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Microscopy Image Browser: A Platform for Segmentation and Analysis of Multidimensional Datasets

Fig 3

Semiautomatic image segmentation in MIB can dramatically decrease the time spent on modelling.

(A) The Random Forest classifier was used to segment ER from wide-field time-lapse LM videos of Huh-7 cells. Labels were assigned (central image) to mark ER (Hsp47-GFP marker seen in green) and background (red), which were then used to train the classifier and segment ER throughout the time-lapse video (right image). (B) The semiautomatic watershed segmentation was used for segmentation of nucleus in the sieve element of A. thaliana root imaged with SB-EM [14]. Assigning of just two labels (green for nucleus and vermilion for background) was sufficient to segment the complete nucleus in 3-D (light blue, image on the right). (C) The separation of the fused objects using the watershed segmentation. The human U251MG astrocytoma cells were loaded with oleic acid producing a large amount of lipid droplets (LDs) (left image) that tend to form clusters. LDs were segmented using marker-controlled watershed; however, because of close proximity, most of the LDs appear merged (the second image). The object separation mode of the watershed tool was used to separate individual LDs for quantitative analysis (third and fourth images). The 3-D models were rendered with 3D Slicer [8]. Scale bars: 2 μm.

Fig 3