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Considering the cumbersome volumetric image acquisition, there is a lack of paired CLP CBCTs and normal CBCTs for learning\u2010based anatomical structure restoration models. Nowadays, the registration\u2010based method relieves the annotation burden, though one\u2010shot registration and the regular mask are limited to handling fine\u2010grained shape variations and harmony between restored bony tissues and the defected\u00a0maxilla.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study aimed to design and evaluate a novel method for deformable partial registration of the CLP CBCTs and normal CBCTs, enabling personalized maxilla completion and cleft defect volume prediction from CLP\u00a0CBCTs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We proposed an adaptable deep registration framework for personalized maxilla completion and cleft defect volume prediction from CLP CBCTs. The key ingredient was a cascaded partial registration to exploit the maxillary morphology prior and attribute transfer. Cascaded registration with coarse\u2010to\u2010fine registration fields handled morphological variations of cleft defects and fine\u2010grained maxillary restoration. We designed an adaptable cleft defect mask and volumetric Boolean operators for reliable voxel filling of the defected maxilla. A total of 36 clinically obtained CLP CBCTs were used to train and validate the proposed model, among which 22 CLP CBCTs were used to generate a training dataset with 440 synthetic CBCTs by B\u2010spline deformation\u2010based data augmentation and the remaining for testing. The proposed model was evaluated on maxilla completion and cleft defect volume prediction from clinically obtained unilateral and bilateral CLP\u00a0CBCTs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Extensive experiments demonstrated the effectiveness of the adaptable cleft defect mask and the cascaded partial registration on maxilla completion and cleft defect volume prediction. The proposed method achieved state\u2010of\u2010the\u2010art performances with the Dice similarity coefficient of 0.90  0.02 on the restored maxilla and 0.84  0.04 on the estimated cleft defect, respectively. The average Hausdorff distance between the estimated cleft defect and the manually annotated ground truth was 0.30  0.08 mm. The relative volume error of the cleft defect was 0.08. The proposed model allowed for the prediction of cleft defect maps that were in line with the ground truth in the challenging unilateral and bilateral CLP CBCTs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The results suggest that the proposed adaptable deep registration model enables patient\u2010specific maxilla completion and automatic annotation of cleft defects, relieving tedious voxel\u2010wise annotation and image acquisition\u00a0burdens.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17046","type":"journal-article","created":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T19:29:01Z","timestamp":1711913341000},"page":"4283-4296","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Adaptable cascaded registration for personalized maxilla completion and cleft defect volume estimation"],"prefix":"10.1002","volume":"51","author":[{"given":"Yungeng","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Machine Perception (MOE) Department of Machine Intelligence School of Intelligence Science and Technology Peking University Beijing China"},{"name":"China Telecom Research Institute Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuru","family":"Pei","sequence":"additional","affiliation":[{"name":"Key Laboratory of Machine Perception (MOE) Department of Machine Intelligence School of Intelligence Science and Technology Peking University Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixiao","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Machine Perception (MOE) Department of Machine Intelligence School of Intelligence Science and Technology Peking University Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Si","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Stomatology Stomatology Hospital Peking University Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi\u2010bo","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Stomatology Stomatology Hospital Peking University Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianmin","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Stomatology Stomatology Hospital Peking University Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbin","family":"Zha","sequence":"additional","affiliation":[{"name":"Key Laboratory of Machine Perception (MOE) Department of Machine Intelligence School of Intelligence Science and Technology Peking University Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,3,31]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1259\/dmfr.20180047"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcms.2019.01.013"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/SCS.0b013e31825c75ba"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1089\/ten.tec.2009.0824"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1597\/13-260"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1597\/13-287"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1597\/14-290"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.prs.0000293870.64781.12"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oooo.2011.10.001"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bjoms.2007.12.010"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1097\/PRS.0b013e3182818e4f"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijom.2010.04.050"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.4915636"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-016-1454-8"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oooo.2017.04.003"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0111126"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1597\/06-054"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1597\/13-045"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcms.2017.02.018"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3005241"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0016520"},{"key":"e_1_2_9_23_1","doi-asserted-by":"crossref","unstructured":"DaiA QiCR Nie\u00dfnerM.Shape completion using 3D\u2010encoder\u2010predictor CNNs and shape synthesis. 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