@inproceedings{KainmuellerLameckerZachowetal.2009, author = {Kainm{\"u}ller, Dagmar and Lamecker, Hans and Zachow, Stefan and Hege, Hans-Christian}, title = {An Articulated Statistical Shape Model for Accurate Hip Joint Segmentation}, series = {EBMC 2009. Int. Conf. of the IEEE Eng. in Med. and Biol. Society (EMBC)}, booktitle = {EBMC 2009. Int. Conf. of the IEEE Eng. in Med. and Biol. Society (EMBC)}, address = {Minneapolis, USA}, pages = {6345 -- 6351}, year = {2009}, language = {en} } @article{HepptHildebrandtSteinmannetal.2007, author = {Heppt, Werner and Hildebrandt, Thomas and Steinmann, Alexander and Zachow, Stefan}, title = {Aesthetic and Function in Rhinoplasty}, series = {Springer Journal}, volume = {264 (Suppl 1), RL 126}, journal = {Springer Journal}, pages = {307}, year = {2007}, language = {en} } @inproceedings{ZachowErdmannHegeetal.2004, author = {Zachow, Stefan and Erdmann, Bodo and Hege, Hans-Christian and Deuflhard, Peter}, title = {Advances in 3D osteotomy planning with 3D soft tissue prediction}, series = {Proc. 2nd International Symposium on Computer Aided Surgery around the Head, Abstract}, booktitle = {Proc. 2nd International Symposium on Computer Aided Surgery around the Head, Abstract}, address = {Bern}, pages = {31}, year = {2004}, language = {en} } @article{SahuMukhopadhyaySzengeletal., author = {Sahu, Manish and Mukhopadhyay, Anirban and Szengel, Angelika and Zachow, Stefan}, title = {Addressing multi-label imbalance problem of Surgical Tool Detection using CNN}, series = {International Journal of Computer Assisted Radiology and Surgery}, volume = {12}, journal = {International Journal of Computer Assisted Radiology and Surgery}, number = {6}, publisher = {Springer}, doi = {10.1007/s11548-017-1565-x}, pages = {1013 -- 1020}, abstract = {Purpose: A fully automated surgical tool detection framework is proposed for endoscopic video streams. State-of-the-art surgical tool detection methods rely on supervised one-vs-all or multi-class classification techniques, completely ignoring the co-occurrence relationship of the tools and the associated class imbalance. Methods: In this paper, we formulate tool detection as a multi-label classification task where tool co-occurrences are treated as separate classes. In addition, imbalance on tool co-occurrences is analyzed and stratification techniques are employed to address the imbalance during Convolutional Neural Network (CNN) training. Moreover, temporal smoothing is introduced as an online post-processing step to enhance run time prediction. Results: Quantitative analysis is performed on the M2CAI16 tool detection dataset to highlight the importance of stratification, temporal smoothing and the overall framework for tool detection. Conclusion: The analysis on tool imbalance, backed by the empirical results indicates the need and superiority of the proposed framework over state-of-the-art techniques.}, language = {en} } @inproceedings{ZilskeLameckerZachow2008, author = {Zilske, Michael and Lamecker, Hans and Zachow, Stefan}, title = {Adaptive Remeshing of Non-Manifold Surfaces}, series = {Eurographics 2008 Annex to the Conf. Proc.}, booktitle = {Eurographics 2008 Annex to the Conf. Proc.}, pages = {207 -- 211}, year = {2008}, language = {en} } @misc{ZilskeLameckerZachow, author = {Zilske, Michael and Lamecker, Hans and Zachow, Stefan}, title = {Adaptive Remeshing of Non-Manifold Surfaces}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-9445}, number = {07-01}, abstract = {We present a unified approach for consistent remeshing of arbitrary non-manifold triangle meshes with additional user-defined feature lines, which together form a feature skeleton. Our method is based on local operations only and produces meshes of high regularity and triangle quality while preserving the geometry as well as topology of the feature skeleton and the input mesh.