Currently there are no routine methods to delineate the primary auditory cortex (PAC) of humans in vivo. Due to the large differences in the location of the PAC between subjects, labels derived from post-mortem brains may be inaccurate when applied to different samples of in-vivo brains. Recent magnetic resonance (MR) imaging studies suggested that MR-tissue properties can be used to define the location of the PAC region in vivo. The basis for such an approach is that the PAC region is more strongly myelinated than secondary areas. We developed a fully automatic method to identify the PAC in conventional anatomical data using a combination of two complementary MR contrasts, i.e. T1 and T2, at 3 Tesla with 0.7 mm isotropic resolution. Our algorithm maps the anatomical MR data to reconstructed cortical surfaces and uses a classification approach to create an artificial contrast that is highly sensitive to the effects of an increased myelination of the cortex. Consistent with the location of the PAC defined in post-mortem brains, we found a compact region on the medial two thirds of Heschl’s gyrus in both hemispheres of all 39 subjects. With further improvements in signal-to-noise ratio of the anatomical data and manual correction of segmentation errors, the results suggest that the primary auditory cortex can be defined in the living brain of single subjects.

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Abstract. Image segmentation based on pairwise pixel similarities has been a very active field of research in recent years. The drawbacks common to these segmentation methods are the enormous space and processor requirements. The contribution of this paper is a general purpose two-stage preprocessing method that substantially reduces the involved costs. Initially, an oversegmentation into small coherent image patchesor superpixels -is obtained through an iterative process guided by pixel similarities. A suitable pairwise superpixel similarity measure is then defined which may be plugged into an arbitrary segmentation method based on pairwise pixel similarities. To illustrate our ideas we integrated the algorithm into a spectral graph-partitioning method using the Normalized Cut criterion. Our experiments show that the time and memory requirements are reduced drastically (> 99%), while segmentations of adequate quality are obtained.

BackgroundObject detection in 3-D medical images is often necessary for constraining a segmentation or registration task. It may be a task in its own right as well, when instances of a structure, e.g. the lymph nodes, are searched. Problems from occlusion, illumination and projection do not arise, making the problem simpler than object detection in photographies. However, objects of interest are often not well contrasted against the background. Influence from noise and other artifacts is much stronger and shape and appearance may vary substantially within a class.MethodsDeformable models capture the characteristic shape of an anatomic object and use constrained deformation for hypothesing object boundaries in image regions of low or non-existing contrast. Learning these constraints requires a large sample data base. We show that training may be replaced by readily available user knowledge defining a prototypical deformable part model. If structures have a strong part-relationship, or if they may be found based on spatially related guiding structures, or if the deformation is rather restricted, the supporting data information suffices for solving the detection task. We use a finite element model to represent anatomic variation by elastic deformation. Complex shape variation may be represented by a hierarchical model with simpler part variation. The hierarchy may be represented explicitly as a hierarchy of sub-shapes, or implicitly by a single integrated model. Data support and model deformation of the complete model can be represented by an energy term, serving as quality-of-fit function for object detection.ResultsThe model was applied to detection and segmentation tasks in various medical applications in 2- and 3-D scenes. It has been shown that model fitting and object detection can be carried out efficiently by a combination of a local and global search strategy using models that are parameterized for the different tasks.ConclusionsA part-based elastic model represents complex within-class object variation without training. The hierarchy of parts may specify relationship to neighboring anatomical objects in object detection or a part-decomposition of a complex anatomic structure. The intuitive way to incorporate domain knowledge has a high potential to serve as easily adaptable method to a wide range of different detection tasks in medical image analysis.