@article{GladilinZachowDeuflhardetal.2004, author = {Gladilin, Evgeny and Zachow, Stefan and Deuflhard, Peter and Hege, Hans-Christian}, title = {Anatomy- and physics-based facial animation for craniofacial surgery simulations}, volume = {42(2)}, journal = {Med Biol Eng Comput.}, doi = {10.1007/BF02344627}, pages = {167 -- 170}, year = {2004}, language = {en} } @article{ZachowGladilinTrepczynskietal.2002, author = {Zachow, Stefan and Gladilin, Evgeny and Trepczynski, Adam and Sader, Robert and Zeilhofer, Hans-Florian}, title = {3D Osteotomy Planning in Cranio-Maxillofacial Surgery: Experiences and Results of Surgery Planning and Volumetric Finite-Element Soft Tissue Prediction in Three Clinical Cases}, journal = {Computer Assisted Radiology and Surgery (CARS)}, publisher = {Springer Verlag}, pages = {983 -- 987}, year = {2002}, language = {en} } @article{LameckerZachowWittmersetal.2006, author = {Lamecker, Hans and Zachow, Stefan and Wittmers, Antonia and Weber, Britta and Hege, Hans-Christian and Elsholtz, Barbara and Stiller, Michael}, title = {Automatic segmentation of mandibles in low-dose CT-data}, volume = {1(1)}, journal = {Int. J. Computer Assisted Radiology and Surgery}, pages = {393 -- 395}, year = {2006}, language = {en} } @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{ZachowHegeDeuflhard2006, author = {Zachow, Stefan and Hege, Hans-Christian and Deuflhard, Peter}, title = {Computer assisted planning in cranio-maxillofacial surgery}, volume = {14(1)}, journal = {Journal of Computing and Information Technology}, pages = {53 -- 64}, year = {2006}, language = {en} } @article{ZachowLameckerElsholtzetal.2006, author = {Zachow, Stefan and Lamecker, Hans and Elsholtz, Barbara and Stiller, Michael}, title = {Is the course of the mandibular nerve deducible from the shape of the mandible?}, journal = {Int. J. of Computer Assisted Radiology and Surgery}, publisher = {Springer}, pages = {415 -- 417}, year = {2006}, language = {en} } @article{ZachowSteinmannHildebrandtetal.2006, author = {Zachow, Stefan and Steinmann, Alexander and Hildebrandt, Thomas and Weber, Rainer and Heppt, Werner}, title = {CFD simulation of nasal airflow: Towards treatment planning for functional rhinosurgery}, journal = {Int. J. of Computer Assisted Radiology and Surgery}, publisher = {Springer}, pages = {165 -- 167}, year = {2006}, language = {en} } @article{HildebrandtBrueningSchmidtetal.2019, author = {Hildebrandt, Thomas and Bruening, Jan Joris and Schmidt, Nora Laura and Lamecker, Hans and Heppt, Werner and Zachow, Stefan and Goubergrits, Leonid}, title = {The Healthy Nasal Cavity - Characteristics of Morphology and Related Airflow Based on a Statistical Shape Model Viewed from a Surgeon's Perspective}, volume = {35}, journal = {Facial Plastic Surgery}, number = {1}, doi = {10.1055/s-0039-1677721}, pages = {9 -- 13}, year = {2019}, abstract = {Functional surgery on the nasal framework requires referential criteria to objectively assess nasal breathing for indication and follow-up. Thismotivated us to generate amean geometry of the nasal cavity based on a statistical shape model. In this study, the authors could demonstrate that the introduced nasal cavity's mean geometry features characteristics of the inner shape and airflow, which are commonly observed in symptom-free subjects. Therefore, the mean geometry might serve as a reference-like model when one considers qualitative aspects. However, to facilitate quantitative considerations and statistical inference, further research is necessary. Additionally, the authorswere able to obtain details about the importance of the isthmus nasi and the inferior turbinate for the intranasal airstream.}, language = {en} } @article{HildebrandtBrueningLameckeretal.2019, author = {Hildebrandt, Thomas and Bruening, Jan Joris and Lamecker, Hans and Zachow, Stefan and Heppt, Werner and Schmidt, Nora and Goubergrits, Leonid}, title = {Digital Analysis of Nasal Airflow Facilitating Decision Support in Rhinosurgery}, volume = {35}, journal = {Facial Plastic Surgery}, number = {1}, doi = {10.1055/s-0039-1677720}, pages = {1 -- 8}, year = {2019}, abstract = {Successful functional surgery on the nasal framework requires reliable and comprehensive diagnosis. In this regard, the authors introduce a new methodology: Digital Analysis of Nasal Airflow (diANA). It is based on computational fluid dynamics, a statistical shape model of the healthy nasal cavity and rhinologic expertise. diANA necessitates an anonymized tomographic dataset of the paranasal sinuses including the complete nasal cavity and, when available, clinical information. The principle of diANA is to compare the morphology and the respective airflow of an individual nose with those of a reference. This enablesmorphometric aberrations and consecutive flow field anomalies to localize and quantify within a patient's nasal cavity. Finally, an elaborated expert opinion with instructive visualizations is provided. Using diANA might support surgeons in decision-making, avoiding unnecessary surgery, gaining more precision, and target-orientation for indicated operations.