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However, this task faces significant challenges: traditional segmentation methods are often limited by parameter sensitivity and manual intervention, while purely deep learning approaches are constrained by the scarcity of annotated medical data and struggle with irregular, low\u2010contrast tumor margins. To overcome these limitations, we propose a fully automatic hybrid pipeline that combines AI\u2010based localization with an interpretable segmentation algorithm. The method first uses a pretrained MONAI 3D RetinaNet detector to localize tumors and generate seeds, followed by a four\u2010point ensemble region growing process with adaptive thresholding and dynamic constraints, and finally reconstructs the tumor using the Marching Cubes algorithm. Evaluated on selected cases from the LIDC\u2010IDRI dataset, the proposed method achieved a mean Dice score of 0.935 for tumor segmentation, exceeding other models like I\u20103D DenseUNet (0.831), 3D MultiResUNet (0.866), and a hybrid CNN (0.720). By synergizing deep learning for robust localization and a constrained region\u2010growing algorithm for precise boundary delineation, our method provides stable\u00a03D tumor contours and interactive models, showing great promise for assisting thoracoscopic virtual preoperative planning.<\/jats:p>","DOI":"10.1002\/ima.70355","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T05:46:49Z","timestamp":1776664009000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>3D<\/scp>\n                    Segmentation of Lung Tumors for Stereotactic Diagnosis and Virtual Preoperative Planning"],"prefix":"10.1002","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2837-5584","authenticated-orcid":false,"given":"Donghui","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering Heilongjiang University of Science and Technology  Harbin Heilongjiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4917-3361","authenticated-orcid":false,"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering Heilongjiang University of Science and Technology  Harbin Heilongjiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,19]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11427-024-2657-y"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2019.0130"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/2962047"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40644-025-00928-3"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.4329\/wjr.v17.i8.107732"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-59200-8"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.21037\/tlcr-2025-460"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/1759-7714.70159"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.net.2020.03.011"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-023-01126-y"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10080981"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1177\/15330338221090847"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104649"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106946"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm11247334"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-024-10751-2"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1235461"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2022.07.2312"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118661"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.70107"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2024.3467279"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/app12136318"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-025-14094-z"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2022.07.004"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-023-02330-4"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.3390\/a16120564"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12122971"},{"key":"e_1_2_10_30_1","doi-asserted-by":"crossref","unstructured":"T. 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