Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation
This work addresses a domain-specific problem for pathologists and researchers in breast cancer diagnosis by offering a potentially faster and more efficient method for nuclear size analysis, though it is incremental as it builds on existing machine learning techniques.
The paper tackles the problem of measuring nuclear area in histopathology slides by proposing a deep learning model that directly computes nuclear area and mean nuclear area from images without segmentation, achieving reliable measurements comparable to traditional segmentation-based methods.
The size of nuclei in histological preparations from excised breast tumors is predictive of patient outcome (large nuclei indicate poor outcome). Pathologists take into account nuclear size when performing breast cancer grading. In addition, the mean nuclear area (MNA) has been shown to have independent prognostic value. The straightforward approach to measuring nuclear size is by performing nuclei segmentation. We hypothesize that given an image of a tumor region with known nuclei locations, the area of the individual nuclei and region statistics such as the MNA can be reliably computed directly from the image data by employing a machine learning model, without the intermediate step of nuclei segmentation. Towards this goal, we train a deep convolutional neural network model that is applied locally at each nucleus location, and can reliably measure the area of the individual nuclei and the MNA. Furthermore, we show how such an approach can be extended to perform combined nuclei detection and measurement, which is reminiscent of granulometry.