论文标题
自适应图像分割的自动编码器策略
An Auto-Encoder Strategy for Adaptive Image Segmentation
论文作者
论文摘要
深神经网络是生物医学图像分割的强大工具。这些模型通常经过备受监督的训练，依靠成对的图像和相应的体素级标签。但是，在大量病例上获得解剖区域的分割可能非常昂贵。因此，非常需要深入的基于学习的细分工具，这些工具不需要重大监督并且可以不断适应。在本文中，我们提出了一种新颖的分割视角，作为一个离散表示学习问题，并提出了一种具有灵活性和适应性的变异自动编码器分割策略。我们的方法称为分割自动编码器（SAE），利用所有可用的未标记扫描，仅需要进行分割，这可以是单个未配对的分割图像。在实验中，我们将SAE应用于大脑MRI扫描。我们的结果表明，SAE可以产生高质量的细分，尤其是在先验良好的情况下。我们证明，马尔可夫随机场的先验可以比空间独立的先验获得明显更好的结果。我们的代码可在https://github.com/evanmy/sae中免费获得。
Deep neural networks are powerful tools for biomedical image segmentation. These models are often trained with heavy supervision, relying on pairs of images and corresponding voxel-level labels. However, obtaining segmentations of anatomical regions on a large number of cases can be prohibitively expensive. Thus there is a strong need for deep learning-based segmentation tools that do not require heavy supervision and can continuously adapt. In this paper, we propose a novel perspective of segmentation as a discrete representation learning problem, and present a variational autoencoder segmentation strategy that is flexible and adaptive. Our method, called Segmentation Auto-Encoder (SAE), leverages all available unlabeled scans and merely requires a segmentation prior, which can be a single unpaired segmentation image. In experiments, we apply SAE to brain MRI scans. Our results show that SAE can produce good quality segmentations, particularly when the prior is good. We demonstrate that a Markov Random Field prior can yield significantly better results than a spatially independent prior. Our code is freely available at https://github.com/evanmy/sae.
