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By amalgamating an array of deep learning models, the segmentation module proficiently labels 17 discrete abdominal targets within the images. We introduce a knowledge\u2010based seed distribution initialization methodology that discerns the most analogous tumor shape in the reference treatment plan from the knowledge base. Subsequently, the seed distribution from the reference plan is transmuted to the current case, thus establishing seed distribution initialization. Furthermore, we parameterize the puncture needles and seeds, while concurrently constraining the puncture needle angle through the employment of a virtual puncture panel to augment planning algorithm efficiency. We also presented a user interface that includes a range of interactive features, seamlessly integrated with the treatment planning generation function.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The multi\u2010organ segmentation module, which is trained by 50 cases of in\u2010house CT scans and 694 cases of publicly available CT scans, achieved average Dice of 0.80 and Hausdorff distance of 5.2\u00a0mm in testing datasets. The results demonstrate that knowledge\u2010based initialization exhibits a marked enhancement in expediting the convergence rate. Our TPS also demonstrates a dominant advantage in dose\u2010volume\u2010histogram criteria and execution time in comparison to commercial TPS.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The study proposes an innovative treatment planning system for low\u2010dose\u2010rate permanent seed brachytherapy for hepatic malignancies. We show that the generated treatment plans meet clinical requirement.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16627","type":"journal-article","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T06:14:19Z","timestamp":1689920059000},"page":"348-362","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Embedding expertise knowledge into inverse treatment planning for low\u2010dose\u2010rate brachytherapy of hepatic malignancies"],"prefix":"10.1002","volume":"51","author":[{"given":"Jianjun","family":"Zhu","sequence":"first","affiliation":[{"name":"Hanglok\u2010Tech Co., Ltd. Hengqin China"},{"name":"Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School Southeast University Nanjing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Hanglok\u2010Tech Co., Ltd. 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