NuSeT: A deep learning tool for reliably separating and analyzing crowded cells
Fig 5
NuSeT can be applied to segment histopathology samples and detect mitotic events.
(A, B) Representative example of liver fat globule segmentation using NuSeT. Notice that NuSeT performs well on both macroglobule (A) and microglobule (B) segmentation. (C-E): Representative example of segmentation and detection of mitosis in breast cancer samples from ICPR 2012. (C) Weight map used for training the mitosis model. An ‘attention’ strategy has been used to focus more on the mitosis events and the environment surrounding them. The shaded region denotes the label for the mitotic event, and colors denote the weights applied during training process. (D) Representative example of mitosis detection and segmentation results with breast cancer sample. Scores on the top-left corner of the bounding boxes denote the possibility of a mitosis event evaluated from the model. Zoom-in of some detected mitotic events are shown in (E). (F) Representative example of mitosis detection and segmentation results with fluorescent nuclei in a time lapse movie of MCF10A epithelial cells stably expressing histone H2B-eGFP. NuSeT can detect mitotic progression from prophase to telophase (mitotic events were identified by NuSeT and then manually classified into different phases).