论文标题
使用多平台UNET和转移学习对髋关节进行自动分割
Auto-segmentation of Hip Joints using MultiPlanar UNet with Transfer learning
论文作者
论文摘要
准确的几何表示对于开发有限元模型至关重要。尽管通常只有很少的数据在准确细分精美特征，例如间隙和薄结构方面，虽然只有很少的数据都有良好的深度学习分割方法。随后，分段的几何形状需要劳动密集型手动修改，以达到可以用于模拟目的的质量。我们提出了一种使用转移学习来重复使用分段差的数据集的策略，并结合了交互式学习步骤，其中数据对数据进行微调导致解剖上精确的分割适合模拟。我们使用修改的多平台UNET，该UNET使用下髋关节分段和专用损耗函数进行预训练，以学习间隙区域和后处理，以纠正由于旋转不变性而在对称类别上的微小不准确。我们证明了这种可靠但概念上简单的方法，采用了临床验证的髋关节扫描的临床验证结果。代码和结果3D模型可在以下网址提供：https：//github.com/miccai2022-155/autoseg}
Accurate geometry representation is essential in developing finite element models. Although generally good, deep-learning segmentation approaches with only few data have difficulties in accurately segmenting fine features, e.g., gaps and thin structures. Subsequently, segmented geometries need labor-intensive manual modifications to reach a quality where they can be used for simulation purposes. We propose a strategy that uses transfer learning to reuse datasets with poor segmentation combined with an interactive learning step where fine-tuning of the data results in anatomically accurate segmentations suitable for simulations. We use a modified MultiPlanar UNet that is pre-trained using inferior hip joint segmentation combined with a dedicated loss function to learn the gap regions and post-processing to correct tiny inaccuracies on symmetric classes due to rotational invariance. We demonstrate this robust yet conceptually simple approach applied with clinically validated results on publicly available computed tomography scans of hip joints. Code and resulting 3D models are available at: https://github.com/MICCAI2022-155/AuToSeg}
