A Classification-Guided Segmentation Algorithm Based on Deep Learning for Epithelium Segmentation in Histopathological Images of Radicular Cysts.

In histopathological analysis of radicular cysts (RCs), lesions in epithelium can provide pathologists with rich information on pathologic degree, which is helpful to determine the type of periapical lesions and make precise treatment planning. Automatic segmentation and localization of epithelium from whole slide images (WSIs) can assist pathologists to complete pathological diagnosis more quickly. However, the class imbalance problem caused by the small proportion of fragmented epithelium in RCs imposes challenge on the typical automatic one-stage segmentation method. In this paper, we proposed a classification-guided segmentation algorithm (CGSA) for accurate segmentation. Our method was a two-stage model, including a classification network for region of interest (ROI) location and a segmentation network guided by classification. The classification stage eliminated most irrelevant areas and alleviated the class imbalance problem faced by the segmentation model. The results of 5-fold cross validation demonstrated that CGSA outperformed the one-stage segmentation method which was lacking in prior epithelium localization information. The epithelium segmentation achieved an overall Dice's coefficient of 0.722, and intersection over union (IoU) of 0.593, which improved by 5.5% and 5.9% respectively compared with the one-stage segmentation method using UNet.Clinical Relevance- This work presents a framework for automatic epithelium segmentation in histopathological images of RCs. It can be applied to make up for the shortcomings of manual annotation which is labor-intensive, time-consuming and objective.

[Improvement program on pretreatment of acid decalcified tissue in hematoxylin-eosin staining].

OBJECTIVE: To explore the treatment conditions of acid decalcified specimens and improve the poor quality of sections and unclear structure of hematoxylin-eosin (HE) staining caused by the change in pH in tooth and hard tissue after acid decalcification. METHODS: A total of 20 cases of oral pathological specimens that contain hard tissues were decalcified and treated with routine treatment, concentrated ammonia water immersion treatment, and saturated lithium carbonate solution immersion treatment. The quality and HE staining effects of hard tissue sections treated with different methods were compared. RESULTS: Compared with routine treatment, lithium carbonate saturated solution treatment showed complete sections. Hematoxylin is strongly stained, the nucleus is clear, and the cytoplasm is bright. CONCLUSIONS: Soaking acid decalcified specimens in lithium carbonate saturated solution before embedding in dehydration can neutralize the acidic environment of the tissue. The quality of sections and HE staining effect are improved and are suitable for the pretreatment of acid decalcified tissue samples of oral pathology.