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}
翻译:精确的几何表示法对于开发限定元素模型至关重要。虽然一般来说是良好的,但深学习的分离方法,只有很少的数据在精确分割细微特征方面有困难,例如差距和薄结构。随后,分割的地理需要劳动密集型手工修改,以达到可以用于模拟的质素。我们提出了一个战略,将学习转移至再利用数据集,但分解不力,同时采取互动式学习步骤,对数据进行微调,得出适合模拟的解剖准确的分解。我们使用经改进的多平流UNet(Multi Planar UNet),在培训前使用低级的臀部联合分解法,加上专门的损失功能来学习差距区域和后处理,以纠正因旋转变化造成的对称类别微的不准确性。我们展示了这种强而概念简单的方法,在可公开获取的对臀部联合进行计算成像扫描的结果中得到临床验证。代码和由此产生的3D模型见:https://github.com/MICAI20-255/AUSeg}