{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T00:15:26Z","timestamp":1788308126654,"version":"build-2803163510"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T00:00:00Z","timestamp":1737417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFF0721400"],"award-info":[{"award-number":["2023YFF0721400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["62273247"],"award-info":[{"award-number":["62273247"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023YFF0721400"],"award-info":[{"award-number":["2023YFF0721400"]}],"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":["62273247"],"award-info":[{"award-number":["62273247"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sperm morphology assessment plays a vital role in semen analysis and the diagnosis of male infertility. By quantitatively analyzing the morphological characteristics of the sperm head, midpiece, and tail, it provides essential insights for assisted reproductive technologies (ARTs), such as in vitro fertilization (IVF). However, traditional manual evaluation methods not only rely on staining procedures that can damage the cells but also suffer from strong subjectivity and inconsistent results, underscoring the urgent need for an automated, accurate, and non-invasive method for multi-sperm morphology assessment. To address the limitations of existing techniques, this study proposes a novel method that combines a multi-scale part parsing network with a measurement accuracy enhancement strategy for non-stained sperm morphology analysis. First, a multi-scale part parsing network integrating semantic segmentation and instance segmentation is introduced to achieve instance-level parsing of sperm, enabling precise measurement of morphological parameters for each individual sperm instance. Second, to eliminate measurement errors caused by the reduced resolution of non-stained sperm images, a measurement accuracy enhancement method based on statistical analysis and signal processing is designed. This method employs an interquartile range (IQR) method to exclude outliers, Gaussian filtering to smooth data, and robust correction techniques to extract the maximum morphological features of sperm. Experimental results demonstrate that the proposed multi-scale part parsing network achieves 59.3% APvolp, surpassing the state-of-the-art AIParsing by 9.20%. Compared to evaluations based solely on segmentation results, the integration of the measurement accuracy enhancement strategy significantly reduces measurement errors, with the largest reduction in errors for head, midpiece, and tail measurements reaching up to 35.0%.<\/jats:p>","DOI":"10.3390\/s25030592","type":"journal-article","created":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T05:47:42Z","timestamp":1737438462000},"page":"592","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Stained-Free Sperm Morphology Measurement Method Based on Multi-Target Instance Parsing and Measurement Accuracy Enhancement"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0177-505X","authenticated-orcid":false,"given":"Miao","family":"Hao","sequence":"first","affiliation":[{"name":"Research Center of Robotics and Micro Systems, School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4466-2626","authenticated-orcid":false,"given":"Rongan","family":"Zhai","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changhai","family":"Ru","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Yang","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Soochow University, Suzhou 215129, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1111\/and.12324","article-title":"How accurate is sperm morphology as an indicator of sperm function?","volume":"47","author":"Franken","year":"2015","journal-title":"Andrologia"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1002\/j.1939-4640.1986.tb00913.x","article-title":"Morphometric analysis of spermatozoa in the assessment of human male fertility","volume":"74","author":"Katz","year":"1986","journal-title":"J. Androl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1308","DOI":"10.1016\/j.fertnstert.2021.06.016","article-title":"Quantitative selection of single human sperm with high DNA integrity for intracytoplasmic sperm injection","volume":"116","author":"Zhang","year":"2021","journal-title":"Fertil. Steril."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.compbiomed.2019.04.032","article-title":"Automatic segmentation of Sperm\u2019s parts in microscopic images of human semen smears using concatenated learning approaches","volume":"109","author":"Movahed","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1038\/aja.2008.51","article-title":"Practical semen analysis: From A to Z","volume":"12","author":"Brazil","year":"2010","journal-title":"Asian J. Androl."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, R., Wang, M., Wang, M., Yin, J., Yuan, Y., and Liu, J. (2021). Automatic Microscopy Analysis with Transfer Learning for Classification of Human Sperm. Appl. Sci., 11.","DOI":"10.3390\/app11125369"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, J., Zha, X., Zhou, Y., Cao, Y., and Chen, D. (2022, January 28\u201331). Improving Human Sperm Head Morphology Classification With Unsupervised Anatomical Feature Distillation. Proceedings of the 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India.","DOI":"10.1109\/ISBI52829.2022.9761633"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2799","DOI":"10.1109\/TRO.2022.3158200","article-title":"Robotic Manipulation of Sperm as a Deformable Linear Object","volume":"38","author":"Dai","year":"2022","journal-title":"IEEE Trans. Robot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1017\/S1431927622012132","article-title":"Fast noninvasive morphometric characterization of free human sperms using deep learning","volume":"28","author":"Liu","year":"2022","journal-title":"Microsc. Microanal."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fraczek, A., Karwowska, G., Miler, M., Lis, J., Jezierska, A., and Mazur-Milecka, M. (2022, January 28\u201331). Sperm segmentation and abnormalities detection during the icsi procedure using machine learning algorithms. Proceedings of the 2022 15th International Conference on Human System Interaction (HSI), Melbourne, Australia.","DOI":"10.1109\/HSI55341.2022.9869511"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W. (2023, January 1\u20136). Segment Anything. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., and Girdhar, R. (2022, January 18\u201324). Masked-attention Mask Transformer for Universal Image Segmentation. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"ref_15","unstructured":"Zou, X., Yang, J., Zhang, H., Li, F., Li, L., Gao, J., and Lee, Y.J. (2023, January 10\u201316). Segment Everything Everywhere All at Once. Proceedings of the 37th International Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, LA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mar\u00edn, R., and Chang, V. (2021). Impact of transfer learning for human sperm segmentation using deep learning. Comput. Biol. Med., 136.","DOI":"10.1016\/j.compbiomed.2021.104687"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1017\/S0967199421000307","article-title":"Importance of real-time measurement of sperm head morphology in intracytoplasmic sperm injection","volume":"30","author":"Itoi","year":"2022","journal-title":"Zygote"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"39","DOI":"10.5534\/wjmh.230050","article-title":"Artificial intelligence in andrology: From semen analysis to image diagnostics","volume":"42","author":"Ghayda","year":"2023","journal-title":"World J. Men\u2019s Health"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7128","DOI":"10.1109\/TMM.2022.3217413","article-title":"Quality-Aware Network for Human Parsing","volume":"25","author":"Yang","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gong, K., Liang, X., Li, Y., Chen, Y., Yang, M., and Lin, L. (2022, January 8\u201314). Instance-level Human Parsing via Part Grouping Network. Proceedings of the 2018 15th European Conference Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01225-0_47"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10949","DOI":"10.1609\/aaai.v34i07.6728","article-title":"Grapy-ML: Graph Pyramid Mutual Learning for Cross-Dataset Human Parsing","volume":"34","author":"He","year":"2020","journal-title":"AAAI"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5599","DOI":"10.1109\/TIP.2022.3192989","article-title":"AIParsing: Anchor-Free Instance-Level Human Parsing","volume":"31","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3260","DOI":"10.1109\/TPAMI.2020.3048039","article-title":"Self-Correction for Human Parsing","volume":"44","author":"Li","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","article-title":"Deep High-Resolution Representation Learning for Visual Recognition","volume":"43","author":"Wang","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"104128","DOI":"10.1016\/j.imavis.2021.104128","article-title":"Interactive multi-scale feature representation enhancement for small object detection","volume":"108","author":"Zheng","year":"2008","journal-title":"Image Vis. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2257","DOI":"10.1109\/TMI.2018.2840827","article-title":"Automated Non-Invasive Measurement of Single Sperm\u2019s Motility and Morphology","volume":"37","author":"Dai","year":"2018","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1097\/JU.0000000000002903","article-title":"Staining-free, Automated Sperm Analysis for In Vitro Fertilization Lab Use","volume":"208","author":"Dai","year":"2022","journal-title":"J. Urol."},{"key":"ref_29","unstructured":"Dong, C., Loy, C.C., He, K., and Tang, X. (2015, January 7\u201313). Learning a Deep Convolutional Network for Image Super-Resolution. Proceedings of the 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), Santiago, Chile."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Loy, C.C., Qiao, Y., and Tang, X. (2018, January 8\u201314). ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. Proceedings of the ECCV 2018 Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yang, L., Song, Q., Wang, Z., and Jiang, M. (2019, January 15\u201320). Parsing R-CNN for Instance-Level Human Analysis. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00045"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., and Lin, L. (2019, January 15\u201320). Graphonomy: Universal Human Parsing via Graph Transfer Learning. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00763"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s00138-010-0288-6","article-title":"Fast and robust laser stripe extraction for 3D reconstruction in industrial environments","volume":"23","author":"Usamentiaga","year":"2012","journal-title":"Mach. Vis. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhou, J., Huang, F., and Liu, L. (2017). Sub-Pixel Extraction of Laser Stripe Center Using an Improved Gray-Gravity Method. Sensors, 17.","DOI":"10.3390\/s17040814"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/34.659930","article-title":"An Unbiased Detector of Curvilinear Structures","volume":"20","author":"Steger","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1109\/TIM.2007.915103","article-title":"Robust and Accurate Surface Measurement Using Structured Light","volume":"57","author":"Yang","year":"2008","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zuiderveld, K.J. (1994). Contrast Limited Adaptive Histogram Equalization. Graphics Gems IV, ACM.","DOI":"10.1016\/B978-0-12-336156-1.50061-6"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Opoku, M., Weyori, B.A., Adekoya, A.F., and Adu, K. (2023). CLAHE-CapsNet: Efficient retina optical coherence tomography classification using capsule networks with contrast limited adaptive histogram equalization. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0288663"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gu, S., Zuo, W., Xie, Q., Meng, D., Feng, X., and Zhang, L. (2015, January 7\u201313). Convolutional Sparse Coding for Image Super-Resolution. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.212"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cetin, A.E., Bozkurt, A., G\u00fcnay, O., Habiboglu, Y.H., K\u00f6se, K., Onaran, I., Tofighi, M., and Sevimli, R.A. (2013, January 3\u20135). Projections onto convex sets (POCS) based optimization by lifting. Proceedings of the 2013 IEEE Global Conference on Signal and Information Processing, Austin, TX, USA.","DOI":"10.1109\/GlobalSIP.2013.6736960"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R.B., He, K., Hariharan, B., and Belongie, S.J. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). FCOS: Fully Convolutional One-Stage Object Detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00972"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/3\/592\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:32:30Z","timestamp":1759919550000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/3\/592"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,21]]},"references-count":42,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["s25030592"],"URL":"https:\/\/doi.org\/10.3390\/s25030592","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,21]]}}}