{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:58:38Z","timestamp":1764784718649,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"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":["62103228"],"award-info":[{"award-number":["62103228"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Nuclei detection is a fundamental task in the field of histopathology image analysis and remains challenging due to cellular heterogeneity. Recent studies explore convolutional neural networks to either isolate them with sophisticated boundaries (segmentation-based methods) or locate the centroids of the nuclei (counting-based approaches). Although these two methods have demonstrated superior success, their fully supervised training demands considerable and laborious pixel-wise annotations manually labeled by pathology experts. To alleviate such tedious effort and reduce the annotation cost, we propose a novel local integral regression network (LIRNet) that allows both fully and weakly supervised learning (FSL\/WSL) frameworks for nuclei detection. Furthermore, the LIRNet can output an exquisite density map of nuclei, in which the localization of each nucleus is barely affected by the post-processing algorithms. The quantitative experimental results demonstrate that the FSL version of the LIRNet achieves a state-of-the-art performance compared to other counterparts. In addition, the WSL version has exhibited a competitive detection performance and an effortless data annotation that requires only 17.5% of the annotation effort.<\/jats:p>","DOI":"10.3390\/e23101336","type":"journal-article","created":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T09:10:49Z","timestamp":1634202649000},"page":"1336","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Local Integral Regression Network for Cell Nuclei Detection"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5121-5640","authenticated-orcid":false,"given":"Xiao","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Automation, Tsinghua University, Beijing 100084, China"}]},{"given":"Miao","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua University, Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8197-5560","authenticated-orcid":false,"given":"Zhen","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua University, Beijing 100084, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108ra113","DOI":"10.1126\/scitranslmed.3002564","article-title":"Systematic analysis of breast cancer morphology uncovers stromal features associated with survival","volume":"3","author":"Beck","year":"2011","journal-title":"Sci. Transl. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"157ra143","DOI":"10.1126\/scitranslmed.3004330","article-title":"Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling","volume":"4","author":"Yuan","year":"2012","journal-title":"Sci. Transl. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.1109\/TMI.2013.2275151","article-title":"Computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies","volume":"32","author":"Filipczuk","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1109\/TMI.2016.2525803","article-title":"Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images","volume":"35","author":"Sirinukunwattana","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"doi-asserted-by":"crossref","unstructured":"Khan, A., Gould, S., and Salzmann, M. (2016, January 8\u201316). Deep convolutional neural networks for human embryonic cell counting. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","key":"ref_5","DOI":"10.1007\/978-3-319-46604-0_25"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1109\/TIP.2018.2795742","article-title":"Her2Net: A deep framework for semantic segmentation and classification of cell membranes and nuclei in breast cancer evaluation","volume":"27","author":"Saha","year":"2018","journal-title":"IEEE Trans. Image Process."},{"unstructured":"Xie, C., Vanderbilt, C.M., Grabenstetter, A., and Fuchs, T.J. (2019, January 8\u201310). VOCA: Cell nuclei detection in histopathology images by vector oriented confidence accumulation. Proceedings of the International Conference on Medical Imaging with Deep Learning, PMLR, London, UK.","key":"ref_7"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/TMI.2018.2865709","article-title":"Segmentation of nuclei in histopathology images by deep regression of the distance map","volume":"38","author":"Naylor","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3257","DOI":"10.1109\/TMI.2019.2927182","article-title":"Deep adversarial training for multi-organ nuclei segmentation in histopathology images","volume":"39","author":"Mahmood","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"doi-asserted-by":"crossref","unstructured":"Zhou, Y., Dou, Q., Chen, H., Qin, J., and Heng, P.A. (2018, January 2\u20137). SFCN-OPI: Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction. Proceedings of the 32th AAAI Conference on Artificial Intelligence (AAAI 2018), New Orleans, LA, USA.","key":"ref_10","DOI":"10.1609\/aaai.v32i1.11900"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.media.2017.07.003","article-title":"Efficient and robust cell detection: A structured regression approach","volume":"44","author":"Xie","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_12","first-page":"283","article-title":"Microscopy cell counting and detection with fully convolutional regression networks","volume":"6","author":"Xie","year":"2018","journal-title":"Comp. Meth. Biomech. Biomed. Eng."