{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T20:35:06Z","timestamp":1773693306907,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T00:00:00Z","timestamp":1649289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61905182"],"award-info":[{"award-number":["61905182"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51727901"],"award-info":[{"award-number":["51727901"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"2020 Medical Science and Technology Innovation Platform Support Project of Zhongnan Hospital of Wuhan University","award":["2020 Medical Science and Technology Innovation Platform Support Project of Zhongnan Hospital of Wuhan University"],"award-info":[{"award-number":["2020 Medical Science and Technology Innovation Platform Support Project of Zhongnan Hospital of Wuhan University"]}]},{"name":"Wuhan Research Program of Application Foundation and Advanced Technology.","award":["Wuhan Research Program of Application Foundation and Advanced Technology."],"award-info":[{"award-number":["Wuhan Research Program of Application Foundation and Advanced Technology."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Chromosome karyotype analysis is of great clinical importance in the diagnosis and treatment of diseases. Since manual analysis is highly time and effort consuming, computer-assisted automatic chromosome karyotype analysis based on images is routinely used to improve the efficiency and accuracy of the analysis. However, the strip-shaped chromosomes easily overlap each other when imaged, significantly affecting the accuracy of the subsequent analysis and hindering the development of chromosome analysis instruments. In this paper, we present an adversarial, multiscale feature learning framework to improve the accuracy and adaptability of overlapping chromosome segmentation. We first adopt the nested U-shaped network with dense skip connections as the generator to explore the optimal representation of the chromosome images by exploiting multiscale features. Then we use the conditional generative adversarial network (cGAN) to generate images similar to the original ones; the training stability of the network is enhanced by applying the least-square GAN objective. Finally, we replace the common cross-entropy loss with the advanced Lov\u00e1sz-Softmax loss to improve the model\u2019s optimization and accelerate the model\u2019s convergence. Comparing with the established algorithms, the performance of our framework is proven superior by using public datasets in eight evaluation criteria, showing its great potential in overlapping chromosome segmentation.<\/jats:p>","DOI":"10.3390\/e24040522","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T13:39:51Z","timestamp":1649338791000},"page":"522","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Adversarial Multiscale Feature Learning Framework for Overlapping Chromosome Segmentation"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2555-9199","authenticated-orcid":false,"given":"Liye","family":"Mei","sequence":"first","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0293-7510","authenticated-orcid":false,"given":"Yalan","family":"Yu","sequence":"additional","affiliation":[{"name":"The Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China"}]},{"given":"Hui","family":"Shen","sequence":"additional","affiliation":[{"name":"The Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China"}]},{"given":"Yueyun","family":"Weng","sequence":"additional","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"},{"name":"The Key Laboratory of Transients in Hydrolic Machinery of Ministry of Education, School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6021-1358","authenticated-orcid":false,"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"The Alipay Tian Qian Security Lab., Beijing 100020, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0098-7613","authenticated-orcid":false,"given":"Du","family":"Wang","sequence":"additional","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"}]},{"given":"Sheng","family":"Liu","sequence":"additional","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"},{"name":"The Key Laboratory of Transients in Hydrolic Machinery of Ministry of Education, School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China"}]},{"given":"Fuling","family":"Zhou","sequence":"additional","affiliation":[{"name":"The Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China"}]},{"given":"Cheng","family":"Lei","sequence":"additional","affiliation":[{"name":"The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,7]]},"reference":[{"key":"ref_1","first-page":"11","article-title":"Down Syndrome and Micrornas","volume":"8","author":"Basu","year":"2018","journal-title":"Biomed. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1002\/pd.5572","article-title":"DiGeorge syndrome chromosome region deletion and duplication: Prenatal genotype-phenotype variability in fetal ultrasound and MRI","volume":"39","author":"Tramontana","year":"2019","journal-title":"Prenat. Diagn."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e00739","DOI":"10.1002\/mgg3.739","article-title":"Phenotypic features of a microdeletion in chromosome band 20p13: A case report and review of the literature","volume":"7","author":"Fang","year":"2019","journal-title":"Mol. Genet. Genom. