Rapid and Automatic Localization of the Anterior and Posterior Commissure Point Landmarks in MR Volumetric Neuroimages1
Rationale and Objective
Accurate identification of the anterior commissure (AC) and posterior commissure (PC) is critical in neuroradiology, functional neurosurgery, human brain mapping, and neuroscience research. Moreover, major stereotactic brain atlases are based on the AC and PC. Our goal is to provide an algorithm for a rapid, robust, accurate and automatic identification of AC and PC.
Materials and Method
The method exploits anatomical and radiological properties of AC, PC and surrounding structures, including morphological variability. The localization is done in two stages: coarse and fine. The coarse stage locates the AC and PC on the midsagittal plane by analyzing their relationships with the corpus callosum, fornix, and brainstem. The fine stage refines the AC and PC in a well-defined volume of interest, analyzing locations of lateral and third ventricles, interhemispheric fissure, and massa intermedia.
Results
The algorithm was developed using simple operations, like histogramming, thresholding, region growing, 1D projections. It was tested on 94 diversified T1W and SPGR datasets. After the fine stage, 71 (76%) volumes had an error between 0–1 mm for the AC and 55 (59%) for the PC. The mean errors were 1.0 mm (AC) and 1.0 mm (PC). The accuracy has improved twice due to fine stage processing. The algorithm took about 1 second for coarse and 4 seconds for fine processing on P4, 2.5 GHz.
Conclusion
The use of anatomical and radiological knowledge including variability in algorithm formulation aids in localization of structures more accurately and robustly. This fully automatic algorithm is potentially useful in clinical setting and for research.
Key Words: Anterior commissure , posterior commissure , midsagittal plane , corpus callosum , brainstem , fornix , localization , brain atlas
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Supported by Biomedical Research Council, Agency for Science, Technology and Research, Singapore. We are grateful to our colleagues Ihar Volkau for valuable discussions and Guoyu Qian for the implementation of the algorithm in VC++. The authors thank all the hospitals, Brainweb and Internet Brain Segmentation Repository for the datasets used in the study.
PII: S1076-6332(05)00639-2
doi:10.1016/j.acra.2005.08.023
© 2006 AUR. Published by Elsevier Inc. All rights reserved.
