Multi-atlas based segmentation methods have recently attracted much attention in medical image segmentation. The
multi-atlas based segmentation methods typically consist of three steps, including image registration, label propagation,
and label fusion. Most of the recent studies devote to improving the label fusion step and adopt a typical image
registration method for registering atlases to the target image. However, the existing registration methods may become
unstable when poor image quality or high anatomical variance between registered image pairs involved. In this paper, we
propose an iterative image segmentation and registration procedure to simultaneously improve the registration and
segmentation performance in the multi-atlas based segmentation framework. Particularly, a two-channel registration
method is adopted with one channel driven by appearance similarity between the atlas image and the target image and
the other channel optimized by similarity between atlas label and the segmentation of the target image. The image
segmentation is performed by fusing labels of multiple atlases. The validation of our method on hippocampus
segmentation of 30 subjects containing MR images with both 1.5T and 3.0T field strength has demonstrated that our
method can significantly improve the segmentation performance with different fusion strategies and obtain segmentation
results with Dice overlap of 0.892±0.024 for 1.5T images and 0.902±0.022 for 3.0T images to manual segmentations.
For subcortical structure segmentation, multi-atlas based segmentation methods have attracted great interest due to their
competitive performance. Under this framework, using deformation fields generated for registering atlas images to the
target image, labels of the atlases are first propagated to the target image space and further fused somehow to get the
target segmentation. Many label fusion strategies have been proposed and most of them adopt predefined weighting
models which are not necessarily optimal. In this paper, we propose a local label learning (L3) strategy to estimate the
target image's label using statistical machine learning techniques. Specifically, we use Support Vector Machine (SVM)
to learn a classifier for each of the target image voxels using its neighboring voxels in the atlases as a training dataset.
Each training sample has dozens of image features extracted around its neighborhood and these features are optimally
combined by the SVM learning method to classify the target voxel. The key contribution of this method is the
development of a locally specific classifier for each target voxel based on informative texture features. The validation
experiment on 57 MR images has demonstrated that our method generates segmentation results of hippocampal with a
dice overlap of 0.908±0.023 to manual segmentations, statistically significantly better than state-of-the-art segmentation
algorithms.
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