Multi-Atlas Image Soft Segmentation via Computation of the Expected Label Value.
Multi-Atlas Image Soft Segmentation via Computation of the Expected Label Value.
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DOI：
10.1109/tmi.2021.3064661
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发表时间：
2021-06
影响因子：
10.6
通讯作者：
Fischl B
中科院分区：
文献类型：
--
作者：
Aganj I;Fischl B
在医学图像分割中，多个图谱的使用是常见的。这通常需要图谱（或平均图谱）与新图像的可变形配准，这在计算上是昂贵的并且容易陷入局部最优。我们建议考虑所有可能的图谱到图像变换的概率，并计算预期的标签值（ELV），从而不仅仅依赖于被配准方法视为“最佳”的变换。此外，我们这样做实际上没有执行变形注册，从而避免了相关的计算成本。我们通过将其应用于磁共振和计算机断层扫描图像数据集上的大脑、肝脏和胰腺分割来评估我们的ELV计算方法。
The use of multiple atlases is common in medical image segmentation. This typically requires deformable registration of the atlases (or the average atlas) to the new image, which is computationally expensive and susceptible to entrapment in local optima. We propose to instead consider the probability of all possible atlas-to-image transformations and compute the expected label value (ELV), thereby not relying merely on the transformation deemed “optimal” by the registration method. Moreover, we do so without actually performing deformable registration, thus avoiding the associated computational costs. We evaluate our ELV computation approach by applying it to brain, liver, and pancreas segmentation on datasets of magnetic resonance and computed tomography images.