Ensemble Segmentation of Nucleus Regions from Histopathological Images towards Breast Abnormality Detection

One of the most occurred cancers which cause death in women is breast cancer, contributing to 16% of all female cancers worldwide. Detection of the disease in the preliminary stage is the only way to treat the disease from any severity. Presence of the digital imaging modalities also allows the computerized diagnosis of a disease which overcomes the limitations of a human perception system. However, a pathologist who is knowledgeable and skilled is necessary for an appropriate diagnosis. Also, tissue sample analysis requires a lot of manual labor. Therefore, combining digital histopathology with computer-aided diagnostic (CAD) tools can assist in solving these issues. In this paper, we have proposed a hybrid framework of nucleus region segmentation from the histopathological images. The primary aim of the proposed framework is to ensemble information from multiple segmentations and, finally, fuse this information (in terms of intersection) to acquire the core and stable nucleus region(s). For this, we have ensemble the U-net model (with VGG-16 as the backbone network) with the fuzzy c-means algorithm for precise nucleus regions segmentation from the histopathological images. Experimental results reveal that the proposed framework performed better for cell nucleus segmentation with dice similarity index values of 0.8517 and 0.9357 using publicly available BreakHis and BreCaHAD, respectively.