IEEE Trans Med Imaging. 2021 Sep;40(9):2403-2414. doi: 10.1109/TMI.2021.3077285. Epub 2021 Aug 31.
Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells react to drug treatment. In this paper, we propose a novel box-based instance segmentation method. Box-based instance segmentation methods capture objects via bounding boxes and then perform individual segmentation within each bounding box region. However, existing methods can hardly differentiate the target from its neighboring objects within the same bounding box region due to their similar textures and low-contrast boundaries. To deal with this problem, in this paper, we propose an object-guided instance segmentation method. Our method first detects the center points of the objects, from which the bounding box parameters are then predicted. To perform segmentation, an object-guided coarse-to-fine segmentation branch is built along with the detection branch. The segmentation branch reuses the object features as guidance to separate target object from the neighboring ones within the same bounding box region. To further improve the segmentation quality, we design an auxiliary feature refinement module that densely samples and refines point-wise features in the boundary regions. Experimental results on three biological image datasets demonstrate the advantages of our method. The code will be available at https://github.com/yijingru/ObjGuided-Instance-Segmentation.
实例分割对于许多生物应用都非常重要，例如研究神经细胞相互作用、植物表型分析以及定量测量细胞对药物治疗的反应。在本文中，我们提出了一种新的基于框的实例分割方法。基于框的实例分割方法通过边界框捕获对象，然后在每个边界框区域内执行单独的分割。然而，由于它们的纹理相似且边界对比度低，现有方法很难在同一边界框区域内将目标与其相邻对象区分开来。为了解决这个问题，在本文中，我们提出了一种基于对象的实例分割方法。我们的方法首先检测对象的中心点，然后预测边界框参数。为了进行分割，沿着检测分支构建了一个基于对象的粗到精分割分支。分割分支重新使用对象特征作为指导，将目标对象与其在同一边界框区域内的相邻对象分开。为了进一步提高分割质量，我们设计了一个辅助特征细化模块，该模块密集地对边界区域的点特征进行采样和细化。在三个生物图像数据集上的实验结果表明了我们方法的优势。代码将在 https://github.com/yijingru/ObjGuided-Instance-Segmentation 上提供。
