{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T10:51:46Z","timestamp":1779101506528,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T00:00:00Z","timestamp":1673049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSFC","award":["51905092"],"award-info":[{"award-number":["51905092"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate segmentation of the left atrial structure using magnetic resonance images provides an important basis for the diagnosis of atrial fibrillation (AF) and its treatment using robotic surgery. In this study, an image segmentation method based on sequence relationship learning and multi-scale feature fusion is proposed for 3D to 2D sequence conversion in cardiac magnetic resonance images and the varying scales of left atrial structures within different slices. Firstly, a convolutional neural network layer with an attention module was designed to extract and fuse contextual information at different scales in the image, to strengthen the target features using the correlation between features in different regions within the image, and to improve the network\u2019s ability to distinguish the left atrial structure. Secondly, a recurrent neural network layer oriented to two-dimensional images was designed to capture the correlation of left atrial structures in adjacent slices by simulating the continuous relationship between sequential image slices. Finally, a combined loss function was constructed to reduce the effect of positive and negative sample imbalance and improve model stability. The Dice, IoU, and Hausdorff distance values reached 90.73%, 89.37%, and 4.803 mm, respectively, based on the LASC2013 (left atrial segmentation challenge in 2013) dataset; the corresponding values reached 92.05%, 89.41% and 9.056 mm, respectively, based on the ASC2018 (atrial segmentation challenge at 2018) dataset.<\/jats:p>","DOI":"10.3390\/s23020690","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T06:38:27Z","timestamp":1673246307000},"page":"690","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Cardiac Magnetic Resonance Image Segmentation Method Based on Multi-Scale Feature Fusion and Sequence Relationship Learning"],"prefix":"10.3390","volume":"23","author":[{"given":"Yushi","family":"Qi","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Donghua University, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunhu","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Donghua University, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liling","family":"Zuo","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Donghua University, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Donghua University, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youlong","family":"Lv","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Donghua University, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102360","DOI":"10.1016\/j.media.2022.102360","article-title":"Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review","volume":"77","author":"Li","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"An, L., Wang, L., and Li, Y. (2022). HEA-Net: Attention and MLP Hybrid Encoder Architecture for Medical Image Segmentation. Sensors, 22.","DOI":"10.3390\/s22187024"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, Y., Han, G., and Liu, X. (2022). Lightweight Compound Scaling Network for Nasopharyngeal Carcinoma Segmentation from MR Images. Sensors, 22.","DOI":"10.3390\/s22155875"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lee, H.M., Kim, Y.J., and Kim, K.G. (2022). Segmentation Performance Comparison Considering Regional Characteristics in Chest X-ray Using Deep Learning. Sensors, 22.","DOI":"10.3390\/s22093143"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"116511","DOI":"10.1016\/j.eswa.2022.116511","article-title":"Multi-threshold image segmentation using a multi-strategy shuffled frog leaping algorithm","volume":"194","author":"Chen","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_6","first-page":"17","article-title":"Synchronous enlargement algorithm for image segmentation in nuclear medicine","volume":"Z1","author":"Liu","year":"2001","journal-title":"J. Tsinghua Univ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"115907","DOI":"10.1016\/j.image.2020.115907","article-title":"Level set formulation for automatic medical image segmentation based on fuzzy clustering","volume":"87","author":"Yang","year":"2020","journal-title":"Signal Process. Image Commun."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1109\/TMI.2015.2398818","article-title":"Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets","volume":"34","author":"Geers","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"102303","DOI":"10.1016\/j.media.2021.102303","article-title":"AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information","volume":"76","author":"Li","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhu, M., Wang, J., Guo, X., Yang, Y., and Wang, J. (2022). Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation. Sensors, 22.","DOI":"10.3390\/s22114222"},{"key":"ref_11","first-page":"519","article-title":"Suvery of medical image segmentation technology based on U-Net structure improvement","volume":"32","author":"Yin","year":"2021","journal-title":"Ruan Jian Xue Bao J. Softw."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, Y., Dai, Y., Yan, C., and Wang, K. (2019). Deep Learning Based Method for Left Atrial Segmentation in GE-MRI. International Workshop on Statistical Atlases and Computational Models of the Heart, Springer.","DOI":"10.1007\/978-3-030-12029-0_34"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1109\/TMI.2018.2866845","article-title":"Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network","volume":"38","author":"Xiong","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Vesal, S., Ravikumar, N., and Maier, A. (2019). Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI. International Workshop on Statistical Atlases and Computational Models of the Heart, Springer.","DOI":"10.1007\/978-3-030-12029-0_35"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., and Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation, Springer.