"Xudong","family":"Qin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Continental Shale Oil, Northeast Petroleum University, Daging 163318, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,9]]},"reference":[{"key":"ref_1","first-page":"39","article-title":"Application of DBSCAN Algorithm and Mathematical Morphology in Rock Thin Section Image Segmentation","volume":"35","author":"Jiang","year":"2016","journal-title":"Microcomput. Appl."},{"key":"ref_2","first-page":"112","article-title":"Overview of Research Methods for Micro-Pore Structures in Rocks","volume":"41","author":"Li","year":"2019","journal-title":"Groundwater"},{"key":"ref_3","unstructured":"Zhang, Z. (2020). Research on Sandstone Thin Section Image Segmentation and Recognition. [Master\u2019s Thesis, University of Science and Technology of China]."},{"key":"ref_4","first-page":"171","article-title":"Rock Image Segmentation and Recognition Based on Superpixel and Semi-Supervised Learning","volume":"55","author":"Liu","year":"2023","journal-title":"Eng. Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.patcog.2016.12.012","article-title":"Automatic mineral identification using color tracking","volume":"65","author":"Aligholi","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_6","first-page":"1646","article-title":"Method for Graphic Recognition of Microscopic Pore-Throat Networks in Reservoirs","volume":"41","author":"Zhang","year":"2011","journal-title":"J. Jilin Univ. (Earth Sci. Ed.)"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.cageo.2015.05.001","article-title":"Semi-automatic segmentation of petrographic thin section images using a \u201cseeded-region growing algorithm\u201d with an application to characterize weathered subarkose sandstone","volume":"83","author":"Asmussen","year":"2015","journal-title":"Comput. Geosci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s00710-007-0200-x","article-title":"A new algorithm using image colour system transformation for rock grain segmentation","volume":"91","author":"Obara","year":"2007","journal-title":"Mineral. Petrol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.cageo.2011.09.008","article-title":"Detecting grain boundaries in deformed rocks using a cellular automata approach","volume":"42","author":"Gorsevski","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, L.C. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_12","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: 18th International Conference, Munich, Germany. Proceedings, Part III.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107404","DOI":"10.1016\/j.patcog.2020.107404","article-title":"U2-Net: Going deeper with nested U-structure for salient object detection","volume":"106","author":"Qin","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_14","first-page":"615","article-title":"Concrete CT Pore and Fracture Segmentation Method Based on Improved UNet","volume":"52","author":"He","year":"2023","journal-title":"J. China Univ. Min. Technol."},{"key":"ref_15","first-page":"1720","article-title":"Building Change Detection in Remote Sensing Images Based on Encoder-Decoder Network UNet3+","volume":"46","author":"Liang","year":"2023","journal-title":"Chin. J. Comput."},{"key":"ref_16","first-page":"104","article-title":"Mineral Recognition in Rock Thin Section Images Based on Improved SKnet and Bi-GRU","volume":"13","author":"Liu","year":"2023","journal-title":"Intell. Comput. Appl."},{"key":"ref_17","first-page":"54","article-title":"Core Particle Image Edge Segmentation Algorithm Based on Improved SLIC","volume":"11","author":"Dong","year":"2021","journal-title":"Intell. Comput. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104518","DOI":"10.1016\/j.marpetgeo.2020.104518","article-title":"Machine learning for point counting and segmentation of arenite in thin section","volume":"120","author":"Tang","year":"2020","journal-title":"Mar. Pet. Geol."},{"key":"ref_19","first-page":"11685","article-title":"Automatic Pore Extraction from Rock Cast Thin Section Images Based on Deep Learning","volume":"20","author":"Cai","year":"2020","journal-title":"Sci. Technol. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2332","DOI":"10.1007\/s10489-021-02530-z","article-title":"Deep neural networks for automatic grain-matrix segmentation in plane and cross-polarized sandstone photomicrographs","volume":"52","author":"Das","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104778","DOI":"10.1016\/j.cageo.2021.104778","article-title":"Application of deep learning for semantic segmentation of sandstone thin sections","volume":"152","author":"Saxena","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_22","first-page":"498","article-title":"Deep Learning-Based Recognition of Thin Sections of Rocks and Minerals under Microscopy","volume":"31","author":"Zhang","year":"2024","journal-title":"Geosci. Front."