| Summary: | Fetal Alcohol Spectrum Disorder (FASD) is the most common form of preventable mental retardation worldwide. This condition affects children whose mothers excessively consume alcohol whilst pregnant. FASD can be identied by physical and mental defects, such as stunted growth, facial deformities, cognitive impairment, and behavioural abnormalities. Magnetic Resonance Imaging provides a non-invasive means to study the neural correlates of FASD. One such approach aims to detect brain abnormalities through an assessment of volume and shape of sub-cortical structures on high-resolution MR images. Two brain structures of interest are the Caudate Nucleus and Hippocampus. Manual segmentation of these structures is time-consuming and subjective. We therefore present a method for automatically segmenting the Caudate Nucleus and Hippocampus from high-resolution MR images captured as part of an ongoing study into the neural correlates of FASD.
Our method incorporates an Active Shape Model (ASM), which is used to learn shape variation from manually segmented training data. A discrete Geometrically Deformable Model (GDM) is rst deformed to t the relevant structure in each training set. The vertices belonging to each GDM are then used as 3D landmark points - effectively generating point correspondence between training models. An ASM is then created from the landmark points. This ASM is only able to deform to t structures with similar shape to those found in the training data. There are many variations of the standard ASM technique - each suited to the segmentation of data with particular characteristics. Experiments were conducted on the image search phase of ASM segmentation, in order to find the technique best suited to segmentation of the research data. Various popular image search techniques were tested, including an edge detection method and a method based on grey prole Mahalanobis distance measurement.
A heuristic image search method, especially designed to target Caudate Nuclei and Hippocampi, was also developed and tested. This method was extended to include multisampling of voxel proles. ASM segmentation quality was evaluated according to various quantitative metrics, including: overlap, false positives, false negatives, mean squared distance and Hausdorff distance.
Results show that ASMs that use the heuristic image search technique, without multisampling, produce the most accurate segmentations. Mean overlap for segmentation of the various target structures ranged from 0.76 to 0.82. Mean squared distance ranged from 0.72 to 0.76 - indicating sub-1mm accuracy, on average. Mean Hausdorff distance ranged from 2:7mm to 3:1mm.
An ASM constructed using our heuristic technique will enable researchers to quickly, reliably, and automatically segment test data for use in the FASD study - thereby facilitating a better understanding of the eects of this unfortunate condition.
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