NuSeT: A deep learning tool for reliably separating and analyzing crowded cells
Fig 4
NuSeT effectively segments single nuclei in disorganizing dense mammary acini.
(A) Representative 3D MCF10AT acinus segmentation using NuSeT. (B) Nuclei tracking. For ease of visualization, only a few of the segmented nuclei are shown at different time points. (C) 3D tracks of the nuclei shown in (B) over time, from 0 h (dark) to 4.5 h (light). (D) Number of nuclei detected in disorganizing acini at different time points using different segmentation methods. Data were collected from 8 representative acini and were normalized by the total number of nuclei at the last time point. Data from the first 5 hours are shown. (E) Cumulative distribution function plots of area of nuclei segmented using different methods. (F) Box plots of nuclear area distribution. The median area for each method is indicated on the top. The area box plot for Otsu’s method (median area: 2816.6 ± 2845 μm2) is shown in S6 Fig. (G) Representative examples comparing nuclei segmentation in dense mammary acini using different methods. Scale bars are 20 μm.