Cell Nuclei Segmentation in Cytological Images Using Convolutional Neural Network and Seeded Watershed Algorithm.

Morphometric analysis of nuclei is crucial in cytological examinations. Unfortunately, nuclei segmentation presents many challenges because they usually create complex clusters in cytological samples. To deal with this problem, we are proposing an approach, which combines convolutional neural network and watershed transform to segment nuclei in cytological images of breast cancer.

Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images.

Prompt and widely available diagnostics of breast cancer is crucial for the prognosis of patients. One of the diagnostic methods is the analysis of cytological material from the breast. This examination requires extensive knowledge and experience of the cytologist.

Computer-Aided Breast Cancer Diagnosis Based on the Analysis of Cytological Images of Fine Needle Biopsies.

The effectiveness of the treatment of breast cancer depends on its timely detection. An early step in the diagnosis is the cytological examination of breast material obtained directly from the tumor. This work reports on advances in computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies to characterize these biopsies as either benign or malignant.