{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T13:12:51Z","timestamp":1788873171503,"version":"build-2803163510"},"reference-count":31,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005719","name":"Dicle University","doi-asserted-by":"publisher","award":["TIP.26.056"],"award-info":[{"award-number":["TIP.26.056"]}],"id":[{"id":"10.13039\/501100005719","id-type":"DOI","asserted-by":"publisher"}]},{"award":["TIP.26.056"],"award-info":[{"award-number":["TIP.26.056"]}],"id":[{"id":"https:\/\/ror.org\/0257dtg16","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Clin. Med.","JCM"],"abstract":"<jats:p>Background: Accurate white blood cell (WBC) segmentation is important for quantitative microscopic image analysis, but conventional supervised approaches depend on labor-intensive pixel-level annotations and may exhibit reduced robustness across datasets. This study proposes a detector-guided, region of interest (ROI)-constrained framework that combines automatic localization, pseudo-mask-based weak supervision, and diffusion-assisted segmentation. Methods: You Only Look Once version 13 Nano (YOLOv13-N) was used to localize WBCs and define ROIs, within which segmentation was performed using the proposed diffusion-assisted model. Automatically generated pseudo-masks served as segmentation-training targets, while expert masks were retained for reference evaluation. Experiments used a verified Dicle cohort of 14,721 records, partitioned into 10,305 training, 2208 validation, and 2208 held-out test records. Performance was evaluated separately at ROI-conditional and end-to-end levels. Generalization was assessed on 11,200 Raabin-WBC records using the frozen framework without external tuning. Results: The automatically generated pseudo-masks achieved a mean Dice score of approximately 0.702, demonstrating usable but imperfect weak supervision. On the held-out Dicle test set, the proposed framework achieved a Dice score of 0.7188, intersection over union (IoU) of 0.5796, precision of 0.8966, and recall of 0.6422 under ROI-conditional evaluation. End-to-end performance was 0.6705 Dice, 0.5408 IoU, 0.8378 precision, and 0.5986 recall, demonstrating the influence of localization on overall performance. Comparison with reference segmentation architectures showed that the proposed framework did not maximize internal Dice, while achieving the highest reported ROI-conditional precision. Frozen external evaluation on Raabin-WBC revealed further degradation under dataset shift, with failure analysis identifying detector\/ROI transfer as an important end-to-end bottleneck. Conclusions: The proposed framework demonstrates the feasibility of WBC segmentation using detector-guided ROI processing and pseudo-mask-based weak supervision, reducing reliance on expert pixel-level segmentation targets. The findings further show that robust cross-dataset localization is critical to end-to-end generalization and provide a clear direction for improving weakly supervised WBC segmentation across heterogeneous microscopy datasets.<\/jats:p>","DOI":"10.3390\/jcm15176846","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T17:13:25Z","timestamp":1788455605000},"page":"6846","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detection-Guided ROI-Constrained Diffusion for Weakly Supervised White Blood Cell Segmentation: A Retrospective Internal and External Dataset Evaluation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6549-940X","authenticated-orcid":false,"given":"Julius","family":"Bamwenda","sequence":"first","affiliation":[{"name":"Electrical & Electronics Engineering Department, Engineering Faculty, Dicle University, 21280 Diyarbak\u0131r, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9368-8902","authenticated-orcid":false,"given":"Mehmet Sira\u00e7","family":"\u00d6zerdem","sequence":"additional","affiliation":[{"name":"Electrical & Electronics Engineering Department, Engineering Faculty, Dicle University, 21280 Diyarbak\u0131r, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5673-8408","authenticated-orcid":false,"given":"Orhan","family":"Ayyildiz","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine-Hematology, Medical Faculty, Dicle University, 21280 Diyarbak\u0131r, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]},{"given":"Veysi","family":"Akpolat","sequence":"additional","affiliation":[{"name":"Department of Biophysics, Medical Faculty, Dicle University, 21280 Diyarbak\u0131r, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"\u0130rem","family":"Akpolat","sequence":"additional","affiliation":[{"name":"Department of Nutrition and Dietetics, \u0130stanbul Okan University, 34959 \u0130stanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1002\/cyto.a.24839","article-title":"Comprehensive data analysis of white blood cells with classification and segmentation by using deep learning approaches","volume":"105","author":"Uyar","year":"2024","journal-title":"Cytom. Part A"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107875","DOI":"10.1016\/j.compbiomed.2023.107875","article-title":"WBC YOLO-ViT: 2 Way\u20132 stage white blood cell detection and classification with a combination of YOLOv5 and Vision Transformer","volume":"169","author":"Tarimo","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1038\/s41598-021-04426-x","article-title":"A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm","volume":"12","author":"Kouzehkanan","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Springer. LNCS 9351.