Deformable Shape Model Research Articles

A method and software for segmentation of anatomic object ensembles by deformable m‐reps

Deformable shape models (DSMs) comprise a general approach that shows great promise for automatic image segmentation. Published studies by others and our own research results strongly suggest that segmentation of a normal or near-normal object from 3D medical images will be most successful when the DSM approach uses (1) knowledge of the geometry of not only the target anatomic object but also the ensemble of objects providing context for the target object and (2) knowledge of the image intensities to be expected relative to the geometry of the target and contextual objects. The segmentation will be most efficient when the deformation operates at multiple object-related scales and uses deformations that include not just local translations but the biologically important transformations of bending and twisting, i.e., local rotation, and local magnification. In computer vision an important class of DSM methods uses explicit geometric models in a Bayesian statistical framework to provide a priori information used in posterior optimization to match the DSM against a target image. In this approach a DSM of the object to be segmented is placed in the target image data and undergoes a series of rigid and nonrigid transformations that deform the model to closely match the target object. The deformation process is driven by optimizing an objective function that has terms for the geometric typicality and model-to-image match for each instance of the deformed model. The success of this approach depends strongly on the object representation, i.e., the structural details and parameter set for the DSM, which in turn determines the analytic form of the objective function. This paper describes a form of DSM called m-reps that has or allows these properties, and a method of segmentation consisting of large to small scale posterior optimization of m-reps. Segmentation by deformable m-reps, together with the appropriate data representations, visualizations, and user interface, has been implemented in software that accomplishes 3D segmentations in a few minutes. Software for building and training models has also been developed. The methods underlying this software and its abilities are the subject of this paper.

Spatial normalization of spine MR images for statistical correlation of lesions with clinical symptoms.

An image analysis method was developed for spatial normalization of spine magnetic resonance images. A deformable shape model of the spine is first constructed, and it is subsequently used by an automated algorithm to find a shape transformation that places patient data into a stereotactic space. Very good agreement with manual segmentations was observed. The main application of this method is in lesion-deficit analysis for determining associations between structural damage and clinical symptoms.

Measuring Size and Shape of the Hippocampus in MR Images Using a Deformable Shape Model

A method for segmentation and quantification of the shape and size of the hippocampus is proposed, based on an automated image analysis algorithm. The algorithm uses a deformable shape model to locate the hippocampus in magnetic resonance images and to determine a geometric representation of its boundary. The deformable model combines three types of information. First, it employs information about the geometric properties of the hippocampal boundary, from a local and relatively finer scale to a more global and relatively coarser scale. Second, the model includes a statistical characterization of normal shape variation across individuals, serving as prior knowledge to the algorithm. Third, the algorithm utilizes a number of manually defined boundary points, which can help guide the model deformation to the appropriate boundaries, wherever these boundaries are weak or not clearly defined in MR images. Excellent agreement is demonstrated between the algorithm and manual segmentations by well-trained raters, with a correlation coefficient equal to 0.97 and algorithm/rater differences statistically equivalent to interrater differences for manual definitions.

Measurement and modeling of boundary shape and surface deformation of the Mongolian gerbil pars flaccida

The shape of the pars flaccida (PF) boundary and its pressure induced deformation was measured in five Mongolian gerbil ears, using an opto-electronic moiré interferometer. To determine the PF boundary, membranes were deformed by middle ear (ME) pressures of −2 kPa and +2 kPa. The boundary of the PF was defined as the locus of points where the pressure induced deformation begins. To a very high precision, this boundary was found to be a circle with the same radius for both over- and underpressure deformations. Between animals the radius varied from 0.760 mm to 0.778 mm. We show that the shape of the PF, while being deformed by static pressure in the ME, can be modeled as part of a sphere. Volume displacement can then be calculated as the volume enclosed by the sphere cap, delimited by the circular PF boundary plane. Volume displacement was calculated for membranes deformed by 400 Pa ME overpressure and −400 Pa ME underpressure. The agreement of the experimental data with the sphere cap model is shown to be very good, and results in a volume displacement measuring accuracy which is better than 10%.