Multi-Atlas and Multi-Modal Hippocampus Segmentation for Infant MR Brain Images by Propagating Anatomical Labels on Hypergraph.
Multi-Atlas and Multi-Modal Hippocampus Segmentation for Infant MR Brain Images by Propagating Anatomical Labels on Hypergraph.
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DOI：
10.1007/978-3-319-28194-0_23
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Wu G
中科院分区：
文献类型：
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作者：
Dong P;Guo Y;Shen D;Wu G
从婴儿磁共振图像中准确分割海马在早期脑发育和神经系统疾病的研究中具有重要意义。近年来，基于多图谱贴片的标签融合方法在医学图像解剖结构分割方面取得了巨大成功。然而，婴儿从出生到1岁的巨大外观变化和较差的图像对比度使得现有的标签融合方法在处理婴儿大脑图像时缺乏竞争力。为了缓解这些困难，我们提出了一种新的多图谱和多模态标签融合方法，该方法通过在超图上传播解剖标签来实现对所有体素的一致标记。具体来说，我们不仅考虑目标图像内的所有体素，而且还考虑跨地图集图像的体素作为超图中的顶点。每个超边缘编码一组不同角度的顶点之间的高阶相关性，包括1)多模态特征空间内的特征亲和性，2)目标图像内的空间相干性，以及3)来自多个地图集的种群启发式。此外，我们的标签融合方法进一步允许那些可靠的体素监督其他难以标记的体素的标签估计，基于已建立的超边缘，直到所有目标图像体素达到一致的标记结果。我们评估了我们提出的标签融合方法在从2周大、3个月大、6个月大、9个月大和12个月大的T1和T2加权MR图像中分割海马的效果。我们的分割结果比传统的最先进的标签融合方法提高了标记精度，这对促进早期婴儿大脑研究显示出巨大的潜力。
Accurate segmentation of hippocampus from infant magnetic resonance (MR) images is very important in the study of early brain development and neurological disorder. Recently, multi-atlas patch-based label fusion methods have shown a great success in segmenting anatomical structures from medical images. However, the dramatic appearance change from birth to 1-year-old and the poor image contrast make the existing label fusion methods less competitive to handle infant brain images. To alleviate these difficulties, we propose a novel multi-atlas and multi-modal label fusion method, which can unanimously label for all voxels by propagating the anatomical labels on a hypergraph. Specifically, we consider not only all voxels within the target image but also voxels across the atlas images as the vertexes in the hypergraph. Each hyperedge encodes a high-order correlation, among a set of vertexes, in different perspectives which incorporate 1) feature affinity within the multi-modal feature space, 2) spatial coherence within target image, and 3) population heuristics from multiple atlases. In addition, our label fusion method further allows those reliable voxels to supervise the label estimation on other difficult-to-label voxels, based on the established hyperedges, until all the target image voxels reach the unanimous labeling result. We evaluate our proposed label fusion method in segmenting hippocampus from T1 and T2 weighted MR images acquired from at 2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old. Our segmentation results achieves improvement of labeling accuracy over the conventional state-of-the-art label fusion methods, which shows a great potential to facilitate the early infant brain studies.