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Laiton-Bonadiez, C., Sanchez-Torres, G., and Branch-Bedoya, J. (2022). Deep 3D Neural Network for Brain Structures Segmentation Using Self-Attention Modules in MRI Images. Sensors, 22.","DOI":"10.3390\/s22072559"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nodirov, J., Abdusalomov, A.B., and Whangbo, T.K. (2022). Attention 3D U-Net with Multiple Skip Connections for Segmentation of Brain Tumor Images. Sensors, 22.","DOI":"10.3390\/s22176501"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.neunet.2021.03.023","article-title":"PyDiNet: Pyramid Dilated Network for medical image segmentation","volume":"140","author":"Gridach","year":"2021","journal-title":"Neural Netw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101958","DOI":"10.1016\/j.media.2021.101958","article-title":"Pancreas segmentation using a dual-input v-mesh network","volume":"69","author":"Wang","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_20","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_21","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 International Publishing.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"17335","DOI":"10.1007\/s11042-020-09062-7","article-title":"A bibliometric and visual analysis of artificial intelligence technologies-enhanced brain MRI research","volume":"80","author":"Chen","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_23","first-page":"1382","article-title":"Combining Sequence Learning and U-Like-Net for Hippocampus Segmentation","volume":"31","author":"Cao","year":"2019","journal-title":"J. Comput. Des. Comput. Graph."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, S., Qiu, C., Yang, W., and Zhang, Z. (2022). Multiresolution Aggregation Transformer UNet Based on Multiscale Input and Coordinate Attention for Medical Image Segmentation. Sensors, 22.","DOI":"10.3390\/s22103820"},{"key":"ref_26","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yamanakkanavar, N., Choi, J.Y., and Lee, B. (2022). Multiscale and Hierarchical Feature-Aggregation Network for Segmenting Medical Images. Sensors, 22.","DOI":"10.3390\/s22093440"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual Attention Network for Scene Segmentation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cho, K., Merrienboer, B.V., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014, January 25\u201329). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_30","first-page":"06432","article-title":"Delving Deeper into Convolutional Networks for Learning Video Representations","volume":"1511","author":"Ballas","year":"2015","journal-title":"Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bai, W., Suzuki, H., Qin, C., Tarroni, G., Oktay, O., Matthews, P.M., and Rueckert, D. (2018). Recurrent Neural Networks for Aortic Image Sequence Segmentation with Sparse Annotations. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-030-00937-3_67"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"101832","DOI":"10.1016\/j.media.2020.101832","article-title":"A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging","volume":"67","author":"Xiong","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., and Cardoso, M.J. (2017). Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1109\/34.232073","article-title":"Comparing images using the Hausdorff distance","volume":"15","author":"Huttenlocher","year":"1993","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_37","unstructured":"Loshchilov, I., and Hutter, F. (2017). Decoupled Weight Decay Regularization. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kausar, A., Razzak, I., Shapiai, I., and Alshammari, R. (2021, January 22). An Improved Dense V-Network for Fast and Precise Segmentation of Left Atrium. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China.","DOI":"10.1109\/IJCNN52387.2021.9534418"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1016\/j.bbe.2022.09.005","article-title":"Semi-supervised structure attentive temporal mixup coherence for medical image segmentation","volume":"42","author":"Pawan","year":"2022","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1109\/TMI.2021.3117495","article-title":"LA-Net: A Multi-Task Deep Network for the Segmentation of the Left Atrium","volume":"41","author":"Uslu","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Luo, X., Liao, W., Chen, J., Song, T., Chen, Y., Zhang, S., Chen, N., Wang, G., and Zhang, S. (2021). Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-030-87196-3_30"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"102530","DOI":"10.1016\/j.media.2022.102530","article-title":"Mutual consistency learning for semi-supervised medical image segmentation","volume":"81","author":"Wu","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zuluaga, M.A., Cardoso, M.J., Modat, M., and Ourselin, S. (2013). Multi-atlas propagation whole heart segmentation from MRI and CTA using a local normalised correlation coefficient criterion. International Conference on Functional Imaging and Modeling of the Heart, Springer.","DOI":"10.1007\/978-3-642-38899-6_21"},{"key":"ref_44","first-page":"31","article-title":"Model-based segmentation of the left atrium in CT and MRI scans","volume":"Volume 8330","author":"Stender","year":"2014","journal-title":"International Workshop on Statistical Atlases and Computational Models of the Heart"},{"key":"ref_45","first-page":"24","article-title":"Multi-atlas-based segmentation of the left atrium and pulmonary veins","volume":"Volume 8330","author":"Sandoval","year":"2014","journal-title":"International Workshop on Statistical Atlases and Computational Models of the Heart"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/690\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:02:25Z","timestamp":1760119345000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/690"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,7]]},"references-count":45,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23020690"],"URL":"https:\/\/doi.org\/10.3390\/s23020690","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,7]]}}}