Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4764-4767. doi: 10.1109/EMBC48229.2022.9871996.
Accurate segmentation of nuclei is an essential step in analysis of digital histology images for diagnostic and prognostic applications. Despite recent advances in automated frameworks for nuclei segmentation, this task is still challenging. Specifically, detecting small nuclei in large-scale histology images and delineating the border of touching nuclei accurately is a complicated task even for advanced deep neural networks. In this study, a cascaded deep learning framework is proposed to segment nuclei accurately in digitized microscopy images of histology slides. A U-Net based model with customized pixel-wised weighted loss function is adapted in the proposed framework, followed by a U-Net based model with VGG16 backbone and a soft Dice loss function. The model was pretrained on the Post-NAT-BRCA public dataset before training and independent evaluation on the MoNuSeg dataset. The cascaded model could outperform the other state-of-the-art models with an AJI of 0.72 and a F1-score of 0.83 on the MoNuSeg test set.
准确的细胞核分割是数字组织学图像分析在诊断和预后应用中的重要步骤。尽管最近在自动化核分割框架方面取得了进展，但这项任务仍然具有挑战性。具体来说，即使对于先进的深度学习网络，在大规模组织学图像中检测小细胞核并准确描绘触核边界也是一项复杂的任务。在这项研究中，提出了一种级联深度学习框架，以在组织学载玻片的数字化显微镜图像中准确分割细胞核。所提出的框架中采用了基于 U-Net 的模型，并带有定制的像素加权损失函数，随后是基于 U-Net 的具有 VGG16 骨干和软 Dice 损失函数的模型。该模型在 MoNuSeg 数据集上进行独立评估之前，在 Post-NAT-BRCA 公共数据集上进行了预训练。在 MoNuSeg 测试集上，级联模型的 AJI 为 0.72，F1 得分为 0.83，优于其他最先进的模型。
