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Accurate and automated segmentation of ovarian tumors in contrast\u2010enhanced computed tomography (CECT) images is crucial in the radiotherapy treatment of ovarian cancer, enabling radiologists to evaluate cancer progression and develop timely therapeutic plans. However, automatic ovarian tumor segmentation is challenging due to factors such as inhomogeneous background, ambiguous tumor boundaries, and imbalanced foreground\u2010background, all of which contribute to high predictive uncertainty for a segmentation\u00a0model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To tackle these challenges, we propose an uncertainty\u2010aware refinement framework that aims to estimate and refine regions with high predictive uncertainty for accurate ovarian tumor segmentation in CECT\u00a0images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To this end, we first employ an approximate Bayesian network to detect coarse regions of interest (ROIs) of both ovarian tumors and uncertain regions. These ROIs allow a subsequent segmentation network to narrow down the search area for tumors and prioritize uncertain regions, resulting in precise segmentation of ovarian tumors. Meanwhile, the framework integrates two guidance modules that learn two implicit functions capable of mapping query features sampled according to their uncertainty to organ or boundary manifolds, guiding the segmentation network to facilitate information encoding of uncertain\u00a0regions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Firstly, 367 CECT images are collected from the same hospital for experiments. Dice score, Jaccard, Recall, Positive predictive value (PPV), 95% Hausdorff distance (HD95) and Average symmetric surface distance (ASSD) for the testing group of 77 cases are 86.31%, 73.93%, 83.95%, 86.03%, 15.17\u00a0 mm and 2.57\u00a0 mm, all of which are significantly better than that of the other state\u2010of\u2010the\u2010art models. And results of visual comparison shows that the compared methods have more mis\u2010segmentation than our method. Furthermore, our method achieves a Dice score that is at least 20% higher than the Dice scores of other compared methods when tumor volumes are less than 20 cm, indicating better recognition ability to small regions by our method. And then, 38 CECT images are collected from another hospital to form an external testing group. Our approach consistently outperform the compared methods significantly, with the external testing group exhibiting substantial improvements across key evaluation metrics: Dice score (83.74%), Jaccard (69.55%), Recall (82.12%), PPV (81.61%), HD95 (12.31\u00a0mm), and ASSD (2.32\u00a0mm), robustly establishing its superior\u00a0performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Experimental results demonstrate that the framework significantly outperforms the compared state\u2010of\u2010the\u2010art methods, with decreased under\u2010 or over\u2010segmentation and better small tumor identification. It has the potential for clinical\u00a0application.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16795","type":"journal-article","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T18:26:01Z","timestamp":1697826361000},"page":"2678-2694","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Uncertainty\u2010aware refinement framework for ovarian tumor segmentation in CECT volume"],"prefix":"10.1002","volume":"51","author":[{"given":"Jiaqi","family":"Hu","sequence":"first","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Department of Gynecologic Oncology Women's Hospital and Institute of Translational Medicine Zhejiang University School of Medicine Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiming","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering &amp; State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering &amp; State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China"},{"name":"School of Information Science and Technology Northwest University Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiadong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering &amp; State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyan","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering &amp; State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China"},{"name":"Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honghe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Pathology, Research Unit of Intelligence Classification of Tumor Pathology and Precision Therapy Chinese Academy of Medical Sciences Zhejiang University School of Medicine Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Lu","sequence":"additional","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Department of Gynecologic Oncology Women's Hospital and Institute of Translational Medicine Zhejiang University School of Medicine Hangzhou Zhejiang China"},{"name":"Cancer center Zhejiang University Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering &amp; State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China"},{"name":"Shanghai United Imaging Intelligence Co., Ltd. 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