Integration and visualization of multimodality brain data for language mapping.

A goal of the University of Washington Brain Project is to develop software tools for processing, integrating and visualizing multimodality language data obtained at the time of neurosurgery, both for surgical planning and for the study of language organization in the brain. Data from a single patient consist of four magnetic resonance-based image volumes, showing anatomy, veins, arteries and functional activation (fMRI). The data also include the location, on the exposed cortical surface, of sites that were electrically stimulated for the presence of language. These five sources are mapped to a common MR-based neuroanatomical model, then visualized to gain a qualitative appreciation of their relationships, prior to quantitative analysis. These procedures are described and illustrated, with emphasis on the visualization of fMRI activation, which may be deep in the brain, with respect to surface-based stimulation sites.

Motivation and organizational principles for anatomical knowledge representation: the digital anatomist symbolic knowledge base.

OBJECTIVE: Conceptualization of the physical objects and spaces that constitute the human body at the macroscopic level of organization, specified as a machine-parseable ontology that, in its human-readable form, is comprehensible to both expert and novice users of anatomical information. DESIGN: Conceived as an anatomical enhancement of the UMLS Semantic Network and Metathesaurus, the anatomical ontology was formulated by specifying defining attributes and differentia for classes and subclasses of physical anatomical entities based on their partitive and spatial relationships. The validity of the classification was assessed by instantiating the ontology for the thorax. Several transitive relationships were used for symbolically modeling aspects of the physical organization of the thorax. RESULTS: By declaring Organ as the macroscopic organizational unit of the body, and defining the entities that constitute organs and higher level entities constituted by organs, all anatomical entities could be assigned to one of three top level classes (Anatomical structure, Anatomical spatial entity and Body substance). The ontology accommodates both the systemic and regional (topographical) views of anatomy, as well as diverse clinical naming conventions of anatomical entities. CONCLUSIONS: The ontology formulated for the thorax is extendible to microscopic and cellular levels, as well as to other body parts, in that its classes subsume essentially all anatomical entities that constitute the body. Explicit definitions of these entities and their relationships provide the first requirement for standards in anatomical concept representation. Conceived from an anatomical viewpoint, the ontology can be generalized and mapped to other biomedical domains and problem solving tasks that require anatomical knowledge.

Incorporating constraint-based shape models into an interactive system for functional brain mapping.

Through intraoperative electrical stimulation mapping, it is possible to identify sites on the surface of the brain that are essential for language function. Interesting correlations have been found between the distribution of these sites and behavioral traits such as verbal IQ. In previous work, tools were developed for building a reconstruction of a patient's cortical surface and using it to recover coordinates of essential language sites. However, considerable expertise was required to produce good reconstructions. This paper describes an improved version of the mapping procedure, in which segmentation is driven by a 3-D shape model. The model-based approach provides more intuitive control over the system, allowing a trained user to complete a surface reconstruction and mapping in about two hours. This level of performance makes it feasible to gather language maps for a large number of patients, which hopefully will lead to significant new findings about language organization in the brain.

Using 3-D shape models to guide segmentation of MR brain images.

Accurate segmentation of medical images poses one of the major challenges in computer vision. Approaches that rely solely on intensity information frequently fail because similar intensity values appear in multiple structures. This paper presents a method for using shape knowledge to guide the segmentation process, applying it to the task of finding the surface of the brain. A 3-D model that includes local shape constraints is fitted to an MR volume dataset. The resulting low-resolution surface is used to mask out regions far from the cortical surface, enabling an isosurface extraction algorithm to isolate a more detailed surface boundary. The surfaces generated by this technique are comparable to those achieved by other methods, without requiring user adjustment of a large number of ad hoc parameters.