Identification and counting of cells and mitotic figures is a standard task in diagnostic histopathology. Due to the large overall cell count on histological slides and the potential sparse prevalence of some relevant cell types or mitotic figures, retrieving annotation data for sufficient statistics is a tedious task and prone to a significant error in assessment. Automatic classification and segmentation is a classic task in digital pathology, yet it is not solved to a sufficient degree. We present a novel approach for cell and mitotic figure classification, based on a deep convolutional network with an incorporated Spatial Transformer Network. The network was trained on a novel data set with ten thousand mitotic figures, about ten times more than previous data sets. The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells and granulocytes) and their position within an image. The mean accuracy of the algorithm in a five-fold cross-validation is 91.45 . In our view, the approach is a promising step into the direction of a more objective and accurate, semi-automatized mitosis counting supporting the pathologist.
Typically, the process of object detection is parted into two sub-processes: Segmentation and classification. This setup is especially sensible for histology since the images represent a large amount of data and classification is usually the more complex process compared to segmentation. Sommer used pixel-wise classification for candidate retrieval and then object shape and texture features for mitotic cell classification @cite_2 . Irshad used active contour models for candidate selection and statistical and morphological features for classification @cite_11 . Those hand-crafted features have significant drawbacks, however: Given the often small data sets, automatic selection of features is prone to random correlation, while using higher-dimensional classification approaches on the complete set increases overfitting @cite_1 . Further, it is questionable, if those approaches can represent the variability in shape and texture of mitotic figures @cite_9 .
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The number of mitoses per tissue area gives an important aggressiveness indication of the invasive breast carcinoma. However, automatic mitosis detection in histology images remains a challenging problem. Traditional methods either employ hand-crafted features to discriminate mitoses from other cells or construct a pixel-wise classifier to label every pixel in a sliding window way. While the former suffers from the large shape variation of mitoses and the existence of many mimics with similar appearance, the slow speed of the later prohibits its use in clinical practice. In order to overcome these shortcomings, we propose a fast and accurate method to detect mitosis by designing a novel deep cascaded convolutional neural network, which is composed of two components. First, by leveraging the fully convolutional neural network, we propose a coarse retrieval model to identify and locate the candidates of mitosis while preserving a high sensitivity. Based on these candidates, a fine discrimination model utilizing knowledge transferred from cross-domain is developed to further single out mitoses from hard mimics. Our approach outperformed other methods by a large margin in 2014 ICPR MITOS-ATYPIA challenge in terms of detection accuracy. When compared with the state-of-the-art methods on the 2012 ICPR MITOSIS data (a smaller and less challenging dataset), our method achieved comparable or better results with a roughly 60 times faster speed. Context: According to Nottingham grading system, mitosis count plays a critical role in cancer diagnosis and grading. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. Aims: The aim is to improve the accuracy of mitosis detection by selecting the color channels that better capture the statistical and morphological features, which classify mitosis from other objects. Materials and Methods: We propose a framework that includes comprehensive analysis of statistics and morphological features in selected channels of various color spaces that assist pathologists in mitosis detection. In candidate detection phase, we perform Laplacian of Gaussian, thresholding, morphology and active contour model on blue-ratio image to detect and segment candidates. In candidate classification phase, we extract a total of 143 features including morphological, first order and second order (texture) statistics features for each candidate in selected channels and finally classify using decision tree classifier. Results and Discussion: The proposed method has been evaluated on Mitosis Detection in Breast Cancer Histological Images (MITOS) dataset provided for an International Conference on Pattern Recognition 2012 contest and achieved 74 and 71 detection rate, 70 and 56 precision and 72 and 63 F-Measure on Aperio and Hamamatsu images, respectively. Conclusions and Future Work: The proposed multi-channel features computation scheme uses fixed image scale and extracts nuclei features in selected channels of various color spaces. This simple but robust model has proven to be highly efficient in capturing multi-channels statistical features for mitosis detection, during the MITOS international benchmark. Indeed, the mitosis detection of critical importance in cancer diagnosis is a very challenging visual task. In future work, we plan to use color deconvolution as preprocessing and Hough transform or local extrema based candidate detection in order to reduce the number of candidates in mitosis and non-mitosis classes. 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 highresolution 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.