Abstract
Accurate segmenting nuclei instances is a crucial step in computer-aided image analysis to extract rich features for cellular estimation and following diagnosis as well as treatment. While it still remains challenging because the wide existence of nuclei clusters, along with the large morphological variances among different organs make nuclei instance segmentation susceptible to over-/under-segmentation. Additionally, the inevitably subjective annotating and mislabeling prevent the network learning from reliable samples and eventually reduce the generalization capability for robustly segmenting unseen organ nuclei. To address these issues, we propose a novel deep neural network, namely Contour-aware Informative Aggregation Network (CIA-Net) with multi-level information aggregation module between two task-specific decoders. Rather than independent decoders, it leverages the merit of spatial and texture dependencies between nuclei and contour by bi-directionally aggregating task-specific features. Furthermore, we proposed a novel smooth truncated loss that modulates losses to reduce the perturbation from outliers. Consequently, the network can focus on learning from reliable and informative samples, which inherently improves the generalization capability. Experiments on the 2018 MICCAI challenge of Multi-Organ-Nuclei-Segmentation validated the effectiveness of our proposed method, surpassing all the other 35 competitive teams by a significant margin.
Abstract (translated)
精确分割核实例是计算机辅助图像分析中的一个重要步骤，它可以提取丰富的特征用于细胞估计、诊断和治疗。然而，由于细胞核簇的广泛存在，以及不同器官之间的巨大形态差异，使得细胞核实例分割容易受到过度/欠分割的影响，因此仍然具有挑战性。此外，不可避免的主观标注和错误标注阻碍了对可靠样本的网络学习，最终降低了对未知器官核进行大规模分割的泛化能力。为了解决这些问题，我们提出了一种新的深度神经网络，即轮廓感知信息聚合网络（CIA网络），它在两个特定任务的解码器之间具有多级信息聚合模块。它不是独立的译码器，而是通过双向聚合特定于任务的特征来利用原子核和轮廓之间的空间和纹理依赖性的优点。此外，我们还提出了一种新的平滑截断损耗，它可以调节损耗以减少异常值的扰动。因此，网络可以集中于从可靠的、信息丰富的样本中学习，从而提高泛化能力。2018年多器官细胞核分割MICCAI挑战实验验证了该方法的有效性，大大超过了其他35个竞争团队。
URL
https://arxiv.org/abs/1903.05358