}, language = {en} } @article{AmbellanTackEhlkeetal.2019, author = {Ambellan, Felix and Tack, Alexander and Ehlke, Moritz and Zachow, Stefan}, title = {Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative}, volume = {52}, journal = {Medical Image Analysis}, number = {2}, doi = {10.1016/j.media.2018.11.009}, pages = {109 -- 118}, year = {2019}, abstract = {We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures. The shape models and neural networks employed are trained using data of the Osteoarthritis Initiative (OAI) and the MICCAI grand challenge "Segmentation of Knee Images 2010" (SKI10), respectively. We evaluate our method on 40 validation and 50 submission datasets of the SKI10 challenge. For the first time, an accuracy equivalent to the inter-observer variability of human readers has been achieved in this challenge. Moreover, the quality of the proposed method is thoroughly assessed using various measures for data from the OAI, i.e. 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy for both OAI datasets. We made the 507 manual segmentations as well as our experimental setup publicly available to further aid research in the field of medical image segmentation. In conclusion, combining statistical anatomical knowledge via SSMs with the localized classification via CNNs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data.}, language = {en} } @article{AlHajjSahuLamardetal.2019, author = {Al Hajj, Hassan and Sahu, Manish and Lamard, Mathieu and Conze, Pierre-Henri and Roychowdhury, Soumali and Hu, Xiaowei and Marsalkaite, Gabija and Zisimopoulos, Odysseas and Dedmari, Muneer Ahmad and Zhao, Fenqiang and Prellberg, Jonas and Galdran, Adrian and Araujo, Teresa and Vo, Duc My and Panda, Chandan and Dahiya, Navdeep and Kondo, Satoshi and Bian, Zhengbing and Bialopetravicius, Jonas and Qiu, Chenghui and Dill, Sabrina and Mukhopadyay, Anirban and Costa, Pedro and Aresta, Guilherme and Ramamurthy, Senthil and Lee, Sang-Woong and Campilho, Aurelio and Zachow, Stefan and Xia, Shunren and Conjeti, Sailesh and Armaitis, Jogundas and Heng, Pheng-Ann and Vahdat, Arash and Cochener, Beatrice and Quellec, Gwenole}, title = {CATARACTS: Challenge on Automatic Tool Annotation for cataRACT Surgery}, volume = {52}, journal = {Medical Image Analysis}, number = {2}, publisher = {Elsevier}, doi = {10.1016/j.media.2018.11.008}, pages = {24 -- 41}, year = {2019}, abstract = {Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future.}, language = {en} } @article{HoffmannLemanisWulffetal.2018, author = {Hoffmann, Rene and Lemanis, Robert and Wulff, Lena and Zachow, Stefan and Lukeneder, Alexander and Klug, Christian and Keupp, Helmut}, title = {Traumatic events in the life of the deep-sea cephalopod mollusc, the coleoid Spirula spirula}, volume = {142}, journal = {ScienceDirect: Deep Sea Research Part I - Oceanographic Research}, number = {12}, doi = {10.1016/j.dsr.2018.10.007}, pages = {127 -- 144}, year = {2018}, abstract = {Here, we report on different types of shell pathologies of the enigmatic deep-sea (mesopelagic) cephalopod Spirula spirula. For the first time, we apply non-invasive imaging methods to: document trauma-induced changes in shell shapes, reconstruct the different causes and effects of these pathologies, unravel the etiology, and attempt to quantify the efficiency of the buoyancy apparatus. We have analysed 2D and 3D shell parameters from eleven shells collected as beach findings from the Canary Islands (Gran Canaria and Fuerteventura), West-Australia, and the Maldives. All shells were scanned with a nanotom-m computer tomograph. Seven shells were likely injured by predator attacks: fishes, cephalopods or crustaceans, one specimen was infested by an endoparasite (potentially Digenea) and one shell shows signs of inflammation and one shell shows large fluctuations of chamber volumes without any signs of pathology. These fluctuations are potential indicators of a stressed environment. Pathological shells represent the most deviant morphologies of a single species and can therefore be regarded as morphological end-members. The changes in the shell volume / chamber volume ratio were assessed in order to evaluate the functional tolerance of the buoyancy apparatus showing that these had little effect.}, language = {en} }