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.
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.
Authors: Amir Alansary; Ozan Oktay; Yuanwei Li; Loic Le Folgoc; Benjamin Hou; Ghislain Vaillant; Konstantinos Kamnitsas; Athanasios Vlontzos; Ben Glocker; Bernhard Kainz; Daniel Rueckert Journal: Med Image Anal Date: 2019-02-14 Impact factor: 8.545
Authors: Christine A Edwards; Abhinav Goyal; Aaron E Rusheen; Abbas Z Kouzani; Kendall H Lee Journal: Front Neurosci Date: 2021-06-17 Impact factor: 4.677