H
Hans Lamecker
Researcher at Zuse Institute Berlin
Publications - 95
Citations - 3314
Hans Lamecker is an academic researcher from Zuse Institute Berlin. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 23, co-authored 94 publications receiving 2852 citations. Previous affiliations of Hans Lamecker include Charité & German Cancer Research Center.
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Journal ArticleDOI
Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets
Tobias Heimann,B. van Ginneken,Martin Styner,Yulia Arzhaeva,V. Aurich,C. Bauer,A. Beck,C. Becker,Reinhard Beichel,G. Bekes,Fernando Bello,G. Binnig,Horst Bischof,Alexander Bornik,P. Cashman,Ying Chi,A. Cordova,Benoit M. Dawant,Marta Fidrich,Jacob D. Furst,D. Furukawa,Lars Grenacher,Joachim Hornegger,D. Kainmuller,Richard I. Kitney,H. Kobatake,Hans Lamecker,T. Lange,Jeongjin Lee,B. Lennon,Rui Li,Senhu Li,Hans-Peter Meinzer,Gábor Németh,Daniela Raicu,A.-M. Rau,E.M. van Rikxoort,Mikael Rousson,L. Rusko,K.A. Saddi,G. Schmidt,D. Seghers,Akinobu Shimizu,Pieter Slagmolen,Erich Sorantin,G. Soza,R. Susomboon,Jonathan M. Waite,A. Wimmer,Ivo Wolf +49 more
TL;DR: A comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.
Proceedings Article
Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model
TL;DR: A fully automatic 3D segmentation method for the liver from contrast-enhanced CT data is presented, based on a combination of a constrained free-form and statistical deformable model, considering the potential presence of tumors in the liver.
Journal ArticleDOI
3D ultrasound-CT registration of the liver using combined landmark-intensity information
Thomas Lange,Nils Papenberg,Stefan Heldmann,Jan Modersitzki,Bernd Fischer,Hans Lamecker,P.M. Schlag +6 more
TL;DR: A method of combining anatomical landmark information with a fast non-parametric intensity registration approach that improves the mean and percentage of point distances above 3 mm compared to rigid and thin-plate spline registration based only on landmarks.
Segmentation of the Liver using a 3D Statistical Shape Model
TL;DR: An automatic approach for segmentation of the liver from computer tomography (CT) images based on a 3D statistical shape model based on minimizing the distortion of the correspondence mapping between two different surfaces is presented.
Proceedings ArticleDOI
A 3D statistical shape model of the pelvic bone for segmentation
TL;DR: An interactive approach is proposed for solving the correspondence problem which is able to handle shapes of arbitrary topology, suitable for the genus 3 surface of the pelvic bone, and allows to specify corresponding anatomical features as boundary constraints to the matching process.