Accurately and quickly binuclear cell (BC) detection plays a significant role in predicting the risk of leukemia and other malignant tumors. However, manual microscopy counting is time-consuming and lacks objectivity. Moreover, with the limitation of staining quality and diversity of morphology features in BC microscopy whole slide images (WSIs), traditional image processing approaches are helpless. To overcome this challenge, we propose a two-stage detection method inspired by the structure prior of BC based on deep learning, which cascades to implement BCs coarse detection at the WSI-level and fine-grained classification in patch-level. The coarse detection network is a multi-task detection framework based on circular bounding boxes for cells detection, and central key points for nucleus detection. The circle representation reduces the degrees of freedom, mitigates the effect of surrounding impurities compared to usual rectangular boxes and can be rotation invariant in WSI. Detecting key points in the nucleus can assist network perception and be used for unsupervised color layer segmentation in later fine-grained classification. The fine classification network consists of a background region suppression module based on color layer mask supervision and a key region selection module based on a transformer due to its global modeling capability. Additionally, an unsupervised and unpaired cytoplasm generator network is firstly proposed to expand the long-tailed distribution dataset. Finally, experiments are performed on BC multicenter datasets. The proposed BC fine detection method outperforms other benchmarks in almost all the evaluation criteria, providing clarification and support for tasks such as cancer screenings.
翻译:精确和快速的双核细胞(BC)检测在预测白血病和其他恶性肿瘤的风险方面起着重要作用。然而,人工显微镜计数耗时且缺乏客观性。此外,由于不列颠哥伦比亚显微显微镜整个幻灯片图像中形态特征的污点和多样性的限制,传统的图像处理方法是无助的。为了克服这一挑战,我们提议了一种两阶段检测方法,由不列颠哥伦比亚之前的基于深层学习的结构所启发,该方法在补丁等级的WSI水平和细度分类中用于实施BC粗略的检测基准。近似显微镜检测网络是一个多任务检测框架,基于细胞检测的圆形框和核心检测的中央关键点。圆形代表降低了自由度,减轻了周围杂乱效应的影响,并且可以在 WSI 中旋转。 检测核心结构中的拟议关键点可以帮助对网络进行感知,并在后来的精细度分类中用于不超超色层分析。 精确的检测网络检测网络是一个基于圆形的精确的分类,一个精细的分类系统化的分类系统检测网络,其精细化的分类系统化的分类系统化的分类系统化模型是用来分析模型,一个基于核心背景分析模型的模细化的模化的模异形结构结构结构结构结构结构结构的模型的模型,一个精化的模型的模型,一个精细化的模型的模型的模型,其精细化系统,其精细化的模型的系统,其结构结构结构结构的模型,其精细化系统化系统化的模型,其结构结构结构结构结构结构结构结构化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化的模型,其结构化模型化系统化的模型化系统化模型化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化系统化模型化模型。