Despite recent advances in data-independent and deep-learning algorithms, unstained live adherent cell instance segmentation remains a long-standing challenge in cell image processing. Adherent cells' inherent visual characteristics, such as low contrast structures, fading edges, and irregular morphology, have made it difficult to distinguish from one another, even by human experts, let alone computational methods. In this study, we developed a novel deep-learning algorithm called dual-view selective instance segmentation network (DVSISN) for segmenting unstained adherent cells in differential interference contrast (DIC) images. First, we used a dual-view segmentation (DVS) method with pairs of original and rotated images to predict the bounding box and its corresponding mask for each cell instance. Second, we used a mask selection (MS) method to filter the cell instances predicted by the DVS to keep masks closest to the ground truth only. The developed algorithm was trained and validated on our dataset containing 520 images and 12198 cells. Experimental results demonstrate that our algorithm achieves an AP_segm of 0.555, which remarkably overtakes a benchmark by a margin of 23.6%. This study's success opens up a new possibility of using rotated images as input for better prediction in cell images.

翻译：尽管数据独立和深度学习算法近期取得了进展，未染色活体贴壁细胞的实例分割仍是细胞图像处理领域长期存在的挑战。贴壁细胞固有的视觉特征，如低对比度结构、模糊边缘及不规则形态，使其即使对于人类专家也难以相互区分，更遑论计算方法。本研究提出一种新型深度学习算法——双视角选择性实例分割网络（DVSISN），用于差分干涉对比（DIC）图像中未染色贴壁细胞的分割。首先，我们采用双视角分割（DVS）方法，利用原始图像与旋转图像对预测每个细胞实例的边界框及其对应掩膜。其次，使用掩膜选择（MS）方法过滤DVS预测的细胞实例，仅保留最接近真实掩膜的实例。该算法在包含520张图像及12198个细胞的数据集上完成训练与验证。实验结果表明，本算法的AP_segm达到0.555，显著超过基准方法23.6%。本研究的成功为使用旋转图像作为输入以改善细胞图像预测开辟了新可能。