Considering the increased workload in pathology laboratories today, automated tools such as artificial intelligence models can help pathologists with their tasks and ease the workload. In this paper, we are proposing a segmentation model (DRU-Net) that can provide a delineation of human non-small cell lung carcinomas and an augmentation method that can improve classification results. The proposed model is a fused combination of truncated pre-trained DenseNet201 and ResNet101V2 as a patch-wise classifier followed by a lightweight U-Net as a refinement model. We have used two datasets (Norwegian Lung Cancer Biobank and Haukeland University Hospital lung cancer cohort) to create our proposed model. The DRU-Net model achieves an average of 0.91 Dice similarity coefficient. The proposed spatial augmentation method (multi-lens distortion) improved the network performance by 3%. Our findings show that choosing image patches that specifically include regions of interest leads to better results for the patch-wise classifier compared to other sampling methods. The qualitative analysis showed that the DRU-Net model is generally successful in detecting the tumor. On the test set, some of the cases showed areas of false positive and false negative segmentation in the periphery, particularly in tumors with inflammatory and reactive changes.

翻译：鉴于当前病理实验室工作负荷日益增加，人工智能模型等自动化工具可辅助病理学家完成任务并减轻其负担。本文提出一种能够勾画人类非小细胞肺癌区域的分割模型（DRU-Net），以及一种能提升分类效果的增强方法。该模型采用截断的预训练DenseNet201与ResNet101V2作为块状分类器进行融合，后接轻量化U-Net作为细化模型。我们使用两个数据集（挪威肺癌生物样本库与霍克兰大学医院肺癌队列）构建所提出的模型。DRU-Net模型的平均Dice相似系数达到0.91。提出的空间增强方法（多镜头畸变）将网络性能提升3%。研究发现，相较于其他采样方法，选择专门包含感兴趣区域的图像块能为块状分类器带来更优结果。定性分析表明DRU-Net模型在肿瘤检测方面总体表现成功。在测试集中，部分病例在肿瘤边缘区域出现假阳性与假阴性分割，尤其在伴有炎症及反应性改变的肿瘤中较为明显。