@article{LameckerZachowHegeetal.2006, author = {Lamecker, Hans and Zachow, Stefan and Hege, Hans-Christian and Z{\"o}ckler, Maja}, title = {Surgical treatment of craniosynostosis based on a statistical 3D-shape model}, volume = {1(1)}, journal = {Int. J. Computer Assisted Radiology and Surgery}, doi = {10.1007/s11548-006-0024-x}, pages = {253 -- 254}, year = {2006}, language = {en} } @article{PimentelSzengelEhlkeetal.2020, author = {Pimentel, Pedro and Szengel, Angelika and Ehlke, Moritz and Lamecker, Hans and Zachow, Stefan and Estacio, Laura and Doenitz, Christian and Ramm, Heiko}, title = {Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks}, volume = {12439}, journal = {Towards the Automatization of Cranial Implant Design in Cranioplasty}, editor = {Li, Jianning and Egger, Jan}, edition = {1}, publisher = {Springer International Publishing}, doi = {10.1007/978-3-030-64327-0_3}, pages = {16 -- 27}, year = {2020}, abstract = {We present an automated method for extrapolating missing regions in label data of the skull in an anatomically plausible manner. The ultimate goal is to design patient-speci� c cranial implants for correcting large, arbitrarily shaped defects of the skull that can, for example, result from trauma of the head. Our approach utilizes a 3D statistical shape model (SSM) of the skull and a 2D generative adversarial network (GAN) that is trained in an unsupervised fashion from samples of healthy patients alone. By � tting the SSM to given input labels containing the skull defect, a First approximation of the healthy state of the patient is obtained. The GAN is then applied to further correct and smooth the output of the SSM in an anatomically plausible manner. Finally, the defect region is extracted using morphological operations and subtraction between the extrapolated healthy state of the patient and the defective input labels. The method is trained and evaluated based on data from the MICCAI 2020 AutoImplant challenge. It produces state-of-the art results on regularly shaped cut-outs that were present in the training and testing data of the challenge. Furthermore, due to unsupervised nature of the approach, the method generalizes well to previously unseen defects of varying shapes that were only present in the hidden test dataset.}, language = {en} } @article{ZachowLameckerZoeckleretal.2009, author = {Zachow, Stefan and Lamecker, Hans and Z{\"o}ckler, Maja and Haberl, Ernst}, title = {Computergest{\"u}tzte Planung zur chirurgischen Korrektur von fr{\"u}hkindlichen Sch{\"a}delfehlbildungen (Craniosynostosen)}, journal = {Face 02/09, Int. Mag. of Orofacial Esthetics, Oemus Journale Leipzig}, pages = {48 -- 53}, year = {2009}, language = {en} } @article{HaberlHellZoeckleretal.2004, author = {Haberl, Hannes and Hell, Bertold and Z{\"o}ckler, Maja and Zachow, Stefan and Lamecker, Hans and Sarrafzadeh, Asita and Riecke, B. and Langsch, Wolfgang and Deuflhard, Peter and Bier, J{\"u}rgen and Brock, Mario}, title = {Technical aspects and results of surgery for craniosynostosis}, volume = {65}, journal = {Zentralblatt f{\"u}r Neurochirurgie}, number = {2}, pages = {65 -- 74}, year = {2004}, language = {en} } @inproceedings{LameckerZoecklerHaberletal.2005, author = {Lamecker, Hans and Z{\"o}ckler, Maja and Haberl, Hannes and Zachow, Stefan and Hege, Hans-Christian}, title = {Statistical shape modeling for craniosynostosis planning}, booktitle = {2nd International Conference Advanced Digital Technology in Head and Neck Reconstruction 2005, Abstract Volume}, address = {Banff, Alberta}, pages = {64}, year = {2005}, language = {en} } @article{LameckerZachowHaberletal.2005, author = {Lamecker, Hans and Zachow, Stefan and Haberl, Hannes and Stiller, Michael}, title = {Medical applications for statistical shape models}, volume = {17 (258)}, journal = {Computer Aided Surgery around the Head, Fortschritt-Berichte VDI - Biotechnik/Medizintechnik}, pages = {61}, year = {2005}, language = {en} } @phdthesis{Zoeckler2006, author = {Z{\"o}ckler, Maja}, title = {Modellgebundene Cranioplastie - Operationstechnik zur Umformung fr{\"u}hkindlicher Sch{\"a}deldeformit{\"a}ten unter Verwendung dreidimensionaler Standardformmodelle aus MRT-basierten Rekonstruktionen nicht deformierter Kinder}, year = {2006}, language = {de} } @article{HochfeldLameckerThomaleetal.2014, author = {Hochfeld, Mascha and Lamecker, Hans and Thomale, Ulrich W. and Schulz, Matthias and Zachow, Stefan and Haberl, Hannes}, title = {Frame-based cranial reconstruction}, volume = {13}, journal = {Journal of Neurosurgery: Pediatrics}, number = {3}, doi = {10.3171/2013.11.PEDS1369}, pages = {319 -- 323}, year = {2014}, abstract = {The authors report on the first experiences with the prototype of a surgical tool for cranial remodeling. The device enables the surgeon to transfer statistical information, represented in a model, into the disfigured bone. The model is derived from a currently evolving databank of normal head shapes. Ultimately, the databank will provide a set of standard models covering the statistical range of normal head shapes, thus providing the required template for any standard remodeling procedure as well as customized models for intended overcorrection. To date, this technique has been used in the surgical treatment of 14 infants (age range 6-12 months) with craniosynostosis. In all 14 cases, the designated esthetic result, embodied by the selected model, has been achieved, without morbidity or mortality. Frame-based reconstruction provides the required tools to precisely realize the surgical reproduction of the model shape. It enables the establishment of a self-referring system, feeding back postoperative growth patterns, recorded by 3D follow-up, into the model design.}, language = {en} } @article{LiPimentelSzengeletal.2021, author = {Li, Jianning and Pimentel, Pedro and Szengel, Angelika and Ehlke, Moritz and Lamecker, Hans and Zachow, Stefan and Estacio, Laura and Doenitz, Christian and Ramm, Heiko and Shi, Haochen and Chen, Xiaojun and Matzkin, Franco and Newcombe, Virginia and Ferrante, Enzo and Jin, Yuan and Ellis, David G. and Aizenberg, Michele R. and Kodym, Oldrich and Spanel, Michal and Herout, Adam and Mainprize, James G. and Fishman, Zachary and Hardisty, Michael R. and Bayat, Amirhossein and Shit, Suprosanna and Wang, Bomin and Liu, Zhi and Eder, Matthias and Pepe, Antonio and Gsaxner, Christina and Alves, Victor and Zefferer, Ulrike and von Campe, Cord and Pistracher, Karin and Sch{\"a}fer, Ute and Schmalstieg, Dieter and Menze, Bjoern H. and Glocker, Ben and Egger, Jan}, title = {AutoImplant 2020 - First MICCAI Challenge on Automatic Cranial Implant Design}, volume = {40}, journal = {IEEE Transactions on Medical Imaging}, number = {9}, issn = {0278-0062}, doi = {10.1109/TMI.2021.3077047}, pages = {2329 -- 2342}, year = {2021}, abstract = {The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use.}, language = {en} }