},{"doi-asserted-by":"crossref","unstructured":"Guo, Y., Stein, J., Wu, G., and Krishnamurthy, A. (2019, January 7\u201310). SAU-Net: A Universal Deep Network for Cell Counting. Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, Niagara Falls, NY, USA.","key":"ref_13","DOI":"10.1145\/3307339.3342153"},{"unstructured":"Larsen, A.B.L., S\u00f8nderby, S.K., Larochelle, H., and Winther, O. (2016, January 19\u201324). Autoencoding beyond pixels using a learned similarity metric. Proceedings of the International Conference on Machine Learning, PMLR, New York, NY, USA.","key":"ref_14"},{"doi-asserted-by":"crossref","unstructured":"Xue, Y., Ray, N., Hugh, J., and Bigras, G. (2016, January 8\u201316). Cell counting by regression using convolutional neural network. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","key":"ref_15","DOI":"10.1007\/978-3-319-46604-0_20"},{"doi-asserted-by":"crossref","unstructured":"Paul Cohen, J., Boucher, G., Glastonbury, C.A., Lo, H.Z., and Bengio, Y. (2017, January 22\u201329). Count-ception: Counting by Fully Convolutional Redundant Counting. Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, Venice, Italy.","key":"ref_16","DOI":"10.1109\/ICCVW.2017.9"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3655","DOI":"10.1109\/TMI.2020.3002244","article-title":"Weakly supervised deep nuclei segmentation using partial points annotation in histopathology images","volume":"39","author":"Qu","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1448","DOI":"10.1109\/TMI.2012.2190089","article-title":"An Integrated Region-, Boundary-, Shape-Based Active Contour for Multiple Object Overlap Resolution in Histological Imagery","volume":"31","author":"Ali","year":"2012","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.1109\/TBME.2014.2303852","article-title":"Breast Cancer Histopathology Image Analysis: A Review","volume":"61","author":"Veta","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s10916-017-0863-8","article-title":"Correlation Filters for Detection of Cellular Nuclei in Histopathology Images","volume":"42","author":"Ahmad","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1109\/TMI.2017.2677499","article-title":"A dataset and a technique for generalized nuclear segmentation for computational pathology","volume":"36","author":"Kumar","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"unstructured":"Corredor, G., Wang, X., Lu, C., Velcheti, V., Romero, E., and Madabhushi, A. (2018, January 10\u201315). A watershed and feature-based approach for automated detection of lymphocytes on lung cancer images. Proceedings of the Medical Imaging 2018: Digital Pathology. International Society for Optics and Photonics, Houston, TX, USA.","key":"ref_22"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1186\/s12938-018-0518-0","article-title":"Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images","volume":"17","author":"Salvi","year":"2018","journal-title":"Biomed. Eng. Online"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.patcog.2018.09.007","article-title":"Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images","volume":"86","author":"Hou","year":"2019","journal-title":"Pattern Recognit."},{"doi-asserted-by":"crossref","unstructured":"Xie, Y., Xing, F., Kong, X., Su, H., and Yang, L. (2015, January 5\u20139). Beyond classification: Structured regression for robust cell detection using convolutional neural network. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","key":"ref_25","DOI":"10.1007\/978-3-319-24574-4_43"},{"doi-asserted-by":"crossref","unstructured":"Zhu, R., Sui, D., Qin, H., and Hao, A. (2017, January 23\u201325). An extended type cell detection and counting method based on FCN. Proceedings of the 17th International Conference on Bioinformatics and Bioengineering (BIBE), Washington, DC, USA.","key":"ref_26","DOI":"10.1109\/BIBE.2017.00-79"},{"doi-asserted-by":"crossref","unstructured":"Raza, S.E.A., AbdulJabbar, K., Jamal-Hanjani, M., Veeriah, S., Le Quesne, J., Swanton, C., and Yuan, Y. (2019, January 8\u201311). Deconvolving Convolutional Neural Network for Cell Detection. Proceedings of the 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy.","key":"ref_27","DOI":"10.1109\/ISBI.2019.8759333"},{"doi-asserted-by":"crossref","unstructured":"Hagos, Y.B., Narayanan, P.L., Akarca, A.U., Marafioti, T., and Yuan, Y. (2019, January 13\u201317). ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection in Multiplex Immunohistochemistry Images. