{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:30:48Z","timestamp":1760149848627,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T00:00:00Z","timestamp":1693785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>In this study, we develop an innovative method that assists computer-aided diagnosis in the determination process of the exact location of the femoral neck junction in plain radiographs. Our algorithm consists of two phases, i.e., coarse prediction and fine matching, which are implemented by supervised deep learning method and unsupervised clustering, respectively. In coarse prediction, standard masks are first produced by a specialist and trained in our proposed feature propagation network (FPU-Net) with supervised learning on the femoral neck dataset. In fine matching, the standard masks are first classified into different categories using our proposed three parameters with unsupervised learning. The predicted mask from FPU-Net is matched with each category of standard masks by calculating the values of intersection of union (IOU), and finally the predicted mask is substituted by the standard mask with the largest IOU value. A total of 4320 femoral neck parts in anterior\u2013posterior (AP) pelvis radiographs collected from China Medical University Hospital database were used to test our method. Simulation results show that, on the one hand, compared with other segmentation methods, the method proposed in this paper has a larger IOU value and better suppression of noise outside the region of interest; on the other hand, the introduction of unsupervised learning for fine matching can help in the accurate localization segmentation of femoral neck images. Accurate femoral neck segmentation can assist surgeons to diagnose and reduce the misdiagnosis rate and burden.<\/jats:p>","DOI":"10.3390\/diagnostics13172855","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T10:14:11Z","timestamp":1693822451000},"page":"2855","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Combination of FPU-Net and Feature Clustering Methods for Accurate Segmentation of Femoral Neck in Radiographic Diagnosis"],"prefix":"10.3390","volume":"13","author":[{"given":"Y. Y.","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence and Computer Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C. H.","family":"Chuang","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Computer Engineering, National Chin-Yi University of Technology, Taichung 411030, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. L.","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, China Medical University Hospital, Taichung 404327, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"T. L.","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, China Medical University Hospital, Taichung 404327, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C. J.","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, China Medical University Hospital, Taichung 404327, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1007\/s00402-010-1105-4","article-title":"Initially missed occult fractures of the proximal femur in elderly patients: Implications for need of operation and their morbidity","volume":"130","author":"Kim","year":"2010","journal-title":"Arch. Orthop. Trauma Surg."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"S1","DOI":"10.1016\/8756-3282(93)90341-7","article-title":"Hip fractures: A worldwide problem today and tomorrow","volume":"14","author":"Melton","year":"1993","journal-title":"Bone"},{"key":"ref_3","first-page":"2990","article-title":"Hip fractures-an enormous public health problem","volume":"103","author":"Thorngren","year":"2006","journal-title":"Lakartidningen"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1016\/j.bone.2007.02.027","article-title":"The societal burden of osteoporosis in Sweden","volume":"40","author":"Sobocki","year":"2007","journal-title":"Bone"},{"key":"ref_5","first-page":"236","article-title":"Challenges in the treatment of femoral neck fractures in the nonelderly adult","volume":"68","author":"Davidovitch","year":"2010","journal-title":"J. Trauma"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1177\/1120700018761154","article-title":"The effect of patient, fracture and surgery on outcomes of high energy neck of femur fractures in patients aged 15\u201350","volume":"29","author":"Hoskins","year":"2019","journal-title":"Hip Int."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1016\/j.injury.2015.11.001","article-title":"Missed subtle fractures on the trauma-meeting digital projector","volume":"47","author":"Chellam","year":"2016","journal-title":"Injury"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy. retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/s41591-018-0307-0","article-title":"The practical implementation of artificial intelligence technologies, medicine","volume":"25","author":"He","year":"2019","journal-title":"Nat. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"749","DOI":"10.3348\/kjr.2018.0530","article-title":"Effect of a deep learning framework-based computer-aided diagnosis system on the diagnostic performance of radiologists in differentiating between malignant and benign masses on breast ultrasonography","volume":"20","author":"Choi","year":"2019","journal-title":"Korean J. Radiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"185016","DOI":"10.1088\/1361-6560\/aada6c","article-title":"Cascaded atrous convolution and spatial pyramid pooling for more accurate tumor target segmentation for rectal cancer radiotherapy","volume":"63","author":"Men","year":"2018","journal-title":"Phys. Med. Biol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5469","DOI":"10.1007\/s00330-019-06167-y","article-title":"Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs","volume":"29","author":"Cheng","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TMI.2016.2553401","article-title":"Guest Editorial. Deep learning in medical imaging: Overview and future promise of an exciting new technique","volume":"35","author":"Greenspan","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.neuroimage.2009.12.059","article-title":"Robust atrophy rate measurement in alzheimer\u2019s disease using multi-site serial mri: Tissue-specific intensity normalization and parameter selection","volume":"50","author":"Leung","year":"2010","journal-title":"Neuroimage"},{"key":"ref_15","unstructured":"Ahmad, N. (2022, August 15). Good Positioning Is Key to PA Chest X-ray Exams. Available online: https:\/\/www.auntminnie.com\/index.aspx?sec=log&itemID=51950."},{"key":"ref_16","unstructured":"Frank, E.D., Long, B.W., and Smith, B.J. (2013). Merrill\u2019s Atlas of Radiographic Positioning and Procedures-E-Book, Elsevier Health Sciences."},{"key":"ref_17","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 (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_18","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.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., and Liang, J. (2018). Unet++: A Nested U-Net Architecture for Medical Image Segmentation, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_20","first-page":"2663","article-title":"H-denseunet: Hybrid densely connected unet for liver and tumor segmentation from ct volumes","volume":"8","author":"Li","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.cmpb.2018.08.003","article-title":"The region of interest localization for glaucoma analysis from retinal fundus image using deep learning","volume":"165","author":"Mitra","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_22","unstructured":"Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (1996). Advances in Knowledge Discovery and Data Mining, AAAI\/MIT Press."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1145\/331499.331504","article-title":"Flynn, and Data clustering: A review","volume":"31","author":"Jain","year":"1999","journal-title":"ACM Comput. Surv."},{"key":"ref_24","first-page":"11","article-title":"Binarization of MRI with Intensity Inhomogeneity using K-Means Clustering for Segmenting Hippocampus","volume":"5","author":"Somasundaram","year":"2013","journal-title":"Int. J. Multimed. Its Appl."},{"key":"ref_25","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_26","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). Attentionu-net: Learning where to look for the pancreas. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6625688","DOI":"10.1155\/2021\/6625688","article-title":"R2au-net: Attention recurrent residual convolutional neural network for multimodal medical image segmentation","volume":"2021","author":"Zuo","year":"2021","journal-title":"Secur. Commun. Netw."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Moldovan, F., Gligor, A., and Bataga, T. (2021). Structured Integration and Alignment Algorithm: A Tool for Personalized Surgical Treatment of Tibial Plateau Fractures. J. Pers. Med., 11.","DOI":"10.3390\/jpm11030190"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/17\/2855\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:46:02Z","timestamp":1760129162000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/17\/2855"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,4]]},"references-count":28,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["diagnostics13172855"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13172855","relation":{},"ISSN":["2075-4418"],"issn-type":[{"type":"electronic","value":"2075-4418"}],"subject":[],"published":{"date-parts":[[2023,9,4]]}}}