Main Menu (Mobile)- Block

Main Menu - Block

janelia7_blocks-janelia7_fake_breadcrumb | block
Koyama Lab / Publications
custom | custom


facetapi-Q2b17qCsTdECvJIqZJgYMaGsr8vANl1n | block

Associated Lab

facetapi-PV5lg7xuz68EAY8eakJzrcmwtdGEnxR0 | block
facetapi-021SKYQnqXW6ODq5W5dPAFEDBaEJubhN | block
general_search_page-panel_pane_1 | views_panes

19 Publications

Showing 11-19 of 19 results
Your Criteria:
    Kainmueller Lab
    09/24/10 | Model-based auto-segmentation of knee bones and cartilage in MRI data.
    Seim H, Kainmueller D, Lamecker H, Bindernagel M, Malinowski J, Zachow S
    Medical Image Analysis for the Clinic - A Grand Challenge, MICCAI 2010, the 13th International Conference on Medical Image Computing and Computer Assisted Intervention. 2010 Sep 24:

    We present a method for fully automatic segmentation of the bones and cartilages of the human knee from MRI data. Based on statistical shape models and graph-based optimization, first the femoral and tibial bone surfaces are reconstructed. Starting from the bone sur- faces the cartilages are segmented simultaneously with a multi object technique using prior knowledge on the variation of cartilage thickness. We validate our method on 40 clinical MRI datasets acquired before knee replacement. 

    View Publication Page
    Kainmueller Lab
    09/01/09 | An articulated statistical shape model for accurate hip joint segmentation.
    Kainmueller D, Lamecker H, Zachow S, Hege H
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference. 2009;2009:6345-51. doi: 10.1109/IEMBS.2009.5333269

    In this paper we propose a framework for fully automatic, robust and accurate segmentation of the human pelvis and proximal femur in CT data. We propose a composite statistical shape model of femur and pelvis with a flexible hip joint, for which we extend the common definition of statistical shape models as well as the common strategy for their adaptation. We do not analyze the joint flexibility statistically, but model it explicitly by rotational parameters describing the bent in a ball-and-socket joint. A leave-one-out evaluation on 50 CT volumes shows that image driven adaptation of our composite shape model robustly produces accurate segmentations of both proximal femur and pelvis. As a second contribution, we evaluate a fine grain multi-object segmentation method based on graph optimization. It relies on accurate initializations of femur and pelvis, which our composite shape model can generate. Simultaneous optimization of both femur and pelvis yields more accurate results than separate optimizations of each structure. Shape model adaptation and graph based optimization are embedded in a fully automatic framework.

    View Publication Page
    Kainmueller Lab
    08/28/09 | Comparison and evaluation of methods for liver segmentation from CT datasets.
    Heimann T, van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G, Bello F, Binnig G, Bischof H, Bornik A, Cashman PM, Chi Y, Cordova A, Dawant BM, Fidrich M, Furst JD, Furukawa D, Grenacher L, Hornegger J, Kainmüller D, Kitney RI, Kobatake H, Lamecker H, Lange T, Lee J, Lennon B, Li R, Li S, Meinzer H, Nemeth G, Raicu DS, Rau A, van Rikxoort EM, Rousson M, Rusko L, Saddi KA, Schmidt G, Seghers D, Shimizu A, Slagmolen P, Sorantin E, Soza G, Susomboon R, Waite JM, Wimmer A, Wolf I
    IEEE transactions on medical imaging. 2009 Aug;28(8):1251-65. doi: 10.1109/TMI.2009.2013851

    This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

    View Publication Page
    Kainmueller Lab
    08/07/09 | Automatic Extraction of Anatomical Landmarks From Medical Image Data: An Evaluation of Different Methods
    Kainmueller D, Hans-Christian Hege , Heiko Seim , Markus Heller , Stefan Zachow

    This work presents three different methods for automatic detection of anatomical landmarks in CT data, namely for the left and right anterior superior iliac spines and the pubic symphysis. The methods exhibit different degrees of generality in terms of portability to other anatomical landmarks and require a different amount of training data. The ſrst method is problem-speciſc and is based on the convex hull of the pelvis. Method two is a more generic approach based on a statistical shape model including the landmarks of interest for every training shape. With our third method we present the most generic approach, where only a small set of training landmarks is required. Those landmarks are transferred to the patient speciſc geometry based on Mean Value Coordinates (MVCs). The methods work on surfaces of the pelvis that need to be extracted beforehand. We perform this geometry reconstruction with our previously introduced fully automatic segmentation framework for the pelvic bones. With a focus on the accuracy of our novel MVC-based approach, we evaluate and compare our methods on 100 clinical CT datasets, for which gold standard landmarks were deſned manually by multiple observers.