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_5","unstructured":"Oktay, O., Schlemper, J., Le Folgoc, 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_6","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer. LNCS 11045.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1049\/cit2.12216","article-title":"Weakly supervised machine learning","volume":"8","author":"Ren","year":"2023","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107840","DOI":"10.1016\/j.compbiomed.2023.107840","article-title":"Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation","volume":"169","author":"Jiao","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_10","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2022). Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation. Proceedings of the European Conference on Computer Vision Workshops, Springer.","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., and Xu, D. (2022). UNETR: Transformers for 3D Medical Image Segmentation. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, IEEE.","DOI":"10.1109\/WACV51458.2022.00181"},{"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.-Y. (2023). Segment Anything. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102918","DOI":"10.1016\/j.media.2023.102918","article-title":"Segment Anything Model for medical image analysis: An experimental study","volume":"89","author":"Mazurowski","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103061","DOI":"10.1016\/j.media.2023.103061","article-title":"Segment anything model for medical images?","volume":"92","author":"Huang","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"108238","DOI":"10.1016\/j.compbiomed.2024.108238","article-title":"Segment anything model for medical image segmentation: Current applications and future directions","volume":"171","author":"Zhang","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_18","unstructured":"Cheng, J., Ye, J., Deng, Z., Chen, J., Li, T., Wang, H., Su, Y., Huang, Z., Chen, J., and Jiang, L. (2023). SAM-Med2D. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103547","DOI":"10.1016\/j.media.2025.103547","article-title":"Medical SAM adapter: Adapting Segment Anything Model for medical image segmentation","volume":"102","author":"Wu","year":"2025","journal-title":"Med. Image Anal."},{"key":"ref_20","unstructured":"Ravi, N., Gabeur, V., Hu, Y.T., Hu, R., Ryali, C., Ma, T., Khedr, H., R\u00e4dle, R., Rolland, C., and Gustafson, L. (2025, January 24\u201328). SAM 2: Segment Anything in Images and Videos. Proceedings of the International Conference on Learning Representations, Singapore."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ma, J., Kim, S., Li, F., Baharoon, M., Asakereh, R., Lyu, H., and Wang, B. (2024). Segment Anything in Medical Images and Videos: Benchmark and Deployment. arXiv.","DOI":"10.1038\/s41467-024-44824-z"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103370","DOI":"10.1016\/j.media.2024.103370","article-title":"MedLSAM: Localize and segment anything model for 3D CT images","volume":"99","author":"Lei","year":"2025","journal-title":"Med. Image Anal."},{"key":"ref_23","first-page":"6840","article-title":"Denoising Diffusion Probabilistic Models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102846","DOI":"10.1016\/j.media.2023.102846","article-title":"Diffusion models in medical imaging: A comprehensive survey","volume":"88","author":"Kazerouni","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_25","first-page":"1623","article-title":"MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model","volume":"227","author":"Wu","year":"2024","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_26","first-page":"6030","article-title":"MedSegDiff-V2: Diffusion-Based Medical Image Segmentation with Transformer","volume":"38","author":"Wu","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4890","DOI":"10.1109\/JBHI.2023.3300179","article-title":"Cluster-Re-Supervision: Bridging the Gap Between Image-Level and Pixel-Wise Labels for Weakly Supervised Medical Image Segmentation","volume":"27","author":"Kuang","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2936","DOI":"10.1109\/TMI.2023.3269523","article-title":"Weakly-Supervised 3D Medical Image Segmentation Using Geometric Prior and Contrastive Similarity","volume":"42","author":"Du","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bamwenda, J., \u00d6zerdem, M.S., Ayyildiz, O., Akpolat, V., and Akpolat, \u0130. (2026). TransNet\u2013SAM2: A Transformer\u2013Foundation Model Framework for Prompt-Free Segmentation of White Blood Cells in Microscopic Blood Smear Images. Diagnostics, 16.","DOI":"10.3390\/diagnostics16111737"},{"key":"ref_30","unstructured":"Lei, M., Li, S., Wu, Y., Hu, H., Zhou, Y., Zheng, X., Ding, G., Du, S., Wu, Z., and Gao, Y. (2025). YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception. arXiv."},{"key":"ref_31","unstructured":"Loshchilov, I., and Hutter, F. (2019, January 6\u20139). Decoupled Weight Decay Regularization. Proceedings of the International Conference on Learning Representations (ICLR), New Orleans, LA, USA."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/17\/6846\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T13:01:37Z","timestamp":1788872497000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/17\/6846"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,3]]},"references-count":31,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["jcm15176846"],"URL":"https:\/\/doi.org\/10.3390\/jcm15176846","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,3]]}}}