{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T16:54:57Z","timestamp":1777913697605,"version":"3.51.4"},"reference-count":42,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Ultrason Imaging"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:p>Detecting ovarian structures in ultrasound images is essential in gynecological and reproductive medicine. An automated detection system can serve as a valuable tool for physicians and assist in complex ultrasound interpretations. This study presents a CNN-based object detector designed to segment and count follicle regions in ovarian ultrasound images. Automated identification of ovarian follicles can aid in diagnosing conditions such as infertility, Polycystic Ovarian Syndrome (PCOS), ovarian cancer, and other reproductive health issues. The proposed model, Multi-Attention Residual Dilated UNet with Squeeze and Excitation (MARDSE-UNet), integrates residual UNet, dilated UNet, and squeeze-and-excitation blocks to enhance follicle detection performance. MARDSE-UNet achieved exceptional results, with 98.69% accuracy, 97.89% precision, 97.7% recall, an F1-score of 86.97%, and Intersection over Union (IoU) of 95.66% in follicle detection using 5-fold cross-validation. The USOVA3D dataset was used for experimentation. The model also incorporates a novel preprocessing stage to address noise and low contrast issues, as well as a post-processing stage to refine edges and extract features such as area, perimeter, and diameter of follicles for a more comprehensive performance comparison. The proposed model outperformed traditional CNN models and other state-of-the-art methods in comparative evaluations.<\/jats:p>","DOI":"10.1177\/01617346251378401","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T09:39:27Z","timestamp":1761298767000},"page":"83-99","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Follicle Counting From Ultrasound Images of Ovaries Using MARDSE-UNET Model"],"prefix":"10.1177","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7041-6640","authenticated-orcid":false,"given":"Debasmita","family":"Saha","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Gour Banga, Malda, West Bengal, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4685-7283","authenticated-orcid":false,"given":"Ardhendu","family":"Mandal","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Technology, University of North Bengal, Siliguri, West Bengal, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8773-4717","authenticated-orcid":false,"given":"Akhil Kumar","family":"Das","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Gour Mahavidyalaya, Malda, West Bengal, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9455-1644","authenticated-orcid":false,"given":"Arijit","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Gour Mahavidyalaya, Malda, West Bengal, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,10,24]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Physiology, Female Reproduction","author":"Rosner J","year":"2025","unstructured":"Rosner J, Samardzic T, Sarao MS. Physiology, Female Reproduction. StatPearls Publishing; 2025."},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-809880-6.00070-9"},{"key":"e_1_3_2_4_2","volume-title":"Physiology, Ovulation","author":"Holesh JE","year":"2025","unstructured":"Holesh JE, Bass AN, Lord M. Physiology, Ovulation. StatPearls Publishing; 2025."},{"key":"e_1_3_2_5_2","volume-title":"Endotext","author":"Deadmond A","year":"2000","unstructured":"Deadmond A, Koch CA, Parry JP. Ovarian reserve testing. In: Feingold KR, Anawalt B, Boyce A, et al, eds. Endotext. MDText.com, Inc.; 2000"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-82904-8"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-818148-5.00003-5"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13051244"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-21724-0"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tjog.2024.10.001"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.3389\/frph.2022.877216"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.gltp.2021.08.010"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2024.101504"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIIP.2011.6108968"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISPA.2000.914907"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2010.10.002"},{"issue":"2","key":"e_1_3_2_17_2","first-page":"7","article-title":"Follicle detection in digital ultrasound images using adaptive K-means clustering algorithm","volume":"14","author":"Rao MJ","year":"2019","unstructured":"Rao MJ, Kumar DRK. Follicle detection in digital ultrasound images using adaptive K-means clustering algorithm. Int J Comput Appl. 2019;14(2):7.","journal-title":"Int J Comput Appl"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-7834-2_51"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.9756\/BIJSESC.9017"},{"issue":"6","key":"e_1_3_2_20_2","first-page":"17","article-title":"Automated ovarian classification in digital ultrasound images using SVM","volume":"1","author":"Tegnoor JR","year":"2012","unstructured":"Tegnoor JR. Automated ovarian classification in digital ultrasound images using SVM. Int J Eng Res. 2012;1(6):17.","journal-title":"Int J Eng Res"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.35940\/ijeat.D3419.0411422"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.12.047"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107515"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-024-01465-1"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.01.033"},{"issue":"12","key":"e_1_3_2_26_2","first-page":"12","article-title":"A review on speckle noise reduction in ultrasound images by comparing various filters","volume":"4","author":"Chauhan P","year":"2015","unstructured":"Chauhan P, Kaushik V. A review on speckle noise reduction in ultrasound images by comparing various filters. Int J Innov Sci Eng Technol. 2015;4(12):12.","journal-title":"Int J Innov Sci Eng Technol"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/1055\/1\/012116"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-97-6588-1_18"},{"key":"e_1_3_2_29_2","unstructured":"Ultrasound energy \u2013 an overview. ScienceDirect Topics. https:\/\/www.sciencedirect.com\/topics\/medicine-and-dentistry\/ultrasound-energy. Accessed Feb 27 2025."},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2024.102234"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10453-z"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1093\/humrep\/deaa012"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.30326"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105621"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-39940-9_565"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnano.2022.972421"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-020-0109-6"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-09954-8"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-56706-x"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3606367"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","unstructured":"Zhao Q Huang G Lin C Cai J Jia J Adam H et al Unbiased IoU for spherical image object detection. arXiv. Published August 18 2021. doi:10.48550\/arXiv.2108.08029.","DOI":"10.48550\/arXiv.2108.08029"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-81935-3_25"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.fraope.2024.10049"}],"container-title":["Ultrasonic Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/01617346251378401","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/01617346251378401","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/01617346251378401","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:29:31Z","timestamp":1777652971000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/01617346251378401"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,24]]},"references-count":42,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["10.1177\/01617346251378401"],"URL":"https:\/\/doi.org\/10.1177\/01617346251378401","relation":{},"ISSN":["0161-7346","1096-0910"],"issn-type":[{"value":"0161-7346","type":"print"},{"value":"1096-0910","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,24]]}}}