Learning-based mitotic cell detection in histopathological images

Breast cancer grading of histological tissue samples by visual inspection is the standard clinical practice for the diagnosis and prognosis of cancer development. An important parameter for tumor prognosis is the number of mitotic cells present in histologically stained breast cancer tissue sections. We propose a hierarchical learning workflow for automated mitosis detection in breast cancer. From an initial training set a pixel-wise classifier is learned to segment candidate cells, which are then classified into mitotic and non-mitotic cells using object shape and texture features. Our workflow banks on two open source biomedical image analysis software: “ilastik” and “CellCognition” which provide a user user friendly interface to powerful learning algorithms, with the potential of making the pathologist work an easier task. We evaluate our approach on a dataset of 35 high-resolution histopathological images from 5 different specimen (provided by International Conference for Pattern Recognition 2012 contest on Mitosis Detection in Breast Cancer Histological Images). Based on the candidate segmentation our approach achieves an area-under Precision-Recall-curve of 70% on an annotated dataset, with good localization accuracy, little parameter tuning and small user effort. Source code is provided.