A 2.5D Cascaded Convolutional Neural Network with Temporal Information for Automatic Mitotic Cell Detection in 4D Microscopic Images
This work addresses mitotic cell detection for mammalian skin research using intravital imaging, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled the problem of automatic mitotic cell detection in 4D microscopic images, which is challenging due to complex backgrounds and false positives, and proposed a 2.5D cascaded CNN (CasDetNet) that achieved higher precision and recall compared to state-of-the-art methods.
In recent years, intravital skin imaging has been increasingly used in mammalian skin research to investigate cell behaviors. A fundamental step of the investigation is mitotic cell (cell division) detection. Because of the complex backgrounds (normal cells), the majority of the existing methods cause several false positives. In this paper, we proposed a 2.5D cascaded end-to-end convolutional neural network (CasDetNet) with temporal information to accurately detect automatic mitotic cell in 4D microscopic images with few training data. The CasDetNet consists of two 2.5D networks. The first one is used for detecting candidate cells with only volume information and the second one, containing temporal information, for reducing false positive and adding mitotic cells that were missed in the first step. The experimental results show that our CasDetNet can achieve higher precision and recall compared to other state-of-the-art methods.