Medical Image Segmentation is a useful application for medical image analysis including detecting diseases and abnormalities in imaging modalities such as MRI, CT etc. Deep learning has proven to be promising for this task but usually has a low accuracy because of the lack of appropriate publicly available annotated or segmented medical datasets. In addition, the datasets that are available may have a different texture because of different dosage values or scanner properties than the images that need to be segmented. This paper presents a StyleGAN-driven approach for segmenting publicly available large medical datasets by using readily available extremely small annotated datasets in similar modalities. The approach involves augmenting the small segmented dataset and eliminating texture differences between the two datasets. The dataset is augmented by being passed through six different StyleGANs that are trained on six different style images taken from the large non-annotated dataset we want to segment. Specifically, style transfer is used to augment the training dataset. The annotations of the training dataset are hence combined with the textures of the non-annotated dataset to generate new anatomically sound images. The augmented dataset is then used to train a U-Net segmentation network which displays a significant improvement in the segmentation accuracy in segmenting the large non-annotated dataset.

翻译：医学图像分割是医学图像分析中的一项重要应用，用于检测MRI、CT等成像模态下的疾病与异常。深度学习已被证明在该任务中具有潜力，但由于缺乏高质量且公开可用的带标注或分割的医学数据集，其准确性通常较低。此外，现有数据集与待分割图像可能因辐射剂量差异或扫描仪属性不同而具有不同的纹理特征。本文提出一种基于StyleGAN的方法，利用易获取的极小规模同类模态带标注数据集，对大型公开医学数据集进行分割。该方法通过扩充小规模分割数据集并消除两数据集间的纹理差异来实现。具体而言，将小规模数据集输入至六个不同的StyleGAN中，这些生成器分别基于从待分割的大型未标注数据集中选取的六种不同风格图像训练而成。通过风格迁移技术对训练数据集进行扩充，将训练数据集的标注信息与未标注数据集的纹理特征相结合，生成具有解剖学可靠性的新图像。最终，使用扩充后的数据集训练U-Net分割网络，实验表明该方法在分割大型未标注数据集时显著提升了分割精度。