3D Capsule Networks for Brain Image Segmentation
3D Capsule Networks for Brain Image Segmentation
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DOI：
10.3174/ajnr.a7845
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发表时间：
2023-04
影响因子：
3.5
通讯作者：
A. Avesta;Y. Hui;M. Aboian;J. Duncan;H. Krumholz;S. Aneja
中科院分区：
文献类型：
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作者：
A. Avesta;Y. Hui;M. Aboian;J. Duncan;H. Krumholz;S. Aneja
背景和目的：当前的自动分割模型（如UNets和nnUNets）具有局限性，包括无法分割在训练期间未表示的图像以及缺乏计算效率。3D胶囊网络有可能解决这些限制。材料和方法：我们使用了在多机构研究中获得的3430个脑MRI来训练和验证我们的模型。我们将我们的胶囊网络与标准替代方案UNets和nnUNets进行了比较，基于分割效率（Dice分数），当图像在训练数据中没有很好地表示时的分割性能，训练数据有限时的性能，以及计算效率，包括所需的内存和计算速度。研究结果：胶囊网络分割第三脑室、丘脑和海马，Dice评分分别为95%、94%和92%，在UNets和nnUNets Dice评分的1%以内。胶囊网络在分割训练数据中没有很好表现的图像方面明显优于UNets，Dice得分高出30%。胶囊网络所需的计算内存不到UNets或nnUNets所需内存的十分之一。与UNet和nnUNet相比，胶囊网络的训练速度也快了25%以上。结论：我们开发并验证了一种胶囊网络，该网络在分割大脑图像方面是有效的，可以分割在训练数据中没有很好表现的图像，并且与替代方案相比计算效率更高。
BACKGROUND AND PURPOSE: Current autosegmentation models such as UNets and nnUNets have limitations, including the inability to segment images that are not represented during training and lack of computational efficiency. 3D capsule networks have the potential to address these limitations. MATERIALS AND METHODS: We used 3430 brain MRIs, acquired in a multi-institutional study, to train and validate our models. We compared our capsule network with standard alternatives, UNets and nnUNets, on the basis of segmentation efficacy (Dice scores), segmentation performance when the image is not well-represented in the training data, performance when the training data are limited, and computational efficiency including required memory and computational speed. RESULTS: The capsule network segmented the third ventricle, thalamus, and hippocampus with Dice scores of 95%, 94%, and 92%, respectively, which were within 1% of the Dice scores of UNets and nnUNets. The capsule network significantly outperformed UNets in segmenting images that were not well-represented in the training data, with Dice scores 30% higher. The computational memory required for the capsule network is less than one-tenth of the memory required for UNets or nnUNets. The capsule network is also >25% faster to train compared with UNet and nnUNet. CONCLUSIONS: We developed and validated a capsule network that is effective in segmenting brain images, can segment images that are not well-represented in the training data, and is computationally efficient compared with alternatives.