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107230","DOI":"10.1016\/j.mineng.2021.107230","article-title":"Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy","volume":"173","author":"Koh","year":"2021","journal-title":"Miner. Eng."},{"key":"ref_24","first-page":"743","article-title":"Deep Learning-Based Automatic Mineral Recognition Method for Rock Thin Sections","volume":"49","author":"Xu","year":"2022","journal-title":"J. Zhejiang Univ. (Sci. Ed.)"},{"key":"ref_25","first-page":"208","article-title":"Deep Learning-Based Organic Matter Pore Recognition and Comparison in Shale SEM Images","volume":"30","author":"Chen","year":"2023","journal-title":"Geosci. Front."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1016\/j.petsci.2022.03.011","article-title":"Rock thin-section analysis and identification based on artificial intelligent technique","volume":"19","author":"Liu","year":"2022","journal-title":"Pet. Sci."},{"key":"ref_27","first-page":"2196","article-title":"A Modular Network Structure for Building Ultra-Large-Scale Data Centers","volume":"28","author":"Lu","year":"2017","journal-title":"J. Softw."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yuan, K., Guo, S., Liu, Z., Zhou, A., Yu, F., and Wu, W. (2021, January 10\u201317). Incorporating Convolution Designs into Visual Transformers. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00062"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1007\/s11390-018-1859-7","article-title":"3D Filtering by Block Matching and Convolutional Neural Network for Image Denoising","volume":"33","author":"Zou","year":"2018","journal-title":"J. Comput. Sci. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Hasan, M., Yakopcic, C., Taha, T.M., and Asari, V.K. (2018). Recurrent Residual Convolutional Neural Network Based on U-net (R2U-Net) for Medical Image Segmentation. arXiv.","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3841","DOI":"10.1007\/s00521-020-05223-9","article-title":"A hybridized intelligence model to improve the predictability level of strength index parameters of rocks","volume":"33","author":"Asheghi","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104225","DOI":"10.1016\/j.catena.2019.104225","article-title":"Landslide susceptibility hazard map in southwest Sweden using artificial neural network","volume":"183","author":"Shahri","year":"2019","journal-title":"Catena"},{"key":"ref_35","first-page":"25","article-title":"Sediment Grain Size Distribution Measurement and Calibration Based on Global Threshold Segmentation","volume":"49","author":"Geng","year":"2024","journal-title":"Sediment Res."},{"key":"ref_36","first-page":"39","article-title":"Research on an Improved Image Segmentation Method Based on the DeepLabV3+ Model","volume":"8","author":"Li","year":"2024","journal-title":"Mod. Inf. Technol."},{"key":"ref_37","first-page":"27","article-title":"Application of an Improved Mask R-CNN for Vehicle Instance Segmentation","volume":"38","author":"Luo","year":"2023","journal-title":"J. Qingdao Univ. (Eng. Technol. Ed.)"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","article-title":"A review of uncertainty quantification in deep learning: Techniques, applications and challenges","volume":"76","author":"Abdar","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lertampaiporn, S., Vorapreeda, T., Hongsthong, A., and Thammarongtham, C. (2021). Ensemble-AMPPred: Robust AMP Prediction and Recognition Using the Ensemble Learning Method with a New Hybrid Feature for Differentiating AMPs. Genes, 12.","DOI":"10.3390\/genes12020137"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zheng, X., Fu, C., Xie, H.Y., Chen, J.L., Wang, X.W., and Sham, C.W. (2022). Uncertainty-aware deep co-training for semi-supervised medical image segmentation. Comput. Biol. Med., 149.","DOI":"10.1016\/j.compbiomed.2022.106051"},{"key":"ref_41","first-page":"193","article-title":"A survey on uncertainty estimation in deep learning classification systems from a bayesian perspective","volume":"54","author":"Mena","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/12\/788\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:50:32Z","timestamp":1760115032000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/12\/788"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,9]]},"references-count":41,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["info15120788"],"URL":"https:\/\/doi.org\/10.3390\/info15120788","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,9]]}}}