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Shenzhen, China.","key":"ref_28","DOI":"10.1007\/978-3-030-32239-7_74"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/TMI.2015.2458702","article-title":"Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images","volume":"35","author":"Xu","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"doi-asserted-by":"crossref","unstructured":"Rad, R.M., Saeedi, P., Au, J., and Havelock, J. (2018, January 29\u201331). Blastomere cell counting and centroid localization in microscopic images of human embryo. Proceedings of the 20th International Workshop on Multimedia Signal Processing (MMSP), Vancouver, BC, Canada.","key":"ref_30","DOI":"10.1109\/MMSP.2018.8547107"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"81945","DOI":"10.1109\/ACCESS.2019.2920933","article-title":"Cell-Net: Embryonic Cell Counting and Centroid Localization via Residual Incremental Atrous Pyramid and Progressive Upsampling Convolution","volume":"7","author":"Saeedi","year":"2019","journal-title":"IEEE Access"},{"unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (July, January 26). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","key":"ref_32"},{"doi-asserted-by":"crossref","unstructured":"Choe, J., and Shim, H. (2019, January 16\u201320). Attention-based dropout layer for weakly supervised object localization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","key":"ref_33","DOI":"10.1109\/CVPR.2019.00232"},{"doi-asserted-by":"crossref","unstructured":"Singh, K.K., and Lee, Y.J. (2017, January 22\u201329). Hide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","key":"ref_34","DOI":"10.1109\/ICCV.2017.381"},{"unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., and Yoo, Y. (November, January 27). Cutmix: Regularization strategy to train strong classifiers with localizable features. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","key":"ref_35"},{"doi-asserted-by":"crossref","unstructured":"Zhang, X., Wei, Y., Feng, J., Yang, Y., and Huang, T.S. (2018, January 18\u201322). Adversarial complementary learning for weakly supervised object localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","key":"ref_36","DOI":"10.1109\/CVPR.2018.00144"},{"doi-asserted-by":"crossref","unstructured":"Zhang, X., Wei, Y., Kang, G., Yang, Y., and Huang, T. (2018, January 8\u201314). Self-produced guidance for weakly-supervised object localization. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","key":"ref_37","DOI":"10.1007\/978-3-030-01258-8_37"},{"doi-asserted-by":"crossref","unstructured":"Kainz, P., Urschler, M., Schulter, S., Wohlhart, P., and Lepetit, V. (2015, January 5\u20139). You Should Use Regression to Detect Cells. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention\u2014MICCAI, Munich, Germany.","key":"ref_38","DOI":"10.1007\/978-3-319-24574-4_33"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.1109\/TMI.2019.2895318","article-title":"Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection","volume":"38","author":"Tofighi","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"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 International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","key":"ref_40","DOI":"10.1007\/978-3-319-24574-4_28"},{"unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","key":"ref_41"},{"doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201322). Non-Local Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","key":"ref_42","DOI":"10.1109\/CVPR.2018.00813"},{"unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, U., and Polosukhin, I. (2017, January 4\u20139). Attention is All You Need. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA.","key":"ref_43"},{"unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL, USA.","key":"ref_44"},{"doi-asserted-by":"crossref","unstructured":"Zhou, X., Cheng, Z., Gu, M., and Chang, F. (2020, January 16\u201319). LIRNet: Local Integral Regression Network for Both Strongly and Weakly Supervised Nuclei Detection. Proceedings of the International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea.","key":"ref_45","DOI":"10.1109\/BIBM49941.2020.9313265"},{"doi-asserted-by":"crossref","unstructured":"Neubeck, A., and Van Gool, L. (2006, January 20\u201324). Efficient non-maximum suppression. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China.","key":"ref_46","DOI":"10.1109\/ICPR.2006.479"},{"unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv.","key":"ref_47"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"S2","DOI":"10.4103\/2153-3539.92028","article-title":"Local isotropic phase symmetry measure for detection of beta cells and lymphocytes","volume":"2","author":"Kuse","year":"2011","journal-title":"J. Pathol. Informat."},{"doi-asserted-by":"crossref","unstructured":"Tofighi, M., Guo, T., Vanamala, J.K., and Monga, V. (2018, January 7\u201310). Deep networks with shape priors for nucleus detection. Proceedings of the 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","key":"ref_49","DOI":"10.1109\/ICIP.2018.8451797"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/10\/1336\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:12:54Z","timestamp":1760166774000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/10\/1336"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,14]]},"references-count":49,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["e23101336"],"URL":"https:\/\/doi.org\/10.3390\/e23101336","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2021,10,14]]}}}