Automatic HEp-2 cell segmentation in indirect immunofluorescence images using deep learning

Guan-Ting Jiang; Yi-Da Wu; Tsu-Yi Hsieh; Yu-Len Huang

doi:10.1117/12.2590426

20 April 2021 Automatic HEp-2 cell segmentation in indirect immunofluorescence images using deep learning

Guan-Ting Jiang, Yi-Da Wu, Tsu-Yi Hsieh, Yu-Len Huang

Proceedings Volume 11792, International Forum on Medical Imaging in Asia 2021; 1179205 (2021) https://doi.org/10.1117/12.2590426
Event: International Forum on Medical Imaging in Asia 2021, 2021, Taipei, Taiwan

Abstract

Autoimmune diseases are an abnormal immune response of human body, which could cause mild symptoms like low grade fever, or severe reactions including damage to joints and muscles or even causing cancer. The early symptoms of autoimmune diseases are generally the same as common illnesses and occasionally occurring, so the physician will apply various tests to determine the existence of autoimmune diseases. Among the commonly used test methods, antinuclear autoantibody (ANA) screening is the one most often used. However, the ANA screening interpretation is a laborintensive process, requiring hours of physician per day to read the specimens of ANA testing. Since neural networks granted new progress in 2012, deep convolutional networks in medical applications have caught physicians' interests. Current approaches of automatic process to autoimmune diseases testing are focused at single class annotation dataset, such as I3A and ICPR 2012 dataset. This study proposes a method using mask R-CNN in mixed pattern dataset to segment the cells in instance level, and classify cells into different cell cycles for further classification research. The proposed method achieves 89% segmentation accuracy and 95.07% cell cycle classification accuracy.

Citation Download Citation

Guan-Ting Jiang, Yi-Da Wu, Tsu-Yi Hsieh, and Yu-Len Huang "Automatic HEp-2 cell segmentation in indirect immunofluorescence images using deep learning", Proc. SPIE 11792, International Forum on Medical Imaging in Asia 2021, 1179205 (20 April 2021); https://doi.org/10.1117/12.2590426

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