We propose a new U-Net-based method for mitosis detection and a semi-automatic image processing algorithm to generate datasets from the H&E- and pHH3- stained tissue images. Instead of manual annotation, which requires not only specialized knowledge but also a lot of labor and time, our dataset generation algorithm is capable of generating precisely labeled datasets that can be easily used as a data expansion for training various kinds of models. Moreover, the proposed U-Net-based mitosis detection model, called GaussUNet, can learn the features of mitotic figures from the images by using novel two-dimensional-Gaussian-distribution-based labels created from the centroid coordinates given by annotations. In addition, we tried to improve the performance of the model by adding false positives obtained from the trained model as the mitosis look-alikes (MLAs) class to the training data. In the experiments, we confirmed the high performance of the proposed method with a simple and efficient model compared to conventional methods.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.