U-Net based mitosis detection from H&E-stained images with the semi-automatic annotation using pHH3 IHC-stained images

Jiaming Li; Toshiyuki Adachi; Saori Takeyama; Masahiro Yamaguchi; Yukako Yagi

doi:10.1117/12.2612815

4 April 2022 U-Net based mitosis detection from H&E-stained images with the semi-automatic annotation using pHH3 IHC-stained images

Jiaming Li, Toshiyuki Adachi, Saori Takeyama, Masahiro Yamaguchi, Yukako Yagi

Author Affiliations +

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120322J (2022) https://doi.org/10.1117/12.2612815
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

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.

Conference Presentation

Citation Download Citation

Jiaming Li, Toshiyuki Adachi, Saori Takeyama, Masahiro Yamaguchi, and Yukako Yagi "U-Net based mitosis detection from H&E-stained images with the semi-automatic annotation using pHH3 IHC-stained images", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120322J (4 April 2022); https://doi.org/10.1117/12.2612815

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