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101943","DOI":"10.1016\/j.media.2020.101943","article-title":"A novel chromosome cluster types identification method using ResNeXt WSL model","volume":"69","author":"Lin","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"301","DOI":"10.12688\/f1000research.84360.1","article-title":"Automated human chromosome segmentation and feature extraction: Current trends and prospects","volume":"11","author":"Balagalla","year":"2022","journal-title":"F1000Research"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.measurement.2013.08.033","article-title":"Analysis of human chromosome classification using centromere position","volume":"47","author":"Madian","year":"2014","journal-title":"Measurement"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Minaee, S., Fotouhi, M., and Khalaj, B.H. (2014, January 13). A Geometric Approach for Fully Automatic Chromosome Segmentation. Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, Philadelphia, PA, USA.","DOI":"10.1109\/SPMB.2014.7163174"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1109\/TMI.2008.916962","article-title":"A Multichannel Watershed-Based Segmentation Method for Multispectral Chromosome Classification","volume":"27","author":"Karvelis","year":"2008","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.patcog.2007.05.013","article-title":"Automatic segmentation of metaphase cells based on global context and variant analysis","volume":"41","author":"Ritter","year":"2008","journal-title":"Pattern Recognit."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Madian, N., and Jayanthi, K.B. (September, January 28). Overlapped Chromosome Segmentation And Separation Of Touching Chromosome For Automated Chromosome Classification. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA.","DOI":"10.1109\/EMBC.2012.6347213"},{"key":"ref_11","first-page":"315","article-title":"A New Technique for Edge Detection of Chromosome G-Band Images for Segmentation","volume":"Volume 55","author":"Sobecki","year":"2014","journal-title":"Advanced Approaches to Intelligent Information and Database Systems"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.cmpb.2011.12.003","article-title":"A review of thresholding strategies applied to human chromosome segmentation","volume":"108","author":"Poletti","year":"2012","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"277","DOI":"10.52292\/j.laar.2014.452","article-title":"A local adaptive threshold approach to assist automatic chromosome image segmentation","volume":"44","year":"2014","journal-title":"Lat. Am. Appl. Res. Int. J."},{"key":"ref_14","first-page":"654","article-title":"Automatic Segmentation of Chromosome Cells","volume":"Volume 845","author":"Hassanien","year":"2018","journal-title":"Advances in Intelligent Systems and Computing"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gawande, J.P., Manohar, R., Gawande, J.P., Manohar, R., Gawande, J.P., and Manohar, R. (2017, January 5\u20137). Watershed and Clustering Based Segmentation of Chromosome Images. Proceedings of the IEEE 7th International Advance Computing Conference, Hyderabad, India.","DOI":"10.1109\/IACC.2017.0145"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sharma, M., Saha, O., Sriraman, A., Hebbalaguppe, R., and Karande, S. (2017, January 21\u201326). Crowdsourcing for Chromosome Segmentation and Deep Classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.109"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lin, C., Yin, A., Wu, Q., Chen, H., Guo, L., Zhao, G., Fan, X., Luo, H., and Tang, H. (2020, January 16\u201319). Chromosome Cluster Identification Framework Based on Geometric Features and Machine Learning Algorithms. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, Seoul, Korea.","DOI":"10.1109\/BIBM49941.2020.9313369"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"101988","DOI":"10.1016\/j.bspc.2020.101988","article-title":"Dense Contour-Imbalance Aware framework for Colon Gland Instance Segmentation","volume":"60","author":"Mei","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bhutto, J.A., Tian, L., Du, Q., Sun, Z., Yu, L., and Tahir, M.F. (2022). CT and MRI Medical Image Fusion Using Noise-Removal and Contrast Enhancement Scheme with Convolutional Neural Network. Entropy, 24.","DOI":"10.3390\/e24030393"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1002\/cyto.a.23375","article-title":"Semantic segmentation of mFISH images using convolutional networks","volume":"93","author":"Pardo","year":"2018","journal-title":"Cytom. Part A"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.procs.2019.09.207","article-title":"Overlapping Chromosome Segmentation using U-Net: Convolutional Networks with Test Time Augmentation","volume":"159","author":"Saleh","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_23","unstructured":"Hu, R.L., Karnowski, J., Fadely, R., and Pommier, J.P. (2017). Image segmentation to distinguish between overlapping human chromosomes. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, P., Cai, J., and Yang, L. (2020, January 20\u201324). Chromosome Segmentation via Data Simulation and Shape Learning. Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (Embc), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176020"},{"key":"ref_25","first-page":"117","article-title":"Raw G-Band Chromosome Image Segmentation Using U-Net Based Neural Network","volume":"Volume 3","author":"Rutkowski","year":"2019","journal-title":"Artificial Intelligence and Soft Computing"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"118650","DOI":"10.1109\/ACCESS.2019.2934476","article-title":"Semi-Supervised Fine-Grained Image Categorization Using Transfer Learning with Hierarchical Multi-Scale Adversarial Networks","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation","volume":"39","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-To-Image Translation With Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Berman, M., Triki, A.R., and Blaschko, M.B. (2018, January 18\u201322). The Lov\u2019asz-Softmax Loss: A Tractable Surrogate for The Optimization of the Intersection-Over-Union Measure in Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00464"},{"key":"ref_30","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, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, J., Qiu, T., and Qi, W. (2021). An Adaptive Deblurring Vehicle Detection Method for High-Speed Moving Drones: Resistance to Shake. Entropy, 23.","DOI":"10.3390\/e23101358"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.1109\/TMM.2019.2895292","article-title":"FuseGAN: Learning to Fuse Multi-Focus Image via Conditional Generative Adversarial Network","volume":"21","author":"Guo","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y.K., Wang, Z., and Smolley, S.P. (2017, January 22\u201329). The Least Squares Generative Adversarial Networks. Proceedings of the International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref_34","unstructured":"Maas, A.L., Hannun, A.Y., and Ng, A.Y. (2013, January 16\u201321). The Rectifier Nonlinearities Improve Neural Network Acoustic Models. Proceedings of the International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_35","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Taha, A.A., and Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Med. Imaging, 15.","DOI":"10.1186\/s12880-015-0068-x"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2366","DOI":"10.1109\/TMI.2015.2433900","article-title":"A Stochastic Polygons Model for Glandular Structures in Colon Histology Images","volume":"34","author":"Sirinukunwattana","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_38","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2018). ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and Sang, N. (2018, January 8\u201314). In Bisenet: Bilateral segmentation network for real-time semantic segmentation. Proceedings of the European Conference on Computer Vision (ECCV 2018), Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","article-title":"BiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation","volume":"129","author":"Yu","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-Decoder with Atrous Separable Convolution For Semantic Image Segmentation. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_42","unstructured":"Wu, H., Zhang, J., Huang, K., Liang, K., and Yu, Y. (2019). Fastfcn: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"014006","DOI":"10.1117\/1.JMI.6.1.014006","article-title":"Recurrent residual U-Net for medical image segmentation","volume":"6","author":"Alom","year":"2019","journal-title":"J. Med. Imaging"},{"key":"ref_44","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., Mcdonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention U-Net: Learning Where To Look For The Pancreas. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., and Tang, X. (2017, January 21\u201326). Residual Attention Network for Image Classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref_46","unstructured":"Pommier, J. (2022, March 29). Overlapping Chromosomes. Available online: https:\/\/www.kaggle.com\/jeanpat\/overlapping-chromosomes."},{"key":"ref_47","unstructured":"Pommier, J. (2021, July 13). Overlapping Chromosomes. Available online: https:\/\/github.com\/jeanpat\/DeepFISH\/tree\/master\/dataset."},{"key":"ref_48","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., and Chintala, S. (2019). Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst., 32."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"CE-Net: Context Encoder Network for 2D Medical Image Segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5653","DOI":"10.3233\/JIFS-201466","article-title":"Segmentation of overlapping chromosome images using U-Net with improved dilated convolutions","volume":"40","author":"Sun","year":"2021","journal-title":"J. Intell. Fuzzy Syst."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/4\/522\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:49:58Z","timestamp":1760136598000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/4\/522"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,7]]},"references-count":50,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["e24040522"],"URL":"https:\/\/doi.org\/10.3390\/e24040522","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,7]]}}}