论文标题
通过环状分割gan对未标记模式进行实例分割
Instance Segmentation of Unlabeled Modalities via Cyclic Segmentation GAN
论文作者
论文摘要
未标记成像模式的实例细分是一项艰巨的任务，但基本的任务是昂贵且耗时。现有作品通过部署针对各种培训数据优化的预训练模型，或将域翻译和图像分割作为两个独立步骤进行优化的预培训模型。在这项工作中，我们提出了一种新型的环状分割生成对抗网络（CYSGAN），该网络（Cysgan）使用统一的框架共同进行图像翻译和实例分割。除了带有带注释的源域的图像翻译和监督损失的自行车损失外，我们还介绍了其他基于自我保护和分割的对抗性目标，以通过利用未标记的目标域图像来改善模型性能。我们使用带注释的电子显微镜（EM）图像和未标记的扩展显微镜（EXM）数据基准了我们对3D神经元核分割任务的方法。我们的Cysgan均优于预计的通才模型和依次进行图像翻译和分割的基线。我们的实施以及新收集的，致密的EXM Nuclei数据集（名为Nucexm）可在https://connectomics-bazaar.github.io/proj/proj/cysgan/index.html上获得。
Instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segment a new modality by either deploying a pre-trained model optimized on diverse training data or conducting domain translation and image segmentation as two independent steps. In this work, we propose a novel Cyclic Segmentation Generative Adversarial Network (CySGAN) that conducts image translation and instance segmentation jointly using a unified framework. Besides the CycleGAN losses for image translation and supervised losses for the annotated source domain, we introduce additional self-supervised and segmentation-based adversarial objectives to improve the model performance by leveraging unlabeled target domain images. We benchmark our approach on the task of 3D neuronal nuclei segmentation with annotated electron microscopy (EM) images and unlabeled expansion microscopy (ExM) data. Our CySGAN outperforms both pretrained generalist models and the baselines that sequentially conduct image translation and segmentation. Our implementation and the newly collected, densely annotated ExM nuclei dataset, named NucExM, are available at https://connectomics-bazaar.github.io/proj/CySGAN/index.html.