}, language = {en} } @inproceedings{GladilinZachowDeuflhardetal.2002, author = {Gladilin, Evgeny and Zachow, Stefan and Deuflhard, Peter and Hege, Hans-Christian}, title = {Adaptive Nonlinear Elastic FEM for Realistic Prediction of Soft Tissue in Craniofacial Surgery Simulations}, series = {Proc. SPIE Medical Imaging 2002}, volume = {4681}, booktitle = {Proc. SPIE Medical Imaging 2002}, editor = {K. Mun, Seong}, address = {San Diego, USA}, doi = {10.1117/12.466906}, pages = {1 -- 8}, year = {2002}, language = {en} } @article{MoldenhauerWeiserZachow, author = {Moldenhauer, Marian and Weiser, Martin and Zachow, Stefan}, title = {Adaptive Algorithms for Optimal Hip Implant Positioning}, series = {PAMM}, volume = {17}, journal = {PAMM}, number = {1}, doi = {10.1002/pamm.201710071}, pages = {203 -- 204}, abstract = {In an aging society where the number of joint replacements rises, it is important to also increase the longevity of implants. In particular hip implants have a lifetime of at most 15 years. This derives primarily from pain due to implant migration, wear, inflammation, and dislocation, which is affected by the positioning of the implant during the surgery. Current joint replacement practice uses 2D software tools and relies on the experience of surgeons. Especially the 2D tools fail to take the patients' natural range of motion as well as stress distribution in the 3D joint induced by different daily motions into account. Optimizing the hip joint implant position for all possible parametrized motions under the constraint of a contact problem is prohibitively expensive as there are too many motions and every position change demands a recalculation of the contact problem. For the reduction of the computational effort, we use adaptive refinement on the parameter domain coupled with the interpolation method of Kriging. A coarse initial grid is to be locally refined using goal-oriented error estimation, reducing locally high variances. This approach will be combined with multi-grid optimization such that numerical errors are reduced.}, language = {en} } @inproceedings{TackZachow, author = {Tack, Alexander and Zachow, Stefan}, title = {Accurate Automated Volumetry of Cartilage of the Knee using Convolutional Neural Networks: Data from the Osteoarthritis Initiative}, series = {IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}, booktitle = {IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}, doi = {10.1109/ISBI.2019.8759201}, pages = {40 -- 43}, abstract = {Volumetry of cartilage of the knee is needed for knee osteoarthritis (KOA) assessment. It is typically performed manually in a tedious and subjective process. We developed a method for an automated, segmentation-based quantification of cartilage volume by employing 3D Convolutional Neural Networks (CNNs). CNNs were trained in a supervised manner using magnetic resonance imaging data and cartilage volumetry readings performed by clinical experts for 1378 subjects provided by the Osteoarthritis Initiative. It was shown that 3D CNNs are able to achieve volume measures comparable to the magnitude of variation between expert readings and the real in vivo situation. In the future, accurate automated cartilage volumetry might support both, diagnosis of KOA as well as longitudinal analysis of KOA progression.}, language = {en} } @misc{TackZachow, author = {Tack, Alexander and Zachow, Stefan}, title = {Accurate Automated Volumetry of Cartilage of the Knee using Convolutional Neural Networks: Data from the Osteoarthritis Initiative}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-71439}, abstract = {Volumetry of the cartilage of the knee, as needed for the assessment of knee osteoarthritis (KOA), is typically performed in a tedious and subjective process. We present an automated segmentation-based method for the quantification of cartilage volume by employing 3D Convolutional Neural Networks (CNNs). CNNs were trained in a supervised manner using magnetic resonance imaging data as well as cartilage volumetry readings given by clinical experts for 1378 subjects. It was shown that 3D CNNs can be employed for cartilage volumetry with an accuracy similar to expert volumetry readings. In future, accurate automated cartilage volumetry might support both, diagnosis of KOA as well as assessment of KOA progression via longitudinal analysis.}, language = {en} }