    View Publication Page
    Kainmueller Lab
    07/26/09 | 3D reconstruction of the human rib cage from 2D projection images using a statistical shape model.
    Dworzak J, Lamecker H, von Berg J, Klinder T, Lorenz C, Kainmüller D, Seim H, Hege H, Zachow S
    International journal of computer assisted radiology and surgery. 2010 Mar;5(2):111-24. doi: 10.1007/s11548-009-0390-2

    PURPOSE: This paper describes an approach for the three-dimensional (3D) shape and pose reconstruction of the human rib cage from few segmented two-dimensional (2D) projection images. Our work is aimed at supporting temporal subtraction techniques of subsequently acquired radiographs by establishing a method for the assessment of pose differences in sequences of chest radiographs of the same patient.

    METHODS: The reconstruction method is based on a 3D statistical shape model (SSM) of the rib cage, which is adapted to binary 2D projection images of an individual rib cage. To drive the adaptation we minimize a distance measure that quantifies the dissimilarities between 2D projections of the 3D SSM and the projection images of the individual rib cage. We propose different silhouette-based distance measures and evaluate their suitability for the adaptation of the SSM to the projection images.

    RESULTS: An evaluation was performed on 29 sets of biplanar binary images (posterior-anterior and lateral). Depending on the chosen distance measure, our experiments on the combined reconstruction of shape and pose of the rib cages yield reconstruction errors from 2.2 to 4.7 mm average mean 3D surface distance. Given a geometry of an individual rib cage, the rotational errors for the pose reconstruction range from 0.1 degrees to 0.9 degrees.

    CONCLUSIONS: The results show that our method is suitable for the estimation of pose differences of the human rib cage in binary projection images. Thus, it is able to provide crucial 3D information for registration during the generation of 2D subtraction images.

    View Publication Page
    Kainmueller Lab
    07/20/09 | Multi-object segmentation of head bones.
    Kainmueller D, Lamecker H, Seim H, Zachow S
    MIDAS Journal. 2009 Jul 20:

    We present a fully automatic method for 3D segmentation of the mandibular bone from CT data. The method includes an adaptation of statistical shape models of the mandible, the skull base and the midfacial bones, followed by a simultaneous graph-based optimization of adjacent deformable models. The adaptation of the models to the image data is performed according to a heuristic model of the typical intensity distribution in the vincinity of the bone boundary, with special focus on an accurate discrimination of adjacent bones in joint regions. An evaluation of our method based on 18 CT scans shows that a manual correction of the automatic segmentations is not necessary in approx. 60% of the axial slices that contain the mandible.

    View Publication Page
    Kainmueller Lab
    01/01/09 | Multi-object segmentation with coupled deformable models.
    Kainmueller D, Lamecker H, Zachow S
    Annals of the British Machine Vision Association. 2009;2009(5):1-10

    For biomechanical simulations, the segmentation of multiple adjacent anatomical struc- tures from medical image data is often required. If adjacent structures are barely dis- tinguishable in image data, in general automatic segmentation methods for single struc- tures do not yield sufficiently accurate results. To improve segmentation accuracy in these cases, knowledge about adjacent structures must be exploited. Optimal graph searching (graph cuts) based on deformable surface models allows for a simultaneous segmentation of multiple adjacent objects. However, this method requires a correspon- dence relation between vertices of adjacent surface meshes. Line segments, each con- taining two corresponding vertices, may then serve as shared displacement directions in the segmentation process. In this paper we propose a scheme for constructing a corre- spondence relation in adjacent regions of two arbitrary surfaces. This correspondence relation implies shared displacement directions that we apply for segmentation with de- formable surfaces. Here, overlap of the surfaces is guaranteed not to occur. We show correspondence relations for regions on a femoral head and acetabulum and other adja- cent structures, as well as an evaluation of segmentation results on 50 ct images of the hip joint. 

    View Publication Page
    Kainmueller Lab
    09/07/08 | Model-based autosegmentation of the central brain of the honeybee, Apis mellifera, using active statistical shape models.
    Singer J, Lienhard M, Seim H, Kainmueller D, Kuss A, Lamecker H, Zachow S, Menzel R, Rybak J
    Neuroinformatics 2008. 2008 Sep 07:. doi: 10.3389/conf.neuro.11.2008.01.064

    The Honeybee Brain Atlas serves as 3D database and communicative platform to accumulate structural data, i.e. reconstructed neurons, derived from confocal scans (Brandt et al., 2005) ( (1). Transforming neurons into the atlas requires manual segmentation of neuropils within confocal images, a time-consuming task requiring expertise in identifying biological structures which can result in different outcomes from various segmenters.

    View Publication Page
    Kainmueller Lab
    10/29/07 | Shape constrained automatic segmentation of the liver based on a heuristic intensity model.
    Kainmueller D, Lange T, Lamecker H
    MICCAI Workshop 3D Segmentation in the Clinic. 2007 Oct 29:

    We present a fully automatic 3D segmentation method for the liver from contrast-enhanced CT data. It is based on a combination of a constrained free-form and statistical deformable model. The adap- tation of the model to the image data is performed according to a simple model of the typical intensity distribution around the liver boundary and neighboring anatomical structures, considering the potential presence of tumors in the liver. All parameters of the deformation as well as the initial positioning of the model in the data are estimated automatically. 

    View Publication Page